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Memory to the moon

First it was power and chips, and now memory has entered the bottleneck chat:

Prices for both DDR5 and DDR4 memory chips have skyrocketed, since the end of last year, not coincidentally with a ~25% rise in PPI for the “computers” category.

The bidding war isn’t limited to DRAM (especially High Bandwidth Memory), which is particularly important to model training, but traditional steady-state NAND, as well:

As of March, monthly contract prices for DRAM had more than tripled yoy, while NAND prices grew by a more modest ~2X.

Truthfully, there is no real hierarchy in the list of AI-infrastructure related shortages, from power, and skilled labor, to chips (GPUs and CPUs) and memory. An historic surge in demand, backstopped by ~$1T in capex, will have that sort of effect, but memory is the most prominent recent addition to the list.

The run on memory has led to a pretty epic quarter for the leading memory-makers (Samsung, SK Hynix and Micron):

Memory-makers are expected to sextuple(!) their operating income in 2026. Micron itself earned more in ‘Q1 than it had in any single year before 2025.

Net-income gains for memory are so massive (and off a relatively lower base), they’ve broken the y-axis for the AI supply chain winners:

So, memory is the new belle of the ball. But, the funny thing about memory is that it used to be one of the most commoditized parts of the chip-stack. Despite being fairly ubiquitous in phones, PCs, and gaming, memory never really enjoyed any pricing power (and the success of those memory chipmakers was very much tied to the consumer goods cycle). Memory competed on price, and the name of the game was “how low can you go?”

But the sheer volume of demand, and the increasing complexity and power requirements of AI-related memory needs (where efficiency is at a premium) has changed everything.

It’s turned even previously sleepy low-cost memory-makers, like Western Digital, from horizontal lines into vertical ones:

Western Digital has done great. But if we put its DRAM specialist spinout (Sandisk) on the chart, it makes OldCo WDC performance look more like a modest hill than a legendary spike.

But, will this run on memory—and the extraordinary pricing power the memory chipmakers now enjoy—ever end? If demand is so high, then why not make more memory chips?

Well, it can take years to bring new capacity online, and in the meantime, manufacturers are prioritizing higher-margin, and more silicon-intensive HBM, which puts even more pressure on the supply of consumer memory chips. Plus, hyperscalers are now signing 5-year agreements—the industry standard used to be 1-year—to ensure their place at the top of the supply queue. As a result, even the more commoditized consumer chips are getting much more expensive, and those costs will flow through to either iPhone, etc.’s margins and/or consumer prices—expectations are that prices on PCs and phones will increase ~10-20%.

At the same time, everyone knows the saying “every shortage becomes a glut,” and no one knows that better than cyclical manufacturing businesses.

They are traditionally very hesitant to invest big bucks in new capacity to meet even substantial surges in demand, knowing that once the surge fades, they still have to foot the bill for all that new capacity. Plus, necessity is the mother of invention, and when costs rise, so too does the innovation-drive for efficiency (which we’ve already seen, e.g. with Google’s TPUs and Amazon’s Trainium, as part of their effort to be somewhat less dependent on NVIDIA’s GPUs, and more recently, with the release of ‘TurboQuant’).

On the other hand, the upward inflection in computational demand may just be getting started in the new-AI-normal, and that would make memory a lot less cyclical than ever before. So, is it a step-change or a one-time thing? That’s a multi-hundred billion question right now.

Affirm’s AI Turn

Rather than replacing workers, there’s every reason to think that AI will function as a force-multiplier, making at least some workers more valuable than before. SWEs are a perfect example of an “AI Augmented” role, and it’s not surprising that demand for SWEs appears to have increased.

It flew under the radar for whatever reason, but the publicly-traded BNPL company Affirm recently gave some very concrete evidence of the AI-driven productivity lift, and the positive impact on Affirm’s hiring intentions:

Earlier this quarter we took a full week as a development team to re-tool Affirm software engineering to be truly AI-first. You can see the difference below, with agentically-written code dominating non-agentic essentially overnight after this effort. The chart shows pull requests per week (each pull request is a product feature, a major bug fix, etc) as a function of time.

We think an order of magnitude gain in software development productivity is entirely plausible, yet we expect to modestly grow our development team – the limiting factor for Affirm has always been engineering cycles, not ideas for what to do with them.

When Affirm “re-tool[ed] software engineering to be truly AI-first,” PRs more than doubled, and roughly two-thirds of the output was “agentic.” As a result, Affirm plans to “modestly grow our development team.” More productivity, more hiring. Checks out.

But the real money quote came at the end: “the limiting factor for Affirm has always been engineering cycles, not ideas for what to do with them.” In other words, we are nowhere near peak-demand for software, and rather than put SWEs out of work, lowering the cost of development is expanding the frontier of what gets built.

Affirm is, of course, just one company, but there is mounting evidence of productivity lift across “AI Industries,” as a whole:

Output/employee inflected upwards in 2025, but especially amongst the “High AI Industries.” And within those industries, it’s tech-related industries doing most of the work.

Now, productivity is notoriously hard to measure in the aggregate, and a lot of this additional output simply reflects the surge in demand (and pricing) for AI-infrastructure. Higher prices for the same thing leads to more measured “output,” without any increase in output. But, at the same time, it also means that we’re very early in this transition, and even industries that stand a lot to gain from automation, have barely begun to scratch the surface of what they’re eventually going to do.

Agents, especially, are extremely early in their lifecycle, and some of the biggest AI adopters, are still barely entering their agent-phase, at least according to some data published by Microsoft:

Nearly 1 in 5 firms are using agents of some kind, but that also means that only 1 in 5 firms are using agents of some kind. Manufacturing is a surprise outlier for agent-deployment. While manufacturing firms comprise less than 10% of the firms using agents (and relatively few “prompts per user”), they account for ~18% of the agents, higher than any other sector.

The future isn’t even here yet, it’s definitely not evenly distributed, and we expect a lot more to come.

Shrinking SmallCaps

While many people are probably aware that there are fewer public companies now, it was somewhat surprising to learn that the shift is driven entirely by the disappearance of small public companies:

The number of large- and mid-sized public companies has stayed relatively constant since 2000 (but really, since 1990). It’s the number of microcaps (and to a lesser extent smallcaps) that has really gone from boom-to-bust.

The SEC attributed the change to three factors: (1) lots of M&A; (2) smallco delistings; and (3) fewer IPOs. Other factors include regulatory costs, which generally don’t scale with market cap (i.e. regulatory costs are a larger share of market for smaller companies), and of course the growth of private equity, which has made it easier for companies to raise capital without recourse to the public markets, at all.

These are very rough estimates, of course, but research has repeatedly shown that the fixed compliance costs of being public are proportionately higher for smaller companies. In that sense, compliance is a regressive tax.

There are other structural reasons for smaller companies avoiding the public markets, as well: small caps don’t benefit from passive flows, less liquidity makes it harder for public market investors to maneuver without big impacts on price, and smaller float makes smallcaps less interesting to sell-side coverage (which leads to less liquidity).

The net result of all of this is that smallcos have retreated from the public markets (not entirely of course), but they haven’t disappeared: it’s just that the action has moved to the private side. Of course, the decision to go public or not is ultimately specific to each company, but the broader trend is real, and it’s a function of structural changes that have made public markets less-appealing, relative to their private alternatives. If we want more companies to go public, then perhaps public markets ought to up their game.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Yesterday we heard from Seema Amble with Is Software Losing Its Head?, today we continue on the same topic with this piece from Gio, Steph and Alex. Enjoy.- AD

Here’s one way you can think about system of record stickiness:

For a long time, the valuable part of social media businesses was the friend graph. When you opened Facebook back in the day, the thing you interacted with was people’s profiles, and the data graph across the profiles was a powerful, durable asset. It was hard to foresee what could disrupt such an obvious network effect.

Then the news feed came along. The news feed gave us a new place to go: “Here’s what happened today; here’s where you catch up and take action, all in one place.” This started out as a complementary layer to the friend graph, but in time, the graph became “just one of many inputs” to the feed serving you relevant content. While it never went away, it’s no longer the important layer - the feed algorithm is, and all kinds of things feed into it. Your social profile, posts and likes are primarily consumed “at the internal API layer”, so to speak; the newsfeed is its consumer.

We think this is starting to happen to one of the supposedly “least disruptable” parts of the enterprise: the CRM. The CRM isn’t going to go away, just like the friend graph never went away—but it’s turning into just an input; one of many inputs, into the systems of intelligence which we use to get work done.

At firms across the country, the typical account executive now opens his laptop in the morning and finds, waiting for him, a small collection of software agents he had no part in programming — a research agent that combs 10-Ks and recent earnings calls before his first meeting of the day; a dialer that coaches him on objections in the moment; an orchestration layer that listens to his calls and writes structured notes back into the CRM without his lifting a finger. None of this, by itself, is earthshaking. But taken together, you recognize what this is: this is the newsfeed. It’s the valuable thing now.

There’s no doubt: owning the system of record has been the winning play for go-to-market software for twenty years. It’s sticky, valuable, and hard to leave. And we can’t imagine the SoR incumbents going away anytime soon: Salesforce and HubSpot still sit on some of the most valuable datasets in the industry, they’ve realized that it matters, and they’re quickly coming up with API-first offerings that bring AI features in their own walls.

But we think we’ve seen this movie before. In the next decade, you want to own the system of intelligence that pulls from the system of record, becomes the user’s one-stop shop for gaining context and taking action, and turns the SoR into something that’s primarily consumed at the API layer. The reasoning layer that sits above the database, and that increasingly treats the database as infrastructure, is where a new generation of companies is being built, and it’s where the majority of the next decade’s enterprise value of GTM software will end up.

Why the database won

Over the last thirty years, software companies have produced an unbelievable number of products to help companies manage themselves. A thousand companies were founded to help salespeople sell; but almost all the value ended up accumulating in just two names: Salesforce, today valued at around $140 billion, and HubSpot, valued around $9 billion. As the line goes, “First prize is a Cadillac. Second prize is a set of steak knives.”

The reason, everyone in the industry has long understood, is simple: Salesforce and HubSpot own the database. And the database is where all the value resides. Every call note, every pricing precedent, every contact, every stray observation about why a deal had stalled is entered into the system, and the cost of leaving it behind becomes enormous. Once that database has accumulated a few years of operational context, switching costs become, as our colleague Alex Rampell has put it, high enough that users are “hostages, not customers.” Every app in the Salesforce AppExchange and every tool in the HubSpot Marketplace is, in effect, paying rent for the right to plug into someone else’s database.

Then, Salesforce and HubSpot do what every dominant platform owner in every era does: they expand outward. They add features like marketing, service, analytics, and commerce: each new module built on the same data spine, and each further raising the cost of any decision to leave.

One of the more counterintuitive findings from our GTM survey is that CRM usage has actually risen since AI tools began to be adopted at scale. The agents that listen to calls and write structured notes back into the system are, for the moment, giving reps fresh reason to consult it, because the data sitting there has become dramatically richer than it used to be.

Orchestration is the new gravity well

AI agents, acting on behalf of sales reps and alongside them, are taking over a steadily widening share of the GTM workflow. Sometimes the rep instructs the agent directly: research this account, draft this outbound sequence, qualify these inbound leads, update this deal record after the call. Sometimes the agent works in the background, listening to a meeting recording and writing the structured fields back into the CRM on its own.

And the agent doesn’t need a drag-and-drop pipeline view. What it needs is structured data it can read and write with low friction. The CRM, from the agent’s perspective, is a database. A very large and carefully curated database, hosted by a trusted vendor, with excellent integrations and a decade of accumulated customer trust; but a database, nonetheless. The opinionated workflows on top become, progressively, legacy furniture - a bit like the lovingly created UI of your Facebook profile; once paramount, now an afterthought.

In the software era, the gravity in enterprise software came from data accumulation: that is, from the fact that every valuable piece of sales context had to live in one place because the human operating on that context could only look in one place at a time. But in the AI era, gravity will come from orchestration. An AI agent doesn’t find it difficult to pull dozens of signals simultaneously from the CRM, the calendar, the shared inbox, the call recording, Slack, the enrichment API, the billing system, and the product telemetry. Nor does it find it difficult to synthesize information across all of them before actually taking any actions.

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Switching costs shift accordingly. “All of our customer data is in Salesforce” becomes “all of our workflows, our reasoning, our accumulated institutional context live in our AI layer.” The CRM used to tax every app that wanted access to its data; now the system of intelligence has become the hub, and the CRM is one of the many systems of record that it orchestrates across.

At the technical core of the new stack sit the foundation models. But a foundation model is not, by itself, a GTM application, any more than Oracle’s database engine was a CRM. Between the model and the customer sits an enormous amount of unglamorous and domain-specific work: orchestrating context across dozens of connected systems, encoding the actual logic of how sales and marketing teams operate, handling permissions and compliance, integrating with the chaotic reality of a Fortune 500 IT environment. That work is the new GTM application layer. It is where the new GTM companies are being built.

Go-to-market has, for decades, been a category in which software was the junior partner to labor. Historically, software made up between 5 and 10 percent of total GTM spending in a typical enterprise; the rest is payroll. Salesforce dominates the software slice, but the software slice has always been a thin wedge of the pie. What AI opens up, for the first time, is the prospect that software companies can meaningfully reduce costs while opening up new high ROI use cases.

The natural question is whether this comes at the expense of sales headcount. So far, it has not, or at least not in a straightforward way. While roles within the GTM team may shift, we’re seeing teams spend even more on people. The ROIs on these agents are strong enough that the total pie grows rather than the labor budget shrinking. Reps using these tools are hitting attainment and quota at noticeably higher rates than those without them; the return on every GTM dollar is rising, rather than merely holding steady.

The next wave

There are two observations worth making about the AI-native GTM startups that have emerged over the last few years. The first is that they are clustering, for now, around a few relatively narrow and high-frequency workflows: in all of these workflows, inputs are structured and outputs are measurable.

And while some of them are doing an existing job in a new way, many of them are inventing new jobs entirely: they are doing things that nobody was quite doing before.

Consider, for a moment, the position of a VP of Sales at a typical enterprise software firm a few years hence. She no longer begins her day by opening Salesforce to a static account list and deciding where to focus. She begins it in a prioritized feed generated by her system of intelligence: which of her accounts had material news overnight, which prospects in the territory are suddenly in market, which deals in the pipeline have gone quiet in ways that ought to be investigated. The daily prioritization decision — which used to consume real cognitive effort from every rep and every sales leader in America — has been quietly offloaded to the intelligence layer. Her reps spend more of their time actually selling.

And, when they sell, they arrive better prepared. Prep that used to happen case by case, if it happened at all, now happens every time as a matter of course. The rep who would never have read the 10-K is walking in with a briefing drafted for him; the new hire six weeks into the job is, by certain measures, better equipped than the ten-year veteran at the desk next to her.

More importantly, the VP of Sales has an honest picture of what her team is doing. At the moment that picture is whatever gets logged into the CRM, which is often incomplete and occasionally fictional. With call transcripts, email threads, and calendar data flowing in automatically, analyzed continuously, she can see, at any given moment, who is running disciplined discovery and who is skipping steps, which accounts are getting coverage and which have been quietly neglected. A system of intelligence that has ingested every interaction across a sales team can surface patterns no human manager, however committed, could see unaided.

The longer-run implications push further still, and begin to open up categories of job that did not really exist before. Every company bleeds institutional knowledge when its reps turn over — context on accounts, the history of what worked for whom, the texture of relationships built up over years. A system of intelligence that has been quietly ingesting that context for the duration of a rep’s tenure can, when she leaves, hand the whole of it over to her successor. Institutional memory becomes something a company can actually ship.

None of this, it should be said, is bad news for the CRM. Salesforce still owns its database; HubSpot still owns its database; the customer data continues to live where it has always lived, for the reasons it has always lived there. But the locus of value is migrating upward, into the layer that reads and writes to the database and does the actual thinking. The pie is getting larger in the process, not smaller. Just as the feed increased the TAM of social media to “everything of interest”, the agent revolution expands what software can plausibly charge for, and does it without gutting the labor budget that funds most GTM work today.

A new generation of companies is being built on top of this emerging layer. The next decade of go-to-market software will be written there.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

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A system of record (SOR) keeps track of the atomic units of a business — in banking, the holy grail SOR is the core banking system. It stores every account, every transaction, and is the single source of truth that every downstream workflow depends on.

In the United States, FIS and Fiserv became two of the most durable, defensible software businesses ever built because they sit at the center of every bank’s operational universe and banks can’t replace them (their data is trapped!).

The result of this incumbency is that financial institutions are sitting on decades of infrastructure debt. This debt has become the single biggest obstacle to AI adoption. Intelligent agents cannot run on systems without clean APIs and data.

Enter Stitch.

Stitch is building an operating system for modern financial institutions, starting in the Middle East. Its API-first platform enables banks, fintechs, and non-financial institutions to launch, scale, and operate financial products across a modular stack, including a general ledger and core database, product-specific software for cards, loans, and bank accounts, and a customizable workflow builder with a marketplace of integrations including KYC/KYB.

Convincing banks to swap their core is no small task — but a rare greenfield opportunity exists in the Middle East, where a nationwide focus on expanding outside of oil is feeding one of the fastest-growing financial services ecosystems in the world; the industry is growing 10% YoY. Saudi Arabia has plans to add hundreds of new financial institutions over the next five years, all of which need core banking software for the first time.

In the last six months alone, more than $5 billion has been transacted on the Stitch platform. Customer numbers grew 10x in 2025, revenue grew 20x, and Stitch has seen pull beyond the GCC into Africa and Southeast Asia. Marquee regional customers like Lulu Exchange and Raya Financing have already launched products up to 80% faster, thanks to Stitch’s flexible infrastructure.

As Stitch becomes the infrastructure layer, it can support these financial institutions in their AI transformation, adding AI-powered customer support, automating loan origination, and eliminating the messy inbox workflows that consume operational headcount at every financial institution.

Mohamed, Stitch’s founder, has the rare combination of relationships, sales instinct, and grit to win in a market where trust and distribution matter as much as product. And he’s hired a team of world-class operators from the likes of FIS and Barclays to bring his vision to life.

We are thrilled to lead Stitch’s Series A — and honored to make this our first investment in Saudi Arabia. After years building conviction around the global fintech infrastructure opportunity, we believe Stitch represents the clearest expression we’ve found of what the next generation of this infrastructure looks like. It is built natively in a market with massive tailwinds, led by a founder who understands both the technology and the regulatory landscape, and designed from the ground up to be the system of record that makes everything else (i.e. AI) possible.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

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Is software losing its head?

Last month Salesforce announced it would open its APIs and launch a headless product, essentially betting that in an agentic world, its value lies in the data layer, not the UI. It’s a smart repositioning. (Although it’s worth noting that not much appears to have changed technically: the APIs Salesforce is now marketing as a “headless product” have largely existed for years. In other words it was a classic Salesforce marketing launch.) The idea behind the new product is that agents can access the data from the system of record without needing to interact with the UI, which is designed for humans to track workflows.

The announcement is a useful prompt for a more interesting question: if you strip away the UI and expose the database, what are you actually left with? How is that different from a Postgres database, a well-designed schema, and an API? Do the classic factors that make systems of record durable persist, or is there a new set of criteria? In the SaaS era, the system of record was defensible because humans lived in the interface. In the agentic era, that advantage weakens. The defensible layers shift downward into data models, permissions, workflow logic, and compliance, and upward into networks, proprietary data generation, and real-world execution.

When software goes headless, where does defensibility move?

The UI was the product

A system of record is the authoritative source of truth for a given domain of business data. It’s the place where the official version of a customer relationship, an employee record, or a financial transaction lives, the system other tools read from and write back to. A CRM is the system of record for revenue. An HRIS is the system of record for people. An ERP is the system of record for money. What makes them powerful isn’t just that they store data, it’s that they become the shared reality an entire organization operates from.

For the last two decades, Salesforce sold a way for sales leaders to run their teams. The dashboards, pipeline views, forecasting tools, and activity feeds were what people were buying. Their business model was predicated on selling seats to users that provided access to these features. The database underneath was critical, but incidental.

Which means the UI drove stickiness. It enforced data hygiene. It created shared vocabulary: Leads, Opportunities, Accounts. It made thousands of sales reps enter data they otherwise wouldn’t have. The UI has been the mechanism that kept the data coherent. The product is so sticky that many sales leaders insist on bringing Salesforce with them to new jobs not because the UI is good, but because it is muscle-memory.

Agents are beginning to upend this model. Instead of interacting through the UI, they can read and write directly to the underlying data, which has prompted a wave of new tools and workarounds that bypass the interface entirely (Salesforce isn’t the only example here: we recently wrote about how SAP is seeing an entire AI-friendly ecosystem proliferate around it). Computer using agents also make the traditional human level factors like preferences, training, undocumented context obsolete over time. In other words, the requirements to be a durable system of record are evolving.

The historical scorecard

Before asking what changes in an agentic world, it’s worth being precise about what drove stickiness for a system of record in the first place. The first few really focus on how humans interact with software and their preferences. Software was made sticky largely by the UI, habit, human workflow and embedded process.

How frequently is it accessed? A CRM is used every day by the GTM team and beyond. That frequency makes it critical infrastructure, and the human layer built on top of it, such as rituals, muscle memory, management cadences built over years, is often the hardest thing to migrate, because it’s not even recognized as something that needs migrating.

Is it write-only or read-write? A sticky SoR is a read-write SoR. A CRM, for example, isn’t a write-only archive; it’s being read constantly. Every call logged, every stage updated, every task created was inputted by someone (who presumably cared about what they were doing). That bidirectional flow means any replacement has to handle live operational data, not just a historical export. There’s no safe moment to cut over, which means that enterprises tend to stick with a provider once they’ve onboarded. On the flip side, an applicant tracking system (ATS) tends to be write-only, there are limited reasons to return to the data after the hire has been made.

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How many undocumented SOPs are there? Business-critical context doesn’t live in any wiki; it’s encoded in workflow rules built up over years by admins and system integrators. In the sales example, the undocumented context is that enterprise deals over $100K need VP approval, EMEA deals require privacy review, and strategic logo discounts can bypass finance only at quarter-end. And this context is often what makes the difference between something getting done in a timely fashion (or without violating some important practice) or not happening at all. Migrating means reverse-engineering every automation, or losing the institutional memory entirely.

Are there a lot of internal or external dependencies? The core question is how many internal systems, team processes, or outside stakeholders depend on this system of record. Internal connectivity refers to other software or workflows downstream of it. External connectivity refers to outside parties like auditors, accountants, or regulators who need direct access to the data for something like an ERP. The higher the connectivity on either dimension, the more that needs to be untangled during a migration.

How critical is the data from a compliance perspective? The core question here is simply: is this system compliance-critical? Compliance-critical systems like payroll, ERPs, and HR data require a legally defensible source of truth, strict admin access controls, and direct involvement from auditors and regulators in any migration. That makes them significantly stickier. Sales data and customer support tools like Zendesk sit at the other end: you care about continuity and context, but there’s no regulatory exposure if data moves or someone gains access.

Not all systems of record have carried the same switching cost. Score a CRM against an Applicant Tracking System (ATS) across these same dimensions and the gap is stark. An ATS is a workflow tool for a bounded process: recruiting. Once a candidate is hired or rejected, that record is largely write-once. The integrations are narrower. The user base is small and concentrated.

An ERP sits at the other extreme: the ledger is the audit trail, and your accountants, auditors, and regulators become direct stakeholders in any migration. Replacing your ATS is painful but survivable. Replacing your CRM is open-heart surgery. Replacing your ERP is open-heart surgery while the patient is running a marathon.

Traditionally, systems of record have not taken advantage of moat-creators like proprietary data or network effects; the workflow typically created enough of a moat. If anything, consumer businesses have brought together tools and a network; historically SORs have not.

Proprietary Data - While many systems of record collected customer data, they didn’t really do much with that data (and often contractually couldn’t). So while a CRM has a rich set of data and could aggregate across customers to generate cross-customer insights, they never did in a meaningful way (although there have been some attempts like Salesforce’s Einstein).

Network Effects - The holy grail would have been network effects. The CRM becomes more valuable because software sellers can find buyers. Like data, network effects have been weak at best for systems of record historically.

So if the UI disappears — and agents arrive — what’s left?

An agent doesn’t need a browser. It needs an API, context, instructions, and the ability to act. Two things made this possible at scale: LLMs became capable enough to reason. As such, an agent can now read context, form a plan, select tools, execute actions, and review output, without a human in the loop for most tasks. And MCP standardized tool access, giving agents a common interface to call external capabilities. An agent with MCP access to your platform can do what a human user does in milliseconds, at scale, without a browser. With the right context, computer-using agents should be able to navigate incumbent software interfaces without even needing APIs.

Simplistically looking at it, there are now three paths for a software buyer:

1) Incumbent system + agents. Use the incumbent’s CLI and APIs — either through their native agent product (Salesforce’s Agentforce, SAP’s Joule) or by building your own agents on top. (Suspend disbelief that APIs are complete and usable and that going headless is not as operationally complex as it is.)

2) DIY the system of record entirely. Build your own data model, operational logic, and things like permissioning, audit trails, integrations, etc. and your own agents from scratch (likely leveraging third party agent building and database tools).

3) Buy an AI-native replacement. Buy the new generation of software built from the ground up for the agentic era, designed for machine readability, with agent orchestration as a first-class feature rather than a bolt-on. This could be headless.

So, what stays from the old scorecard? The elements driven by human behavior and preferences fade away like frequency of access or read vs. read-write which are related to human muscle memory. Agents may kill muscle memory as a moat, but they do not kill operational logic and context as a moat. If anything, they make that logic more important, because agents need explicit rules, permissions, and process definitions in order to act safely.

Undocumented SOPs stay important in the short-term. The institutional logic encoded in your workflow rules is exactly what agents need to operate correctly on your behalf. It’s also the hardest thing to reconstruct. That doesn’t export cleanly, yet, especially when there are still humans involved in some part of the process. However, capturing context is becoming easier, and as agents replace more labor this becomes less relevant.

Connectivity is still hard to unwind and extends further. The connectivity factor shifts. It’s less about keeping up with humans and more about maintaining connectivity across traditionally siloed functions and software. A CRM agent needs to stitch together data and context across sales, billing and customer success. And if your platform is also the node through which agents from multiple external organizations transact buyers, sellers, partners, the dependency deepens further. An incumbent with agents is going to have a tougher time working across the primitives of various underlying software, as would a DIY database and set of agents.

Compliance-critical data remains important. Data for regulators or with regulatory or legal risk needs a single trusted source of data. A customer is less likely to switch if they trust their existing product. Take payroll and accounting data - an agent might want to access this data, you’re less likely to build and maintain this in-house. In a fully agentic world, one of the hardest unsolved problems is: which agents are authorized to do what, on whose behalf, with what auditability? A system of record that becomes the identity and permissioning layer for agent-to-agent interactions has a structural role that’s genuinely hard to displace, not because of the data it holds, but because of the trust architecture it enforces.

Going forward an increasingly relevant set of factors become important for driving defensibility for AI native startups:

How hard is it to recreate the SoR? - Data is going to matter more in a few ways. First, in the near term, the ease in extracting and recreating the data that underlies the system of record. AI is making this easy with a number of tools that enable a user to do this. In the near term, incumbents can and will make this harder by making APIs painful, gated, incomplete, or economically unattractive, if APIs are even provided at all. But as the extraction tools get better, particularly as computer using agents improve, they will make it even easier. Simultaneously, of course, new companies are recreating a richer set of data from emails, phone calls and voice agents, and internal documents. AI lowers the cost of recreating the first 80% of a system of record. The remaining 20%, which are the exceptions, approvals, compliance requirements, and edge-case workflows, is still what separates a useful wedge from a true replacement.

Is there meaningful proprietary data? - Second, the data itself becomes more interesting. The defensible data is not the data you import; it’s the data your product uniquely causes to exist. We talk about walled gardens of data - data that is either proprietary, regulated, or constantly needs to be updated. A software provider that has invested in collecting authoritative and complete data has an advantage over general-purposed providers or competitors that don’t have this data. Another vector here around data is when the data depends on internally generated actions. The best businesses won’t just warehouse data entered elsewhere. They will generate new data exhaust through being in the loop and include things like observed behavior, response rates, timing patterns, process outcomes, benchmarks, exception patterns and agent performance traces. The key thing here is that the data is the context now.

Does it own the action layer? - In the old world, storing the record was enough. In the new world, agents take action and defensibility may shift toward products that can operate in a closed loop from taking the action, to capturing, the outcome, to using that feedback to improve future decisions. For an ERP, this could be approving spend, triggering payroll, reconciling invoices, sending notices, etc. Products that close the loop are more defensible because they sit inside execution, not just observation: they generate unique data, improve with use, and become harder to remove without breaking the workflow. The value here increases of course with more context gathered and more edge cases handled.

Is there a real-world execution element? - Business models that have connectivity into real-world operations that will not be fully automated. The obvious examples are businesses with an operations network built out, like DoorDash, which historically were not systems of record but are instructive here. More broadly, any software business that closes the loop into services, fulfillment, logistics, field operations, or payments has a different kind of defensibility than pure SaaS. These companies do not just store the record or recommend an action; they dispatch people, move goods, or complete the service.

For builders, this suggests opportunity in markets where software can increasingly decide and agents can increasingly coordinate, but where the final mile still requires execution in the real world. For example, vertical software tied to field services.

Are there network effects? - Historically, network effects were weak in most systems of record because the software was primarily internal. But in an agentic world, network effects may become much more important if the system is embedded in a multi-party workflow. If the system mediates recurring interactions between multiple parties like buyers and sellers, employers and employees, companies and auditors, vendors and customers, payers and providers, then each additional participant can make the network more useful to the next.

One way is via shared workflow coordination: the product becomes the place where both sides of a process transact, exchange context, and resolve exceptions. Another is through benchmarking and intelligence: the system can surface norms, anomalies, and recommendations based on patterns observed across the network, which works together with the data point above. And a third is through trust and standardization: once counterparties begin to rely on the same rails for approvals, handoffs, compliance, or payments, the product becomes harder to displace because it is no longer just a database, but part of the coordination infrastructure for the market itself.

How technically capable is the buyer? - In a world where anyone can theoretically build their own agents, there is still a wide range in buyers’ actual ability to do so. Especially in vertical end markets and among functional buyers that have historically not had strong internal engineering resources, the odds that they will build, maintain, and continuously improve their own database, workflow logic, agent stack, and governance layer remain low. Cost matters here too: DIY may reduce software licensing in theory, but often shifts spend into implementation, maintenance, and internal complexity. This means there is real opportunity in categories where the buyer’s operations are operationally complex but technically underserved which is true of much manufacturing, construction back-office, industrial and field-service workflows, or for areas like accounting.

There are a few other important factors, which will also be table stakes for software. For example, the ontology needs to be different. A lot of “DIY database” thinking underestimates how much value lives in the object model itself. Incumbent software was built for dashboards, reports, and humans, capturing workflow. This would be opportunities, tickets, candidates, etc. Agentic schema needs to capture reasoning, actions, state tracking, exception handling, delegation, and coordination across systems. The native object model might become tasks, intents, threads, policies, or outcomes instead.

Similarly, permissioning needs to be updated for managing agents, not just humans. This includes: who can do what, through which agent, under what policy, with what approvals, with what audit trail, and with what rollback / exception handling.

And of course, all of this is in the context of cost (e.g. how much it costs to build and maintain agents/database, how much the API access costs), which again circles back to things like how hard it is to recreate the data and the number of dependencies.

So where does this leave us?

As incumbents go headless, they are making an implicit bet that the data layer will remain the source of value. In some categories, especially those that are deeply compliance-bound like financial services, that bet may hold for some time and going headless may be farther away. For the software builder, the opportunity to compete against the incumbents as they go headless and build durable software changes. The next generation of systems of record are already starting to look different: not just repositories of data collected to log human work, but agentic such that they capture the context, initiate the work and record the data exhaust. Moreover, the most interesting businesses will extend into real-world execution, coordinating the likes of field workers, logistics providers, service teams, and physical assets, or sit between multiple parties. They will be mixing the business models of the old world, and the core of the traditional system of record, the data, will be what sits in the background.

A big thank you to @astrange for her thought partnership on this!

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

When DUDE44, an American F-15, was downed over Iranian territory, the United States expended considerable resources—logistics, risk, cost—to recover the stranded Americans. Not because the math favored it in any narrow operational sense, but because as Americans we believe in “no man left behind” as a tenet.

This deeply held belief—that we are a nation that does not leave our warfighters behind—shapes our R&D agenda and our military capability stack. It shapes what war is, when we choose to fight it. The engineering requirements of “how do we bring them home” are a technical constraint for us, and they are the good kind of constraint: one that links our tools and our values to each other.

Our commitment to “no man left behind” matters a great deal in how we prepare for autonomous warfare. The moral case for autonomous warfare is not despite our values, but because of them. A democracy that refuses to replace soldiers with machines when machines can do the job is breaking its covenant with the people it sends to fight. America must now build autonomous systems at both the quality and quantity required to win. We have the talent advantage. We are losing the production race. The stakes are whether the United States arrives at the next conflict with overwhelming superiority in autonomy, or whether we cede that advantage to adversaries who are already building it.

From the Stick to the Swarm

The entire history of warfare can be read as a single, accelerating motion: extending the standoff between weapon and target. The first man to pick up a stick made a two-foot standoff. The first to throw a rock made it a hundred. The American sharpshooter at Saratoga, firing accurate rounds from cover at three hundred yards, opened a gap British line infantry could not close. The British called it cowardly when they saw the standard panic response of an army watching its officers fall to a weapon it cannot answer. But Britain fielded its own riflemen within a generation. This is how it goes.

The same charge has followed every standoff weapon since. The airplane moved the standoff to hundreds of miles. The Italians used it for reconnaissance in 1911, and by 1941 carrier aviation let Japanese pilots fly 230 miles to Pearl Harbor and bomb a fleet whose faces they would never see. The bomber over Hiroshima killed roughly 80,000 people in a flash and ended the war in the Pacific without the million American casualties projected for an invasion of the home islands. An infantryman asked to do that work one body at a time could not have done it. The technology did the work the human could not. It also brought a generation of American servicemen home alive.

The nuclear bomb extended the standoff to civilizational scale and, by being too costly to use, anchored it there. The United States built the largest arsenal in human history and used exactly two warheads. Cold War deterrence did not eliminate war. Proxy fighting continued in Korea, Vietnam, Afghanistan, and dozens of smaller theaters. But it kept the great powers from direct confrontation for eighty years. The lesson, drawn correctly by the strategists who set policy in 1945 and after, is that capability prevents catastrophe more reliably than restraint without it.

The next iteration of that machinery is already here. Cheap, swarming, AI-targeted autonomous systems will dominate the battlefield of the next century the way airpower dominated the last. Ukraine and Russia have been fighting an autonomous war for three years, both sides building, fielding, and adapting in real time. The first-person-view (FPV) drone costs $400 to $500 per unit, roughly the same as a single 60mm mortar round. For the price of an unguided munition, the modern infantry unit now fields a precision-guided one.

Operation Spiderweb, June 1, 2025, was the proof of concept at strategic scale. One hundred and seventeen FPV drones, concealed in wooden sheds bolted to the backs of trucks, driven across Russia by civilian drivers who didn’t know what they were carrying, launched remotely from inside Ukraine, guided by AI trained on museum models of Soviet aircraft. Forty-one strategic bombers destroyed or damaged. An estimated $7 billion in losses for an operation that cost in the thousands of dollars. No Ukrainian operator was within a thousand miles of the kill zone. No Ukrainian casualties.

In contrast, Russia fed infantry into Avdiivka in human waves, at casualty ratios that would end any American commander’s career, absorbed as acceptable cost because the underlying obligations are simply different. A military that treats soldiers as mass does not face the engineering problem of how to extract a casualty from a drone-saturated environment. It never asks that question, therefore it never builds that answer. The nation that goes back for its people just builds different machines than the nation that does not.

The Usual Concerns

Some make a serious case that autonomy is structurally different from prior leaps in standoff, and that the differences make it war-promoting rather than war-deterring. Three versions of that claim deserve direct rebuttal.

The first holds that autonomy lowers the threshold for democratic leaders to authorize force by removing the political cost of casualties: the flag-draped coffins coming home to Dover. The argument gets the moral framing wrong. We have never said Kevlar encourages war, or armor encourages war, or that the medevac helicopter encourages war. America must defend our interests. Casualties in that pursuit are a tragedy to be minimized. Refusing to minimize them, in the name of preserving political restraint, is not a coherent moral position.

The second holds that AI removes the psychological resistance to killing. The idea traces to S.L.A. Marshall, a World War II combat historian who claimed that most soldiers, as many as 85%, never fired their weapons in battle, held back not by fear but by a deep human resistance to killing another person. Dave Grossman later built on that finding in On Killing, arguing that modern military training works precisely by conditioning soldiers to overcome that resistance. The concern, then, is that autonomous weapons do the same thing, permanently and at scale. But the argument conflates two different resistances. Marshall’s research, contested as it is, was about the soldier on the trigger, the rifleman who hesitates at close range. The decision to go to war is made by commanders and political leaders who have never been on the trigger and have never carried that resistance. Lincoln sent Sherman through Georgia. Truman authorized Hiroshima. The wall Grossman names is the wall the trigger-puller hits. It has never been the wall that restrained the order to fire. Removing it changes who pulls triggers. It does not change who orders force.

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The third holds that a nation with mature autonomous capability, facing one without, has every incentive to use the asymmetric window before it closes. The historical record contradicts this. The United States held a unilateral nuclear monopoly from 1945 to 1949 and used the weapon zero additional times. Asymmetric capability has rarely produced inevitable use. What it produces is leverage, exercised through signaling and credible threat rather than detonation. The more pressing question is what happens when an adversary holds asymmetry over us. Building decisive autonomous capability is how we ensure no adversary holds that asymmetry, in the same way the nuclear arsenal ensured no adversary held nuclear asymmetry over us. The argument for building autonomy parallels the argument that has held the peace under nuclear arms for eighty years.

The moral argument for autonomy eclipses any of these weak arguments against it. In a democracy, the state asks young men and women to put their bodies between the country and its enemies. The state makes that request under a covenant: it will send them only when necessary, and when it does send them, it will give them every advantage available to bring them home. The covenant binds the elected leadership of a democracy more deeply than any other form of government. A democracy that refuses to develop technology capable of substituting machines for human soldiers in lethal roles, when that technology is available, is breaking that contract. It is choosing to send a 19-year-old to die in a place where a machine could have completed the mission.

Not just a weapon

Autonomy, however, means more than lethality. That framing, dominant in the public debate, misses the full extent of what the technology enables and why it matters.

The engineering stack that makes Spiderweb possible—autonomous navigation, sensor fusion, real-time decision-making in denied and contested environments—is the same stack required to put a machine into a hot zone to extract a wounded soldier. Not a related technology. The same technology, expressed in a different mission. Where autonomy is most visibly transforming standoff in its offensive dimension, its application as a life-saving tool is older, less discussed, and in many respects more foundational. That application did not begin with the current war. It predates it. And as automated lethality on the battlefield increases—as FPV drones make open-ground movement a death sentence—the imperative for autonomous capabilities grows in direct proportion to the threat: Casualty evacuations, logistics & resupply, reconnaissance of chemical or radiologically contaminated areas, disposal of explosive ordnance—the dull, dirty and dangerous work better performed by machine than human.

Long before the current era of drone warfare, explosive ordnance disposal units were fielding unmanned robotic systems for the same reason that animates the entire history of standoff: missions deemed too dangerous for human intervention. The calculus was unchanged. What has changed is the capability of the machine and the range of missions it can perform.

In the Bakhmut and Avdiivka corridors, vehicle exposure time in the open during daylight was measured in seconds before FPV strike. Conventional casualty extraction became effectively impossible in certain pockets of the line. Ukrainian units improvised: agricultural drones fitted with stretcher frames, and remote-controlled ground vehicles used for resupply and the recovery of human remains along routes that would otherwise kill any driver who attempted them.

Before, the alternative to these improvised autonomous solutions was not a better conventional solution. It was leaving people behind.

A case study in the Pacific

So then how does this look on the battlefield? Let’s use Operation Detachment, the U.S. invasion of Iwo Jima, February 19th, 1945, as a case study.

The Marines were tasked with seizing and securing Iwo Jima. Specifically: capture Airfield No. 1 (South Field), Airfield No. 2 (Central Field), and deny the island to Japanese forces permanently in order to eliminate Japanese early-warning radar and interceptor bases disrupting B-29 raids, establish P-51 escort fighter bases, and provide emergency landing fields for damaged bombers returning from Japan.

Ten days of pre-landing fires were requested but only three were approved, based on faulty intelligence. Using naval guns and B-24 and B-25 bombers, the Americans softened their target before landing 30,000 Marines from the 4th and 5th Divisions on February 19th, 1945. Over 36 days, more than 70,000 Marines fought at close quarters through volcanic terrain, tunnels, and fortified ridgelines. The mission was accomplished. 26,000 Americans were wounded or killed. The Japanese suffered over 21,000 killed.

Now take the same mission. The same objective. The same enemy, with the same tunnel network, the same 21,000 defenders, the same fortified ridgelines. But fight it with tomorrow’s technology.

The operation itself begins not on the beach, but weeks before anyone considers putting a Marine ashore. Persistent ISR drones, small and quiet, operating around the clock, feed a continuous stream of imagery and signals intelligence into the AI targeting system. Every tunnel entrance is located. Every hidden artillery position is identified by its acoustic signature, its heat profile, its pattern of use. The 18 kilometers of tunnel network that made Kuribayashi’s defense impenetrable in 1945 becomes a database. The AI platform simultaneously constructs and refines the strike plan, categorizing targets by type, depth, and interdependency, identifying which positions are command nodes, which are logistics hubs, and which are mutually supporting and must be struck together. It presents the commander with a prioritized, resourced, and sequenced strike plan rather than a target list. A target list tells you what to hit. A strike plan tells you what to hit first, with what, and in what order to produce the effect you need.

Pre-landing fires no longer need to be the blunt instrument of naval bombardment. At Iwo Jima in 1945, Marine commanders begged for ten days of shelling before the beach assault, but they got three, courtesy of Admiral Blandy. Even so, the limit of three days was never really the problem. Ships were firing at a Japanese tunnel network they couldn’t fully see, at defenders who simply went underground and waited out the shells. The real limiting factor was always the inability to find and fix targets faster than the enemy could adapt. An AI targeting system assigns munitions to individual positions and coordinates simultaneous engagement across the entire island. Not sequential. Not sector by sector. Everything at once, with each strike timed to prevent defenders from repositioning between impacts.

In 1945, American commanders believed the pre-landing bombardment had destroyed most of the Japanese defenders. But it had not, and Marines died for that mistake. The AI targeting system cannot make the same error. It requires confirmation of destruction before marking a target complete, and it knows the difference between a position that has been suppressed and one that has been destroyed. The hardest targets to confirm were the ones underground. But the tunnel network that made Iwo Jima so costly presents no special problem for autonomous systems. Where soldiers cannot go, micro-drones can. Systems small enough to navigate underground passages enter the tunnels directly, reaching defenders that no surface assault could touch.

And if any individual drone is shot down, the mission continues without interruption. There are no people on board to lose.

In this scenario, the beach landing—the moment that cost 5,300 Americans in the first 58 hours—does not happen. The Japanese built their entire defensive theory around one assumption: that the Americans must come ashore on the southeastern beaches. That assumption was correct in 1945. It is no longer correct. Without a beach landing, the volcanic ash terraces, the interlocking fields of fire, the months of tunnel construction oriented to cover the shoreline, all of it is a fortress that the attacker simply never has to enter.

The mission is accomplished. A small special operations element then moves to seize and secure the airfields. Not a division. Not 30,000 men across seven beach sectors. A team.

The island is denied to the enemy. The objective is identical. The human cost is not.

Kuribayashi was a brilliant commander who correctly identified the single point of American vulnerability and built his entire defense around exploiting it. Autonomous systems guided by AI don’t eliminate brilliant adversaries. They eliminate the vulnerability.

Peace Through Engineering

America does not wage war the way other nations wage war. That is not an accident. It is doctrine, built from values. We have always been willing to spend disproportionate resources to recover a single soldier, sailor, airman, or Marine, because we believe, at the level of national covenant, that the people we send to fight are not inputs to be optimized. They are citizens. And the state that sends them bears an obligation to bring them home. That belief has shaped our weapons, our tactics, our training, and our tolerance for operational cost in ways that distinguish American warfighting from our adversaries. Autonomous systems are not a departure from that tradition but its fullest expression.

For the first time in the history of warfare, the technology exists to honor that covenant completely, to accomplish the mission without sending the man. The United States must now field it at the quality and quantity that delivers a decisive advantage. Historically, the American defense establishment has struggled to achieve dominance on both of those two axes.

The first one is quality, the intelligence layer: sensor fusion, target discrimination, electronic warfare resilience, swarming behavior, on-board decision-making in degraded communications environments. This is the part of the problem the United States understands. Our AI talent pool, our frontier model labs, and our operational experience integrating advanced systems are durable advantages.

The second is quantity. Mass. The capacity to produce attritable platforms at the volume the modern battlefield actually consumes. This is the part the United States has done poorly for fifty years. Our procurement system rewards exquisite, high-cost platforms in small numbers. The F-35, by every measure of capability, is the finest fighter aircraft ever built. By every measure of cost-per-unit and units-fielded, it is a cautionary tale. China is not making the same mistake. Chinese drone production capacity, by open-source estimates, exceeds the entire NATO production capacity by an order of magnitude.

Both axes are required. Quality without quantity gives you boutique capability: beautiful systems too rare to use. Quantity without quality gives you dumb mass, attrited at the same cost ratios that destroyed Russian armor in Ukraine. The United States needs both, and the procurement system has to be restructured to deliver both. That means treating attritable autonomous platforms as a different procurement category from crewed aircraft and surface combatants. They are. They should be procured, fielded, and replaced like ammunition, not like aircraft carriers—expendable, not durable items. We have the talent advantage, but we are losing the production race.

The strategic logic for autonomy is the strategic logic the United States has faced in every major technology race of the last century. We did not build the atomic bomb because we wanted to use it. We built it because the alternative was a Reich or a USSR that built it first and used it without our restraint. The same logic applies, in sharper form, to autonomous warfare. China is racing toward decisive autonomous capability. The Chinese state operates without American legal constraints, without American norms about civilian casualties, and without American restraint about authoritarian uses of surveillance and force. A future in which the People’s Liberation Army has decisive autonomous superiority over Taiwan, the South China Sea, or American forces in the Pacific is a future in which the rules of force projection are written by Beijing.

If China moves on Taiwan, the form will not be a 1944-style amphibious assault. It will be cyber, information operations, autonomous logistics interdiction, and drone swarms over Taiwanese command nodes: the full toolkit, applied at strategic scale, designed to break Taiwanese will before any visible kinetic engagement begins. The only thing that prevents that future is a credible American autonomous capability that makes the Beijing calculation come out the other way. Recent American operations against Iranian and Venezuelan targets demonstrated autonomy at strategic effect. Beijing’s silence in response was calculation, not indifference. The window in which we hold decisive advantage is the window in which we deter. We must not waste it.

The standoff that started with a stick has reached its current form. There will be further milestones; autonomy is not the last. What will not change is the obligation: the obligation of the state to its citizens, the obligation of the leadership to the men and women it might one day send into harm’s way. We owe them our best engineering, our largest arsenal of attritable systems, and our most capable AI. We owe them Peace through Strength written into the budget, not just into the speech. The technology that keeps them home is a moral asset, not a moral hazard.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Other Income

Much has been made of the massive earnings growth in the public markets, that is large to begin with, but expected to get even larger, this year and next.

There is, however, a fun little wrinkle underneath the earnings story that’s not something you see everyday. Not all income comes from the same place. And the share of hyperscaler income attributable to “Other Income” was exceptionally high:

“Other income” was more than a third of net-income in Q1, even though historically, it’s ~5-10% (give or take).

Other Income can mean a lot of different things, but in this case, the hyperscalers (but really Amazon and Google, mostly) explicitly attributed nearly all of the gains (~$53B) to their private market investments. Alphabet’s CFO said, “Other income and expenses was $37.7B . . . primarily due to unrealized gains in our nonmarketable equity securities portfolio,” while Amazon flagged in its 10-Q its $15.6B gain (net of expenses) “from our investments in Anthropic.”

The “other income” story is an investment returns story. The hyperscalers are good at venture, one might suppose.

Putting aside, though, Google’s and/or Amazon’s recent successes in their private market tech investments, the truly staggering thing is how much everyone invests in tech—you might even say (as we are wont to do) that tech is the cycle.

KKR estimates that tech-related capex is the only kind of capex currently contributing to growth (and it’s contribution is growing)—in fact, tech capex contributed 1.9% of the 2% total GDP growth in Q1, i.e. basically all of it.

But, tech investing is bigger than just capex, and its role in the economy is bigger than its recent contributions to GDP.

By the BEA’s measure of total business capital expenses (which includes R&D and software, in addition to capex), tech is now 55% of all business investment in the US:

Tech’s share of capital expenses has been steadily climbing for quite some time, and there’s good reason to think it will continue to climb (perhaps even more quickly). As per Yardeni Research:

Before the Age of AI, economists were taught that there are only three factors of production, namely, Land, Labor, and Capital . . . Now, economists should recognize that there is a fourth factor of production, namely, Data . . . The Digital Revolution increases the incentive to create more Data (a.k.a. Information), especially now that AI tools can process so much more of it, increasing its value . . . All the data increases the demand for “compute.”

In other words, the more useful data becomes, the more we invest in it (and the tools around it), and AI has made data even more useful than before.

Good on Amazon and Google for doing VC, but the truth is that we’re all tech investors now.

The Slop Surplus

Great news, everyone: there are now so many more e-books to read, thanks to AI:

Monthly releases of Amazon e-books has tripled since ChatGPT’s release.

There are two ways to interpret this chart:

First, is the easy one: AI showed up in late 2022, the slop tsunami began, and Amazon is now drowning in machine-generated junk. By late 2025, new e-book releases were running at over 300,000 per month (roughly triple the pre-ChatGPT baseline).

Second, is the slightly more nuanced one: yes, there’s a slop tsunami, but there are still more “quality” books than before.

A new NBER paper from Imke Reimers (Cornell) and Joel Waldfogel (Minnesota) suggests that the supply increase is large enough that even with average quality dropping, the absolute number of moderately good books rose. Reimers and Waldfogel calibrate a nested logit demand model and find that the 2025 choice set delivered about 7% more consumer surplus than a human-only counterfactual would have. Not earth-shattering, but positive and rising every year. A 2023 reader was barely better off; a 2025 reader, meaningfully so.

In fact, one of the biggest beneficiaries of adding AI to the mix are incumbent authors (the ones publishing before LLMs existed). Incumbents got much more productive after 2023:

AI hasn’t just ushered in robot-authors, but it has super-charged the human authors as well.

This is roughly the prediction Marc Andreessen made on David Perell’s podcast a couple years back: “It’s now so easy to write that we are absolutely awash in bad content . . . On the other hand, these tools are now so effective that there ought to be a giant explosion of high quality content that goes right along with that.”

The slop is real, but so is the surplus. And the writers who were good before LLMs are getting more done.

Call Centers, Not Dead Yet

David George just wrote a whole thing about how the AI jobs apocalypse is a fantasy. You should read it. It’s good.

One point he makes is to distinguish between AI “substitution” v. AI “augmentation”, whereby the former category of workers are certainly at-risk, while the latter become more valuable than before. One ready example of a job in the substitution category is customer service—that makes sense: AI can handle all the Q&A, with infinite patience to boot.

Maybe so, and it does seem likely that customer service will face substantial substitution, but however logical that may sound, apparently someone forgot to tell the customer service reps:

Per Apollo, IT and business processing industry employment in the Philippines (the call center capital of the world) rose from 1.15 million in 2016 to 1.9 million in 2025—straight through every major leap in AI capability. The industry’s trade group is projecting another 70,000 jobs added in 2026 (+3.7% YOY).

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It’s not just the Philippines that seems relatively immune from the great customer service replacement—it’s true in the US, as well:

Demand for customer service reps has perked up, more so than the field:

Indeed’s job-posting data shows customer service jobs are not only increasing, but they’re running well-ahead of the (negative) headline figure, growing ~10pp faster YOY.

Even more striking, the flippening is fairly recent (August 2025).

Does that mean that everyone is wrong and actually AI is a massive tailwind for customer service reps? Well, probably not.

The story here is really about the relative costs of text-based LLM output v. voice. The latter is much more expensive, and it’s still too expensive to justify fully automating the function. Goldman Sachs actually ran their own internal experiment and estimated the head-to-head costs of humans v. AI call center reps, as being roughly comparable:

Goldman’s all-in estimate for an AI rep is $92/day, which is slightly more expensive than the $90/day a human costs. That stands in comparison to a coding agent, which relies entirely on text, and is orders of magnitude cheaper than a human—of course, the big difference between code and customer support, is that there is much, much more latent demand for code than customer service. Demand for SWEs has also accelerated, substantially.

The anecdata supports the notion that AI may eventually substitute-out call centers, but for now, the juice isn’t worth the squeeze (in many cases). In early 2024, Klarna announced that it had replaced 700 customer service agents with AI. The CEO said the bot was doing the work of all of them, and it was the most-cited example of “AI is replacing humans” in the service economy.

By May 2025, the CEO had reversed course, and Klarna started rehiring as service quality dropped and customers were getting generic, repetitive answers. “We focused too much on efficiency and cost,” he told Bloomberg. “The result was lower quality, and that’s not sustainable.”

This won’t last forever. API costs are dropping fast, companies like Decagon are scaling extremely quickly, and the parity number probably looks different in 18 months.

In AI, Great Products Win Fast

AI continues to go mobile at an incredibly rapid pace:

All of downloads, monetization, and time-spent inflected upwards in Q1—monetization and time-spent both nearly doubled yoy.

Maybe people are spending less time on social media because they’re spending more time vibe-coding killer stuff on their phones? That wouldn’t be so bad.

Bonus chart: per YipitData, the number of panelists using 2-5 and 6-9 AI vendors both continue to rise—at this point, less than 20% are using just one vendor.

There’s no winner-takes-all in the B2B AI market, for now, at least.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

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Almost everyone in American public life learned how a bill becomes a law from the same place: a three-minute animated segment on Saturday morning television. The Schoolhouse Rock version is not wrong, exactly. But it leaves out nearly everything that matters in determining whether a bill becomes a law in today’s Congress.

Members of Congress have introduced hundreds of AI bills. What is their path to becoming a law? What kinds of bills are likely to survive the legislative process?

We’re fortunate at a16z to have a whole team of people who know exactly how an AI bill becomes a law, including Ben (one of the authors of this piece) who served as floor director for House Majority Leader Steve Scalise for several years. This piece breaks down the whole process from start to finish, including:

• The funnel dynamics of proposing and passing bills (only 1.5% make it through!)

• The structural hurdles and constraints of Congress, and how they shape AI legislation

• How to evaluate the political viability of an AI proposal

• What to watch for, if you think a bill will move (or won’t)

“Don’t just stand there, do something!”

Since the public emergence of generative AI tools such as ChatGPT in late 2022, there has been a frenzy among lawmakers at both the state and federal level to regulate artificial intelligence. While the states have successfully enacted a growing web of AI laws, the federal government has been slower to act. This is not for a lack of ideas. AI regulation is one of the most discussed topics among policymakers in Washington. In just the past two years, over 100 AI-related bills have been introduced in Congress, yet only one stand-alone bill, the Take It Down Act, has been signed into law.

At first glance, this gap invites familiar explanations. Some point to industry lobbying as the primary obstacle, arguing that technology companies have successfully blocked or weakened legislative proposals. Others cite partisan gridlock or the inherent difficulty of regulating a rapidly evolving technology. These arguments are frequently raised, and each may describe a real dynamic in specific cases. But none of them accounts for the more fundamental pattern: even proposals with broad bipartisan support and no organized industry opposition struggle to advance through Congress. The barrier is not simply political. It is structural, rooted not in how the legislative process looks on paper, but in the reality of how it plays out.

The US Congress is not designed to translate policy interest into law quickly or easily. It is a system defined by multiple inflection points, fragmented jurisdiction, and procedural hurdles that make legislative success the exception rather than the rule. Any given bill must navigate committee gatekeeping, secure leadership support, survive floor consideration in both chambers, and ultimately be reconciled into a single text before it reaches the president’s desk for signature. These features are built into the design of the Constitution.

But they have particular consequences for emerging technologies like artificial intelligence, where policy cuts across numerous committee jurisdictions, lacks settled partisan alignment, and often requires comprehensive rather than incremental solutions. Even widespread agreement that “something should be done” does not translate into legislative outcomes.

The legislative filter

A sub-1.5 percent enactment rate

The modern federal legislative process operates less as a pipeline than as a filter. Consider the numbers from the 118th Congress (2023-2025): 19,297 bills or resolutions were introduced across the House and Senate. Of those, only 1,809 were reported out of at least one House or Senate committee. 1,029 bills or resolutions passed the full Senate, 995 passed the full House and, ultimately, just 274 bills cleared both chambers and were signed into law.

That is an enactment rate below 1.5 percent. At each stage of the process, proposals are winnowed down through a combination of institutional constraints and political incentives. For most legislation, these hurdles are substantial. For AI legislation, they are particularly acute. The interesting question, then, is not what bills are being introduced. It is what separates the 274 that made it through from the 19,000 that did not.

Why so many bills are introduced if most will die

It is not difficult for a bill to be introduced. Any one of the 535 members of Congress need only reduce an idea to writing, sign the upper right corner of the cover page, and submit it while the chamber is in session. This low barrier to entry helps explain the sheer volume.

It also reflects a deeper reality about how Congress operates. Most bills are not introduced with the expectation that they will become law. In the modern Congress, members often introduce what might be called “messaging bills”: legislation designed to make a political statement, generate media attention, or signal responsiveness to particular interest groups or constituencies. A messaging bill can function as little more than a press release in legislative form. It can also serve a more strategic purpose: by introducing legislation on a narrow issue, a member can stake out early ownership of a policy area and position themselves as a relevant voice in subsequent negotiations. Even proposals with little chance of passage can help establish credibility and secure a seat at the table when broader legislative efforts take shape.

More serious proposals intended to move through the legislative process typically require more preparation. Members work closely with their staff and with legislative counsel to draft statutory text, often incorporating input from committees, leadership, and external stakeholders. When done in an intentional manner, this process can take months; sometimes years. But even well-developed AI proposals face significant obstacles once they leave the introduction stage.

What do the bills that make it through have in common?

Before examining each stage where bills stall, it is worth asking what the small number of proposals that emerge from the legislative funnel tend to share. The patterns are remarkably consistent.

Bipartisan commitment. Not merely bipartisan co-sponsors on a press release, but genuine bipartisan investment in moving the bill forward. There is a meaningful difference between a member lending their name to a bill and a member spending political capital on it. Successful legislation typically involves a bipartisan coalition who have decided to co-own the issue and drive toward its enactment.

External urgency. Legislation tends to advance when the political cost of inaction exceeds the cost of acting. This can take several forms: a crisis that demands a response, a looming lapse in funding or authority, a court decision that creates regulatory uncertainty, a growing state-level patchwork that imposes compliance burdens on national industries, or international competitive pressure. In the AI context, the proliferation of state AI laws, international competitiveness, and growing public concern are generating pressure on Congress to act. Whether and when that pressure reaches the threshold necessary to move legislation is one of the central questions of the current moment.

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Committee and leadership buy-in. Bills generally do not move unless the relevant committee chair or ranking member wants it to. Committee chairs control the hearing calendar, the markup schedule, and whether a bill is ultimately reported favorably to the full chamber.Following a successful committee process, Leadership must then decide which bills should and should not be considered by the whole chamber. This is perhaps the most underappreciated variable in legislative outcomes.

Executive branch buy-in and shepherding. While the legislative process formally ends with the President’s signature or veto, the executive branch often shapes legislation from the outset. Through policy frameworks, public statements, and agency engagement, the White House can signal to Congress that it is seeking legislative action in a given area, helping to define the contours of the debate and catalyze movement on Capitol Hill. Sustained executive branch engagement is often essential for any major proposal to survive the process.

Connection to a legislative vehicle. A bill does not need to pass on its own if it can attach to something that must pass. This dynamic is important enough to warrant its own discussion below.

Where AI bills get stuck

The committee problem

The committee stage represents the first major bottleneck. Committees serve as gatekeepers, deciding which bills receive hearings, markups, and ultimately a vote to be reported to the full chamber. Most bills never make it past this stage.

For AI legislation, committee gatekeeping is particularly consequential because of the issue’s inherently cross-cutting nature. Artificial intelligence touches on commerce, consumer protection, national security, intellectual property, labor, and more. Jurisdiction over AI policy is fragmented across multiple committees in both the House and Senate.

Compare this to a policy area with a single committee home. Higher education legislation moves through Senate Health, Education, Labor, and Pensions and the House Education and Workforce committee. Tax legislation moves through Senate Finance and House Ways and Means. A single chair can drive the process from hearing through markup to floor. For AI, no single home exists. A comprehensive AI bill falls within the jurisdiction of several committees, each with its own priorities, expertise, and political incentives. Committees may compete for jurisdiction, decline to act, or advance conflicting approaches. Rather than producing a single, coherent legislative pathway, this structure disperses authority and makes standalone AI legislation difficult to advance.

This fragmentation is not a bug that can be fixed with better coordination. It is an accurate reflection of how broadly AI implicates existing regulatory structures. The implication is significant: comprehensive AI legislation is more likely to move through leadership-driven coordination across committees than through any single committee acting on its own.

It’s important to note that bills are not strictly required to move through a formal committee process before receiving a floor vote in either chamber, but it is relatively uncommon for sweeping policy proposals—such as a federal AI framework—to advance without some committee action. That said, AI as a topic has already been extensively debated at the committee level through hearings and markups of bills with a more limited scope.

The floor: agenda control and scarcity

Even if a bill is reported out of committee, it must still secure time on the chamber floor. This stage is where many observers, including those who follow AI policy closely, underestimate the difficulty of enactment. A bill can have strong committee support, bipartisan backing, and genuine policy merit, and still never reach a vote in either chamber. The constraints differ meaningfully between the House and the Senate, but in both, floor time is a scarce resource allocated through collective judgment, not policy merit alone.

In the House, floor access turns first on whether a bill appears capable of securing a simple majority of support for passage—218 votes when the chamber is at full occupancy and attendance. That is only part of the threshold question. An equally as important question is whether the measure is supported by a substantial majority of the majority party. House Leadership often tries to operate under what is sometimes referred to as the “Hastert Rule” after former Speaker Dennis Hastert: bills that significantly divide the majority party’s own conference or caucus are rarely brought to the floor, even if they could pass with bipartisan support. A Republican Speaker who would need to rely on 20 Republicans and 200 Democrats to pass an AI bill would be falling short of core party leadership obligations in scheduling that vote. The same dynamic applies regardless of which party holds the majority.

The Rules Committee adds another layer of control and complexity. Unless 2/3rds of the House agrees to suspend the rules and pass a bill, the Rules Committee issues a special rule governing the terms of floor consideration: how long the bill will be debated, which amendments will be allowed, and under what conditions. The majority party controls the Rules Committee, and the rule it produces can shape the outcome of floor consideration as much as the substance of the bill itself. A restrictive rule can block amendments that might fracture a delicate coalition; an open rule can expose the bill to politically difficult votes. For a cross-cutting issue like AI, where members may have competing priorities and comprehensive bills bring with them wide germaneness, the structure of the rule is itself a significant variable.

As a result, the House floor is not simply a venue for considering bills that have left committee. It is a screening mechanism for measures that are both passable and manageable within the majority party’s internal and external politics.

In the Senate, the constraints are different but no less severe. Floor time itself is often the binding constraint. The chamber’s rules permit extended debate and, absent the unanimous consent pipeline, effectively require multiple votes at a super majority threshold to advance legislation. Because any single senator can object to a unanimous consent request, and because invoking cloture consumes up to 30 hours of post-cloture debate time, Senate leadership must be highly selective about which measures receive consideration. The opportunity cost of bringing any bill to the floor is measured in terms of everything else that cannot be considered during that time. For a Senate Majority Leader with a limited number of legislative weeks, this calculus is unforgiving. A bill that would consume a week of floor time for uncertain prospects will almost always lose out to measures with clearer paths to passage or more pressing deadlines.

The filibuster compounds this dynamic. Even a bill with simple majority support in the Senate can be blocked unless 60 senators agree to end debate. For AI legislation, which lacks settled partisan alignment and where individual senators may have strong views shaped by their state’s technology sector, labor constituency, or ideological orientation, reaching the 60-vote threshold is a significant challenge. This makes the Senate the chamber where promising AI legislation is most likely to stall, not because of opposition to the substance, but because of the procedural cost of moving it.

These dynamics interact in particular ways for AI legislation. In the House, assembling a stable coalition of 218 votes requires resolving competing interests across committees and constituencies before a bill ever reaches the floor. Members from different committees may have advanced different or conflicting approaches, and reconciling those approaches for floor consideration is itself a negotiation. In the Senate, the scarcity of floor time favors proposals that are either broadly supported enough to pass quickly or important enough to justify the procedural effort.

Finally, before a bill can be sent to the president’s desk, both chambers must agree on identical text. Reconciling differences between the House and Senate requires additional negotiation, sometimes conducted through formal conference committees, but more frequently through informal bicameral negotiations of the kind that produced the FY2025 NDAA. This work is most effective when it begins before either chamber commits to a final position, and it adds yet another stage at which legislation can stall or fail entirely.

How laws frequently get made: the must-pass vehicle

Given these constraints, it is worth being direct about how much federal law actually gets made. For many policy areas, the likeliest path to enactment runs not through standalone bills but through attachment to legislation that Congress must pass.

The National Defense Authorization Act, annual appropriations bills, and budget reconciliation packages are the workhorses of enacted legislation. The NDAA alone has been enacted for 64 consecutive fiscal years. According to the Congressional Research Service, the bill is “frequently used as a vehicle for legislation under the jurisdiction of committees other than the House and Senate Committees on Armed Services.” The FY2025 NDAA, for example, spanned 794 pages and incorporated the Department of State and Intelligence Authorization Acts along with provisions touching the Departments of Justice, Homeland Security, and Veterans Affairs. These vehicles must move on a predictable schedule, and they create opportunities for provisions that might never survive the process on their own.

This is not a cynical observation. It reflects the design of the system. Congress faces collective action problems in moving standalone legislation on cross-cutting issues, and must-pass vehicles provide a mechanism for overcoming them.

The political landscape

Stakeholders and outside coalitions

Beyond formal procedure, federal legislation typically must also survive negotiation among outside groups, industry stakeholders, advocacy organizations, and political constituencies. For most major policy areas, these actors play a meaningful role in shaping legislative outcomes, as Congress often draws on outside expertise and firsthand industry knowledge to inform policy.

For artificial intelligence, this landscape is unusually broad. AI policy implicates technology companies of vastly different sizes and business models, labor groups concerned about workforce displacement, civil society organizations focused on civil rights and consumer protection, national security stakeholders, creators worried about intellectual property, and consumers. Each brings distinct and often competing priorities. Crafting federal legislation that can attract sufficient support across this landscape is inherently difficult. A provision that satisfies large technology companies on preemption may alienate state attorneys general. A transparency requirement that satisfies civil society groups may impose disproportionate compliance burdens on startups. A licensing or registration regime that benefits established incumbents may raise barriers to entry for smaller competitors.

Larger legislative packages provide a mechanism for managing these tensions. By combining multiple provisions, each addressing different stakeholder concerns, such packages allow policymakers to assemble a broader base of support than any one proposal could achieve on its own. This is one reason, beyond the procedural dynamics discussed above, that coordinated, multi-issue frameworks may be better positioned to advance than narrow, standalone proposals.

Transparency and the cost of legislating

Federal lawmaking takes place under conditions of intense and increasing public visibility. Congressional activity is subject to constant scrutiny from the press, advocacy groups, and an increasingly engaged online audience. The 24-hour news cycle, combined with real-time commentary on platforms like X, ensures that even early-stage proposals are quickly amplified and debated in full public view.

Political science research has examined how this level of transparency affects legislative outcomes. Tim Groseclose and Nolan McCarty, in their influential study “The Politics of Blame: Bargaining Before an Audience,“found that public-facing negotiations incentivize position-taking over compromise, reducing legislative efficiency. When legislators know their moves will be immediately visible to outside audiences, the incentive shifts from finding workable deals to staking out defensible positions. The result is that the same transparency designed to promote accountability can make it harder to assemble the kind of pragmatic, cross-partisan agreements that complex legislation requires.

For AI, where public interest is high and the stakes are easily dramatized, this visibility raises the political cost of every legislative choice. A vote on an AI bill is not just a policy decision; it is a public statement that can be framed, clipped, and circulated. Members must weigh not only the substance of a bill, but also how it will be received by constituents, advocacy groups, and national media.

The contrast with state legislatures is instructive. States have become increasingly active in AI policymaking, and their processes receive far less sustained national attention. Proposals can sometimes advance with fewer external pressures, even when the underlying policy questions are similarly complex. This is not an argument against transparency. It is an observation about the asymmetry it creates: Congress faces higher political costs for legislating on AI than state legislatures do, which partly explains why the states have moved faster.

What to watch for

Given these structural dynamics, how should someone following AI legislation distinguish signal from noise?

Signs a bill might move

The indicators that matter track the structural attributes described throughout this post. A bill is more likely to advance when it has bipartisan lead sponsors who are actively coordinating, not merely co-sponsoring. When it has the engagement of a committee chair who is personally invested in moving the issue. When a companion bill exists in the other chamber, with sponsors working together across the bicameral divide. When it’s being discussed for attachment to a must-pass bill. When an external forcing function is generating urgency, such as a growing state patchwork, a court decision, competitive pressure from abroad, or a salient public harm. And, most relevant now, when Administration and Congressional leadership coordinate on a single legislative push.

Signs a bill might not

Conversely, a bill introduced by a single-party sponsor, with no committee action, and no companion bill, is more likely a messaging bill than a legislative effort, regardless of how much attention it receives. A bill introduced in direct response to a news cycle, without the months of preparation that serious legislative text requires, is another signal. So is the absence of committee leadership engagement after introduction. These indicators do not make a bill irrelevant; messaging bills can shape the terms of debate and position members for future negotiations. But they should calibrate expectations about near-term outcomes.

For technology companies, startups, and others in the AI ecosystem, these indicators provide a practical framework for deciding where to focus attention and engagement. Not every bill that generates a headline warrants a response. The proposals that matter are the ones that exhibit the structural attributes associated with legislative success, and they are almost always a small fraction of the total.

So how does an AI bill become a law?

By navigating a process that is designed, by its nature, to prevent most bills from doing exactly that.

The structural constraints described in this post are real: jurisdictional fragmentation at the committee level, the scarcity of floor time in both chambers, the complexity of bicameral reconciliation, an unusually broad stakeholder landscape, and the heightened cost of legislating under public scrutiny. These are not temporary obstacles. They are features of the system.

But the current legislative environment has several features that make federal AI legislation more plausible than it has been at any prior point. The White House released a National AI Framework in March 2026, signaling executive branch interest in shaping the legislative agenda. House and Senate Committee leadership are actively scoping comprehensive AI legislation. The state-level patchwork of AI laws continues to grow and the high-stakes competition with China for AI leadership is sharpening, generating increasing pressure on Congress to establish a federal baseline. Many stakeholders, including firms like a16z, have publicly advocated for Congress to pass a federal framework to govern AI. And public demand for AI regulation remains high and bipartisan.

The question is no longer whether Congress is interested in AI legislation. It is whether the political conditions will align to move a proposal through a process that filters out more than 98% of everything that enters it. The most likely path runs through a highly coordinated, comprehensive framework of bipartisan provisions, supported by committees of jurisdiction in both chambers, and guided through the process by White House and Congressional Leadership. That may not be precisely how Schoolhouse Rock described it. But it is how an AI bill is most likely to become a law.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

The loudest voices in AI want to talk about what happens if AI goes wrong. We want to ask a different question: what if AI goes wildly, unbelievably right?

That is to say: what if AI doesn’t just reduce the cost of work, but improves the experience and NPS of working? What if, instead of making people feel more interchangeable, AI could make each of us more discoverable and ultimately more valuable? What if AI leads to opportunity becoming truly abundant?

Think back to the moments that changed the arc of your career: someone took a bet on you; an unexpected email landed in your inbox; a call from a friend led to a project that led to a company. Almost every pivotal leap forward in a person’s working life comes down to the same thing: the right opportunity finding the right person at the right time.

History suggests that human intelligence is one of the most important drivers of better life outcomes. Across decades of social science, one conclusion appears again and again: when people’s capabilities are better recognized and better developed, outcomes improve.

Every person has a comparative advantage somewhere in the economy. The problem has never been a shortage of human capability. It’s been that since most of the capability is unstructured and invisible, we simply haven’t had the infrastructure to surface it.

Enter Ethos.

Ethos is building AI-powered infrastructure for human opportunity.

Using AI voice agents, Ethos captures the knowledge, expertise, and nuance that traditional professional profiles miss, making the world’s expert intelligence machine-readable. From there, it matches people to a wide range of opportunities across the economy: market research, expert calls, fractional roles, and full-time jobs. The result is more doors, opening more often, for more people.

The company is already seeing remarkable momentum. More than 35,000 experts are joining every week, spanning the full economy: lawyers, doctors, consultants, investment bankers, electricians, plumbers, and many more. Clients include hedge funds, private equity firms, leading foundational AI labs, and enterprise consultancies. And for some members, the platform is already creating meaningful income: top earners are making more than $10,000 per month on Ethos.

Ethos celebrates human greatness in the age of AI. At a moment when a lot of the conversation around AI is defensive, James and Daniel are building from the opposite premise: AI shouldn’t make you replaceable, but irreplaceable.

They are, in their own right, exactly the kind of human greatness Ethos exists to celebrate. James cut his teeth at SoftBank helping transform companies like Arm, and has spent his career at the intersection of talent, capital, and operating. Daniel spent nearly a decade as a researcher at Google DeepMind working on the frontier of reinforcement learning and agentic systems. It’s a rare combination: a commercial operator who has seen how global firms actually source expertise, and a research scientist who can build AI that understands a person at depth.

The last great improvement in the NPS of employment was Deel, making work global. The next will be Ethos: making work abundant.

If you are an expert — in anything, from law to HVAC — you can build your Ethos profile at askethos.com.

If you are a company that needs access to human intelligence at scale, reach out to the Ethos team directly.

If you want to help build it, Ethos is hiring across engineering, product, and go-to-market.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

The AI Alarmist, “Permanent Underclass” panic isn’t a convincing story. It isn’t even a new story. It’s the “lump-of-labor” fallacy, with updated branding.

The “lump-of-labor” fallacy claims there is a fixed amount of work to be done. It assumes a zero-sum competition between existing workers, and anyone or anything that may do the same job—whether that’s other workers, machines, or in this case, AI. If there is a fixed amount of useful work that needs doing, then if AI does more, humans must do less.

The problem with that premise is that it defies everything we know about people, markets and economics. Human wants and needs are anything but fixed. Keynes famously predicted almost a century ago that automation would lead to a 15-hour work-week, but of course Keynes was wrong. He was right that automation created a “labor surplus,” but rather than just sit back and enjoy the ride, we found new and different productive endeavors to fill our time.

Of course AI will absolutely eliminate some tasks and compress some roles (and there’s some evidence that that may already be happening). The shape of the labor market will change, as it always does when a transformational technology is unlocked. But the claim that AI will produce economy-wide, permanent unemployment is unhelpful marketing, bad economics and worse history. To the contrary, productivity gains should increase demand for labor, because labor becomes more valuable.

Here is our argument why.1

“Checkmate, humans?” Come on.

We agree with the doomers—and, frankly, anyone with their eyes open—that the price of cognition is collapsing. AI is getting better and better at what, until recently, was considered the exclusive domain of the human brain.

The doomer argument goes, “If AI can do our thinking for us, then humanity’s ‘moat’ evaporates and our terminal value goes to zero.” Checkmate, humans. Apparently, we’ve done all the thinking we’re ever going to need or want, and now that AI will carry an increasingly large share of the cognitive load, humans slide into obsolescence.

Here’s the thing, though: precedent (and intuition) shows that when the cost of a powerful input falls, the economy does not politely stand still. Costs fall, quality rises, speed rises, new products become viable, and demand moves outward.2 Jevons Paradox reigns supreme. When fossil fuels first made energy cheap and plentiful, we did more than just put whalers and woodchoppers out of business; we invented plastics!

Contra-doomers, there’s every reason to expect that AI will have a similar effect. Now that AI will carry an increasingly large share of the cognitive load, humans are free to tackle even more ambitious frontiers than ever before.

If history is any guide, we can expect that technological transformation will enlarge the size of the pie.

Every “dominant economic sector” has given way to an even larger successor . . . which, in turn, has made the economy only that much larger.

Tech today is bigger than finance, railroads, or industrials ever were, but still smaller as a fraction of the economy or the market as a whole. Far from being negative-sum, productivity gains have been a positive-sum force on steroids. The net result of having delegated so much of our efforts to machines is that the economy and labor market have only gotten bigger, more diverse, and more complex.

Doomers want you to ignore the history of innovation, freeze-frame the collapsing cost of cognition, and call it the whole movie. They see task-substitution and just stop.

“We’re going to 10x our cognitive output, but rather than do more thinking, we’re going to pat our tum-tums and hit lunch early, and so is everyone else,” reflects not only a massive failure of imagination, but of basic observation. Doomers call it “realism,” but it’s just not what happens, ever!

Luddite Fails

Let’s take a look at what does actually happen, when great leaps forward in productivity ripped through the economy.

Agriculture

In the early 20th century before widespread adoption of farm mechanization, roughly a third of U.S. employment was in farming. By 2017, it was about 2 percent.

If automation caused permanent unemployment, the tractor should have broken the labor market forever. Instead, farm output almost tripled, which supported a massive increase in population—and far from being permanently unemployed, those workers flowed into previously unimagined industries, factories, stores, offices, hospitals, labs, and eventually services and software.

So, sure, you could say that technology upended the career prospects for the median farmhand, but in doing so, it unlocked a global labor (and resource) surplus, and an entirely new economy.

Electrification

Electricity tells a similar story.

Electrification did not just swap one power source for another. It replaced shafts and belts with individual motors, forced factories to reorganize around entirely new workflows, and created entirely new categories of consumer and industrial goods.

This is exactly what we expect to see during the distinct phases of technological revolutions, as documented by Carlota Perez in Technological Revolutions and Financial Capital: huge upfront investment and financial interest, huge declines in the costs of durable goods, and then a generational run for durable goods manufacturers.

It took time for electricity to work its productive magic. At the turn of the 20th century, only 5 percent of American factories used electricity to power their machines, and fewer than 10 percent of homes had electricity at all.

By 1930, electricity supplied almost 80 percent of manufacturing power, and labor productivity growth doubled for decades.

Far from destroying demand for labor, more productivity meant more manufacturing, more salespeople, more lending, and more commercial activity—not to mention the second-order effects of labor-saving devices, like washing machines and cars, both of which pulled more people into higher value endeavors than was previously possible.

As prices for cars fell, both auto production and employment exploded.

That is what a real general-purpose technology does: it reorganizes the economy and expands the frontier of useful work.

We see this again and again. Did VisiCalc and Excel doom the bookkeepers? Emphatically, no. Vastly more efficient computational technology led to an explosion of bookkeepers, and created an entire industry of FP&A.

We lost ~1M “bookkeepers” and gained ~1.5M “financial analysts.”

Those new service-sector jobs

It’s of course not always the case that task-substitution leads to job-growth in some adjacent part of the economy. Sometimes, the productivity surplus materializes as net-new job-growth in an entirely unrelated industry.

But what if AI means that some people will become fantastically wealthy, leaving the rest behind?

Well, at a minimum, those fantastically wealthy people will need to spend their money somewhere, creating whole new service industries from scratch, just like they did before:

Massive productivity gains and subsequent wealth-creation led to entirely new lines of work that may never have come to fruition without rising incomes and worker availability (even though they were technologically possible, well before the 90s). However one feels about service industries that cater to the wealthy, the net result left everyone better off, as more demand led to a massive ramp in median wages (leading to more “wealthy” people).

Ernie Tedeschi, Stripe’s in-house economist, offers a fascinating “all-in-one” example of a job disrupted, transformed, and remade with technology: travel agents.

Did technology reduce demand for travel agents? Yes, absolutely:

Travel agency payrolls are today about half of what they were at the turn of the century, almost certainly because of technology.

So, does that mean technology was a job-killer? No, again, because travel agents didn’t just end up permanently unemployed. They found work elsewhere in the economy, which overall has about the same employment:population ratio now, as it did in 2000 (when adjusted for aging).

Meanwhile, for the travel agents who did remain in the now tech-enabled industry, increased productivity meant higher wages than before:

“Average weekly earnings at travel agencies were 87% of overall average weekly earnings back in the heyday of 2000. By 2025, the ratio had reached 99%, meaning travel agency wages had outpaced the rest of the private sector over that span.”

So, even then, while it’s true that tech devastated travel agent employment, in the aggregate, working-age people are just as employed as they were before, and the remaining travel agents are doing better than ever.

Augmentation > Substitution (and the Jobs that Don’t Yet Exist)

That last point is very important, and reflects yet another way that doomers are only telling one small part of the story.

For some jobs, AI is an existential threat. True. But for others, AI is a force-multiplier—which will make those jobs that much more valuable. For every job at-risk of AI-Substitution, there are other jobs that stand to benefit:

Goldman’s estimated “AI Substitution” effects are more than balanced-out by the effects of “AI Augmentation.”

Management teams also appear to be much more focused on augmentation than substitution, for what it’s worth:

As of now, AI-as-augmentation out-mentions AI-as-substitution on earnings calls by ~8:1.

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While Goldman doesn’t even include them on their “augmentation” list, software engineers are probably the perfect example of an AI Augmented role.

AI is a force-multiplier for coding. Not only are git pushes skyrocketing (as are new apps and new business formation), but it appears as though demand for SWE is inflecting upwards:

Software Development jobs (both by count, and a percent of the overall job market) have been increasing since the beginning of 2025.

Is that because of AI? Truthfully, it’s probably too soon to tell, but AI most definitely augments the work of software engineering, not to mention that AI is top-of-mind for every executive at every company.

With everyone trying to figure out how to incorporate AI into their businesses, it stands to reason that there would be substantial hiring efforts underway to make that happen, making certain employees more valuable, not less:

AI-exposure seems to be driving above-trend wage-growth (which is especially true for systems design).

Those gains may be somewhat narrow for now, but it’s still so, so early. As expertise widens, so too will the opportunities. In all events, it’s not the data that the doomers want you to see.

Meanwhile, according to Lenny Rachitsky (of Lenny’s Newsletter, one of the great tech-insider communities), open PM jobs continue to climb (off their rate-driven collapse) and are now more plentiful than they’ve been since 2022:

Hiring growth in both software engineers and product managers is a concise example of why the “lump of labor” fallacy is wrong. If AI substituted thinking 1:1, then you might plausibly expect, “PMs need fewer engineers,” or you could argue “engineers need fewer PMs,” but that isn’t what we see. We see demand for both continuing to rebound, because what matters is people are getting more work done.

That’s why the doomer failure is primarily a failure of imagination. They focus on the tasks that get automated away, and ignore a new frontier of demand that will create jobs we haven’t even conceived of yet:

The majority of new jobs created since 1940 didn’t even exist in 1940. And in 2000, it was pretty easy to imagine all the travel agents that would be out of a job, but it was probably much harder to imagine an entire middle-market tech services industry built around “cloud migration,” since, of course, the cloud was more than a decade away.

What does the current scoreboard show?

Up until this point, we’ve been focused mostly on theory and precedent because both theory and precedent favor the bulls:

It’s true. With every productivity unlock, we get an increase in demand and/or a reallocation of the surplus to somewhere else in the economy. That means more jobs, including a whole bunch that will get a lot more valuable, and still more that we’ve never even heard of yet. If somehow this time it’s going to be different, the doomers have to make a much stronger case than frantic handwaving.

That “job substitution” is not a civilization-killer (but the opposite really) makes sense. Humanity, by its nature, does not get complacent. We finish one job and look for another.

But, theory and precedent aside, what does the actual data show, with respect to AI and employment? With the caveat that it’s early (for better or for worse), the weight of the data does not support the doomer claim. If anything, the data shows “no change, one way or another,” but there is also emerging data that points in the other direction: AI is more job-maker than job-taker.

First, let’s start with some academic research—this is not an exhaustive literature review, but just a sampling of recent papers:

• AI, productivity, and the workforce: evidence from corporate executives (NBER Working Paper 34984): “Taken together, these results suggest that while AI adoption has not yet led to meaningful changes in total employment, it is already beginning to reshape the allocation of tasks and occupations within firms. In particular, routine clerical and administrative activities appear more exposed to substitution, while analytical, technical, and managerial tasks are more often described as being complemented by AI.”

• Firm Data on AI (Fed Reserve Bank of Atlanta Working Paper 2026-3): “Across the four surveys, more than 90% of firms on average estimate no impact over the last three years.”3

  

• Microstructure of AI Diffusion: Evidence from firms, business functions, and worker tasks (Census Center for Economic Studies, Working Paper CES 26-25): “AI-driven employment change instances remain modest, with only about 5% of AI-using firms reporting any headcount impact—distributed nearly equally between increases (2.3% firm-weighted and 3.7% employment-weighted) and decreases (2.0% firm-weighted and 2.4% employment-weighted).”

  

• Tracking the Impact of AI on the Labor Market (Yale Budget Lab, April 16, 2026). “While anxiety over the effects of AI on today’s labor market is widespread, our data suggests it remains largely speculative. The picture of AI’s impact on the labor market that emerges from our data is one that largely reflects stability, not major disruption at an economy-wide level.”

You get the idea. The recurring refrain from the most recent research is “no change on net, but some evidence of reallocation between jobs and tasks.” In one case, the net-effects of AI implementation on hiring were positive.

There is one notable exception to the “no change” story. Researchers at Stanford, the Dallas Fed, and Census all found (to varying degrees) that entry-level roles with high “AI exposure” are increasingly difficult to find. Before anyone concludes that “AI is killing entry-level jobs,” however, it bears mentioning that these researchers also variously found an increase in entry-level roles where AI is augmentative (and an increase where AI has no impact at all).

But, even if we stipulate for a moment that AI is “killing” certain entry-level roles (as opposed to the effects of broader cyclical hiring trends, as well as “aging in place”), in the bigger scheme of things, the data is showing pretty clearly that the aggregate effects of AI on employment are basically null.4

This is probably the most succinct view of the scoreboard with respect to AI’s impact on employment:

“Still no statistically significant relationship between AI and unemployment or employment growth.”

There is, perhaps, some pull towards AI Augmented roles, and some push from AI-substitute roles:

Hiring growth appears stronger (and unemployment weaker) for “AI Augmented” Industries, while the opposite is true for industries at higher risk for “AI substitution.”

In other words, the aggregate picture is neutral, but not unchanged: some job-destruction, and some job-creation, some jobs deprecated, others now with a premium.5 At this rate, job-postings for devs will exceed the pre-pandemic level in less than two years. AI may have already single-handedly saved the SF Office market, as well.

That’s where we started: AI will definitely kill and/or compress some roles (and businesses), but it’s a mistake to think that’s the end of the story. Reorientation (and eventually growth) of the labor market—as opposed to widespread unemployment—is exactly what we should expect from a transformational technology. It’s happened before, and it will almost certainly happen again (and it looks like it’s already underway).

Knowledge Work is Just Getting Started

It sounds trite, but it’s true: this isn’t the end of knowledge work—if anything, it’s the beginning.

Automation strips out the repetitive layer and pulls human work up the stack. The reason why is simple: humans want to expand! When one layer of scarcity falls, people move up to the next one. When food gets cheaper, we spend more on housing, health, education, travel, entertainment, convenience, pets, safety, beauty, and longevity.

The same thing happens in labor markets. New work keeps appearing because human ambition does not stop, and conquering old frontiers reveals new frontiers to conquer.

New business formation is already exploding, with a pretty decent correlation to AI adoption:

New apps are hitting the app store at a 60% YOY clip:

There should be no reason to think of the modern economy as some kind of museum of yesterday’s roles. Instead, it’s a creative allocation machine, enabling new jobs, new work, new goals, and new inventions, all the time.

Much of robotics has been considered science fiction, because the computational demands in a dynamic environment were too high. But AI is bringing an entirely new robotics industry into scope:

Robotics data sets have exploded, going from tenth to first, in just two years.

There’s a universe of robotics jobs no one has ever needed, until AI unlocks that need.

To repeat, none of this means every role survives intact. The BLS expects customer service representatives and medical transcriptionists to decline, and perhaps that decline is already underway:

Some jobs will disappear, others will shrink. There will be adjustments and painful transitions, and it may take some time for productivity gains to wind their way through the economy (in fits and starts). We should be sympathetic to those changes and pour effort into making them as smooth as possible, including, among other things, with active job retraining (an initiative that a16z is proud to support).

Productivity is supposed to eliminate drudgery, and that is what it will do this time around too. But, the AI job apocalypse story only works if you assume human wants and ideas suddenly freeze at the exact moment intelligence gets cheap. That is absurd. I, for one, reject the Wall-E meme, and I don’t think I’m alone:

The macro story is not a jobless future, where we retire fat and complacent to our Netflix-scooters.

The future is cheaper intelligence, bigger markets, new firms, new industries, and higher-order human work. There is no fixed amount of work, let alone a fixed amount of cognition, and there never was. AI is not the end of work. It is the beginning of abundant intelligence.

LFG!

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

5

It’s actually not the case that the aggregate employment picture is neutral—the effects on “AI exposure” and “AI Adoption,” are basically neutral—but AI has likely been a net-jobmaker, thus far. None of Goldman’s (or the other cited research) accounts for all the hard-hat jobs that AI Capex has created:

Data center construction (and the electrification of everything) have created a massive run on the skilled-trades that’s expected to persist at least through the end of the decade.

Putting aside the longer-term effects of AI on the labor market, the sheer magnitude of AI Capex makes AI a jobmaker, for now. There’s a good argument that AI Capex is the cycle now, and outside of healthcare, it’s the only jobmaker in town.

America | Tech | Opinion | Culture | Charts

A few weeks ago, we announced the a16z Growth Engineer Fellowship—an 8-week cohort of growth engineers, AI-native GTM leaders, and tech-savvy agent builders shaping the future of company growth.

We received thousands of applications from talented people around the world. Today, we’re excited to share more about the 65 fellows we’ve selected to join the inaugural cohort who are leading growth at companies like OpenAI, Replit, Notion, Coinbase, ElevenLabs, Perplexity, and Clay.

Many of these fellows are household names in the growth × AI space who have helped to write the original growth playbooks—like Luke Harries from ElevenLabs, Raman Malik from Perplexity, Ben Shanken from Coinbase, and Drew Bredvick from Vercel.

Some are AI-native engineers now harnessing their technical acumen to build massive growth engines at scale—like Aaron Lu at Notion, Ramya Durvasula at Partiful, and Vaibhav Aggarwal at OpenAI.

Others are translating a depth in product strategy to design powerful new growth systems—like Alkarim Nasser at Google, Ravish Agrawal at Gamma, Emily Gavrilenko at Cursor, and Ajay Prakash at Superpower.

You might recognize Yash Tekriwal from his educational Clay content—or Jordan Crawford from his thoughtful write-ups on growth engineering via Claude Code.

But we’ve also got young hustlers like Diana Lim who are fresh out of college, trained in computer science, and already tastefully automating everything in sight at early-stage startups.

Across the board, these fellows are architecting growth engines behind iconic tech products, engineering custom loops most people wouldn’t believe are possible, and operating at the bleeding edge of what growth can be.

We hope that you’ll join us in celebrating everything this group has already done, and everything they’re about to do next.

Congratulations and welcome, a16z Growth Engineer Fellows!

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

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When you fill a prescription, the inventory system that tracked that drug from manufacturer to pharmacy was almost certainly running on the German software giant SAP. Most of the food in your kitchen passed through one too. So did the parts in whatever you drove this morning; and so did the electricity in the building you’re sitting in. About three-quarters of the Fortune 500 run their core operations on SAP. SAP is a huge and incredibly important company: and it dominates the industry that keeps much of the world’s most important software running.

That industry is system integration. Accenture, Deloitte, IBM, Infosys, and TCS dominate the top of the market, and tens of thousands of smaller firms work in their wake. Their work is the connective tissue of enterprise IT—not only the business process redesigns that follow when a company adopts a new platform of any kind, but also the migrations and integrations between systems that were never really designed to be compatible.

Every Fortune 500 firm has hundreds or thousands of these consultants embedded in its operations. Software licenses themselves add up to roughly $300 billion a year globally; the work of making that software actually function inside large companies costs several times more. Total IT services spend is around $1.8 trillion annually, and integration of enterprise applications accounts for a substantial chunk of it.

This is one of the largest pieces of enterprise technology that has barely changed in twenty years. The delivery model the global SI industry uses today is essentially the one its predecessors built when SAP itself was a startup. The largest SAP migrations at Fortune 500 companies take three to five years and cost between $100 million and $500 million, and roughly 70 percent of large transformation programs fail to deliver what they set out to accomplish.

The reason this work has resisted automation for so long is that it is genuinely difficult.

A typical SAP environment contains data from dozens of overlapping systems, many of them with inconsistent schemas and missing documentation, in which basic concepts like “customer” or “order” mean entirely different things. And they run millions of lines of custom code – much of it written by people who left the company long before the current CIO was hired.

To make this concrete: picture a global manufacturer that has run on SAP ECC for fifteen years. Their procurement system has been heavily customized over time to handle a specific contract structure with their Tier 1 suppliers, encoded in tens of thousands of lines of ABAP that nobody currently at the company fully understands. Revenue gets booked through a custom recognition workflow that was built for a regulatory environment which has since changed twice. Upgrading this company is its own multi-disciplinary project. Every custom flow has to be analyzed and classified. The new environment then has to behave correctly across every edge case the old one handled. Audit has to pass. The cutover has to be executed on a weekend that cannot run long, because the factories cannot stop. Roughly 10,000 ECC environments globally face some version of this problem, and most of the work has to happen in the next three years.

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For decades only humans could deal with this kind of mess. Software wasn’t capable of the judgment, the contextual reasoning, or the code comprehension the work demands, so a global industry grew up to do it manually. The economics worked out beautifully for the firms at the top and badly for almost everyone else. CIOs know perfectly well that their core systems are aging and expensive to maintain. They also know SAP is ending standard support for ECC in 2027, which puts an enormous amount of upgrade pressure on their desks right now. But the alternative to staying on legacy is a multi-year, multi-hundred-million-dollar program with a failure rate north of two-thirds, so most companies do the rational thing. They wait, patch what they can, pay the extended maintenance fees, and hope the problem can be deferred for another year.

I started Tessera because the technology to do this differently has only just arrived, and because the window to build a new kind of company in this space is fairly narrow. I came at the problem from research rather than consulting. What struck me looking at the SI industry from outside was how unusually well its core work matches the strengths of modern AI. The outputs have structure, success criteria are reasonably well-defined, there’s an enormous body of prior code and documentation to train against, and the feedback loops are clean. The parts of this work that have been hardest for humans for thirty years are exactly the parts AI has just gotten good at.

Until very recently it wasn’t. Brittle scripts could shuffle data between identical schemas, but they could not read 20-year-old ABAP and figure out what business problem it had been written to solve. That changed in the last two years, more dramatically than people outside this field tend to appreciate. Modern frontier models can read custom enterprise code, including the obscure dialects like ABAP that most of the world’s mission-critical software is written in. Mapping fields across heterogeneous schemas, which is most of what data migration actually consists of, has become newly tractable. Business logic can be inferred from observed system behavior when documentation is missing, which is the usual situation. The long tail of judgment-heavy structured work, that historically required teams of $300-an-hour consultants is now within reach of a well-orchestrated agent system, at quality that holds up to the same kind of review human work gets.

This is the technical change that makes a different kind of company possible, and Tessera is what comes after it. We are an AI enterprise transformation platform. Software does the work humans used to do, and our people focus on the parts of transformation that genuinely benefit from human judgment: architectural decisions, stakeholder alignment, and the strategic question of how a business should actually run on the other side of a migration.

Our first focus is the type of nitty-gritty ERP work in which SAP has excelled for decades. Our agents ingest a customer’s ECC environment line by line and field by field, producing a comprehensive map of what is there and how it behaves. From that, they classify each component as keep, replace, or retire, and then they build the actual S/4HANA implementation: writing the new code, generating the test suite, validating the data movements, and orchestrating the cutover itself. A six-person team at Tessera can now deliver work that would historically have required sixty consultants, working over two full years.

There is a great deal of activity in the broader space of “AI for services” right now, and I want to be precise about how Tessera differs from most of it. The dominant pattern in the market today is a kind of “autocomplete for consultants”: tools that help an existing human bill faster, while still leaving the underlying delivery model intact. We are doing something more structural. We are building software that does the work, and we have organized the company around that economic reality.

A reasonable question is why the firms that already dominate this market, like Accenture and Deloitte, aren’t the ones doing this. The answer is mostly the standard Innovator’s Dilemma. Their entire business model rests on billable hours. Doing the same work in software, with outcome-based pricing, would compress their revenue per migration by something like an order of magnitude in the scenarios where it works best. Leadership at any of those firms can see clearly that the future of this market is software. The firm itself cannot get there, because everything about how those companies hire, train, sell, and account for revenue is calibrated to a fundamentally different shape of business.

The longer-term vision goes well beyond SAP. Every large company runs on a constellation of enterprise systems: ERPs, CRMs, HRIS platforms, supply chain tools, financial reporting stacks, all of which need continuous integration, modernization, and migration as the business itself evolves. Almost all of that work can become software. When it does, the cost of running enterprise IT falls by an order of magnitude, freeing up enormous amounts of capital and management attention currently absorbed by maintenance and modernization projects. The speed at which large companies can adapt to new circumstances also accelerates dramatically. When a Fortune 500 CFO wants to consolidate two divisions onto a single financial reporting stack today, the project takes 18 months and a small army of consultants. In the world Tessera is building, it takes weeks. Most of the changes that currently get framed as “system integration projects” — territory reorganizations, post-acquisition integrations, subsidiary spin-outs, compliance overhauls — become things companies just do, rather than year-long programs they have to plan around.

The companies that adopt this model first will have a structural advantage over the ones that don’t, and that gap will open faster than most people expect.

This is the largest market in enterprise software that the AI shift hasn’t touched yet. Every business of meaningful size depends on these systems, and the friction of modernizing them is a real drag on productivity for the companies and the economies that depend on them. We are going to take that friction out.

If you are an engineer, a researcher, an enterprise software veteran, or anyone who has looked at a $1.8 trillion industry still operating on a delivery model from another era and thought there has to be a better way, we would love to hear from you.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

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They say you should invest in big markets.

It’s hard to think of a larger market than the one for SIs (system integrators). Large companies can have 50+ ERPs across their many divisions, and spend hundreds of millions each annually on ERP transformation (as well as other systems of record like their CRM and HRIS). There is a vast amount of services spend both internally and externally on consultants and SIs to do migrations and maintenance - companies spend over $500 billion dollars annually on this.

They say you should invest in great people.

Kabir is a whiz kid. After starting college at 13, he graduated from UC San Diego with a CS degree at 17, earned his PhD there at 20 (where his advisor described him as “the strongest research student I have ever advised/mentored in my career” with an “unprecedented … slate of research accolades”), and went on to do research at Meta before founding Tessera. In the 18 months since, he’s closed multimillion-dollar ACV contracts with large enterprise CIOs and recruited a team of more than 30 people (including six people from Netflix, where he was an intern). Kabir’s slope has been one of one.

When you find a great person building in a big market, partnering is a no brainer.

That is what we found in Tessera, where Kabir is building an AI-native system integrator for enterprise transformations. ERP transformations are a massive pain point for enterprise IT teams, often costing companies millions of dollars in consulting costs to analyze custom code, map and validate data, redesign business processes, test integrations, and manage cutovers. Tessera is initially focused on SAP upgrades, including ECC to S/4HANA, but the broader vision is far bigger: to automate the management and modernization of enterprise software systems end to end.

We’re thrilled to partner with Tessera Labs for their Series A.

If you are a talented engineer or operator and interested in deploying frontier intelligence into one of the world’s largest markets at one of its fastest-growing startups, you should consider joining them.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

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For more news from the a16z crypto team, subscribe to their Substack.

Crypto cycles tend to follow a pattern. A wave of speculation pulls in attention and capital. Some of it gets wasted. Some of it funds infrastructure that wouldn’t otherwise get built. When the noise dies down, what’s left is usually more useful than it looked at the peak, and more durable than it looked at the trough.

You see this in every cycle if you look past prices: what actually gets built, and what people keep using when the hype fades. We’re at one of those quieter moments now. And the signal coming through is one of the most encouraging it has been in years.

The clearest evidence is stablecoins. Trading volumes go up and down with the market, but stablecoin usage has kept climbing even through downturns. People are using them to save, to send money across borders, and to pay for things, often exposing just how slow, expensive, and unreliable the alternatives are. Their growth looks less like speculation and more like network adoption: usage compounds because the technology is useful, not because of expectations about price action.

Blockchains are also proving their worth in capital markets. Since the last cycle we’ve seen meaningful growth in perpetual futures for price discovery, prediction markets for surfacing truth, and onchain lending for stablecoin credit markets. Traditional assets are starting to move onchain, and onchain finance is being used for assets beyond network tokens. A new financial system is taking shape that runs continuously, settles nearly instantly, costs almost nothing, and is open to anyone with internet access.

Regulation is moving in the right direction too. The GENIUS Act is a good example of what thoughtful policy can look like: clear definitions, strong safeguards, and room for builders to build. We expect more regulatory progress for the rest of the crypto market through legislation and rulemaking. This gives consumers protection, builders certainty, and mainstream institutions a path to participate.

It’s worth stepping back and asking why this matters now in particular.

Software is getting more complex and harder to trust. AI systems are powerful and largely opaque. The infrastructure the internet runs on is more consolidated than ever. In that environment, the properties that crypto networks were designed to provide become more valuable, not less:

• Systems that are transparent and verifiable

• Networks that are global from day one

• Economic models that align users, creators, developers, and operators

• Infrastructure that doesn’t depend on a small number of intermediaries.

These properties are showing up in real products: in payments, in financial services, in creator platforms, in decentralized infrastructure, in new ways for people and machines to coordinate. Much of this is being built by startups, and increasingly adopted by financial institutions, tech companies, and others to deliver faster, cheaper, and more reliable services.

In practice, this means sending money globally in an instant, holding dollars without relying on a bank, tokenizing assets so they can move frictionlessly anywhere, tapping into composable networks that others can build on, and embedding these capabilities in applications everywhere. It also includes new models that weren’t possible before: where users can own their assets and identities directly, and hold inviolable digital property rights; where swarms of software agents can decide, act, and transact on a user’s behalf, acquiring compute, data, and services as they go; and where increasingly autonomous networks can fund, govern, and evolve themselves through code.

That’s why we’re announcing our new Crypto Fund 5; it is built for this moment. The founders we’re backing with this $2.2 billion fund are working on the part of the cycle that gets less attention and we believe produces more of the lasting value: turning new infrastructure into products people use every day. That is how every important computing platform has eventually mattered, and it is how crypto will too.

a16z crypto

your guide to the next internet from the team at a16z crypto

This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

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The Fastest V-Shaped Recovery Ever

S&P 500 sold-off by ~10% during the Iran conflict, and just 11 trading sessions later, it made a full recovery.

It constituted the fastest V-shaped recovery ever on record.

Last Friday, the S&P 500 closed at an ATH 7,165.08, up 30% yoy and almost 9% over the past month alone.

For situation monitors, the whiplash is a thing to behold. For everyone else, they may not have even noticed their 401(k)s took a header in the first place.

It could just be a coincidence, but either way, these fresh new market highs seem to have revived an old debate: are stocks too expensive? This comes only weeks after wondering ‘Is Tech Cheap?’ What a world.

Well, two of the most successful investors of the past fifty years recently weighed-in, and for their part, they’re inclined to say “yes, stocks are too expensive [for them, at least]”

In a CNBC interview during the March correction, Warren Buffett was asked whether stocks looked cheap. “No,” he replied. “Three times since I’ve taken over Berkshire, [the market has] gone down more than 50%... this is nothing. If they’re 5-6% cheaper, we aren’t in it to make 5% or 6%.”

Buffett is certainly very busy not-putting his money where his mouth is.

Berkshire is sitting on roughly $373 billion in cash – the largest pile in its history – still waiting for that rainy day to go bargain-hunting.

The second hall-of-famer to cast some doubt on stock prices is Paul Tudor Jones. In an interview with Patrick O’Shaughnessy, PTJ pointed out just how high stock prices have become, in relation to the economy writ-large:

“We’re at 252% of stock market cap to GDP. In 1929, we were 65%. In 1987 we got to 85-90%. In 2000, 170% . . . Let’s say we mean revert to the past 25 or 30-year P/E. That would be a 30, 35% decline. Well, 35% on 250% of GDP is 80, 90% of GDP.

10% of our tax revenues are capital gains, they go to zero. So you can see the budget deficit blowing up. You can see the bond market getting smoked. You can see this kind of negative self-reinforcing effect.”

Sounds bad.

Now, what would cause that mean-reversion to transpire? Neither PTJ or Buffett can say for sure, but they’ve been around long enough to take the possibility seriously. It should be noted that the S&P 500 is up more than 10% since Buffett’s interview, for those keeping score at home.

Far be it for us to question the wisdom of those two giants—and mean reversion can be a helluva drug—but there may be some reasons why the market is structurally different now (i.e. we’re going to question it, anyways).

One reason that prices may be structurally higher is that there is just a lot more cash around now than there was before.

Just to take an overly simplistic view of things, despite all the money currently at-work in equities, the balance of money market funds continues to rise:

MMF balances are at record levels of ~$8 trillion, nearly double pre-pandemic levels.

More cash for MMF, and more cash for equities, and more cash for everyone, make stocks go brrr? Could be.

Fed Chair nominee Kevin Warsh made a similar call-out in his confirmation hearings last week. The Fed’s balance sheet doubled during COVID in 2 years—from $4.2 trillion to a $9 trillion peak. Even after three years of quantitative tightening, it sits at $6.7 trillion today, and roughly 30% of all U.S. dollars in M2 existence today were created in the last five years. That’s a lot of fresh cash.

If you view the S&P 500 as a price-ratio to dollars, the shrinking value of the denominator may be doing more work than people realize.

“The big balance sheet has become an ordinary, recurring force that, I think, has been quite unhelpful,” Warsh told the Senate Banking Committee. He called it “fiscal policy in disguise,” and said the Fed is “not blameless” in the widened wealth gap and K-shaped economy that has emerged.

A second possibility, of course, is what we’ve alluded to before: the reason that stock prices are growing is because earnings are growing and margins are historically wide (and getting wider).

Generally speaking, more profits at fatter margins is a well-regarded basis for assigning higher multiples.

PTJ is certainly right that multiples are historically high and that the value of the stock market (relative to GDP) is too . . . but if you’re going to turn the clock back 30 years for a benchmark of what those values ought to be, then it bears mentioning that profit margins are roughly double what they were in the 1990s, and forecast earnings growth is more than double the long-term historical average.

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Now, perhaps those forecasts are illusory, but still, “the prices are too high because they’re higher than before,” is on its own, a strange objection.

Depending on where you look, you might even say that in some cases prices are relatively low:

On a growth-adjusted basis, software multiples have already mean-reverted to the previous decade’s average.

“This time it’s different,” are, of course, the most famous last words, and when GOATs speak, we listen . . . but certain things are, in fact, different. For now, at least.

First Semis, Then Software

While on the subject of the stock market, one thing we’ve observed before is that while tech is driving a lot of the growth, it’s really semis (and AI infrastructure) specifically that’s driving the lion’s share of returns. Software, for its part, is still scuffling, relatively speaking (again, as we’ve observed many times before).

The out-performance of the AI buildout v. the more subdued performance of rest-of-tech raises an interesting question: how can AI infrastructure trade at a substantial premium, while the putative beneficiaries of AI trade at little-to-no premium, at all? If all this capex is going to be justified, there will need to be tangible benefits that flow through to the token-buying customers . . . but as of now, few-to-none of those benefits appear to be priced-in.

Without going down a rabbit hole of the various possibilities that might explain that disconnect, for present purposes, the point we want to make is that it has happened before: semis went first, and then software did eventually take the cake.

The post-GFC market recovery was also a “semis and infrastructure, first, software and services, second” kind of thing:

During the mobile-tech cycle, it took about 5 years for software to emerge as the clear market leader. Before then, it was semis and infra (i.e. the enabling capex) that led the way.

More precisely:

• Semis like Qualcomm and ARM captured the picks-and-shovels returns from 2010 to 2012 because every smartphone needed chips before any apps could exist;

• the platform layer (Samsung and Apple) came next by selling all those smartphones; and then

• software and services came last, and won biggest—Google and Amazon (and the “app layer” more generally) pulled ahead, as value migrated up the stack to where the applications (now running on all that hardware) lived and breathed.

Is the AI cycle following the same semis-then-software trajectory? It’s not for us to say, but there are certainly some similarities.

First, of course, is the semis-led charge, although this time, instead of Qualcomm leading the way, it’s Nvidia.

Second, is perhaps the acceleration in cloud, one layer up in the stack, where all those chips are being put to work:

Reported revenue backlogs for the hyperscalers nearly doubled yoy in ‘Q4. That’s not supposed to happen to already fully-scaled businesses.

Third is . . . well, the third is where it gets interesting.

The previous semi-led cycle’s biggest winners came from the application layer—Uber, Airbnb, Instagram, WhatsApp, etc.—and enterprise SaaS, as well. They were preceded by all the hardware because without all the hardware, none of the software would have been possible at that scale. It did take ~5 years, though, from the beginning of the semis-charge to when software really took off.

In the AI cycle, we know that the application layer is, in fact, scaling—indeed, scaling faster than ever before, albeit mostly on the private side. Speed-to-revenue for AI companies is unprecedented.

Whether and when the application layer takes off in the public markets—and brings software back to the fore—only time will tell. But this isn’t the first time that atoms have led the bits.

Peak Social Media

Here’s another first.

Meta’s daily active users declined for the first time, well, not “ever,” but the first time since 2021, which makes this the second time ever.

3.5B+ daily users is still a lot, but it’s slightly less than before.

It turns out, however, that “less time on Facebook” may be part of a broader theme.

Time spent on social media globally peaked in mid-2022 and has been falling since.

According to GWI’s tracking of 250,000 adults across 50+ countries, daily social media use across the developed world fell by about 10% from a peak of ~2.5 hours in 2022 to ~2.3 hours.

The decline is sharpest among the demographic most associated with platform addiction: 16-to-24-year-olds. If you’re in camp “social media and the phones are bad for the youngs,” then this is good news.

There is one notable exception, however:

North American social media consumption is still rising. By 2024, it had climbed to roughly 15% more than European levels and is still rising.

Why do North Americans appear to be uniquely addicted to social media? Your guess is as good as ours, but it will be interesting to see how AI plays into this dynamic. AI-generated content is exploding all over the internet, and social media is no exception. Whether standalone AI-content apps for image/video generation, or the tools that platforms are rolling out for creators, AI is increasingly in the creator’s seat (or perhaps the co-pilot’s seat).

YouTube CEO Neal Mohan wrote in his 2026 letter that more than 1 million channels used YouTube’s AI creation tools daily in December, and 9 of the top 100 fastest-growing YouTube channels globally are entirely AI-generated.

Maybe consumers will get tired of it . . . or maybe social media’s hold on our attention spans is just getting started.

Your SaaSflation Is My Opportunity

Here is yet another observation related to the current software malaise: the spillover into the credit markets.

For a decade, software was considered a fantastic thing to lend against. SaaS companies had predictable cash flows and sticky, recurring revenue, which are exactly the kinds of things that lenders like to see. The private equity industry built an entire playbook around it: buy mature software businesses, lever them up, and let the revenue service the loans. Lenders, for their part, were happy to harvest the yield.

But with the big compression in software multiples, that playbook has broken down. It’s had some big implications for private credit, and the publicly traded Business Development Companies that specialize in this sort of thing.

Pricing on software-backed debt has plummeted, relative to comparable credits from other sectors:

Average bids on software loans have dropped to ~90 cents on the dollar, and software now comprises ~35% of distressed loans (usually defined as loans trading with a ~20%+ discount).

Software gets less valuable, so too do software-backed loans. Makes sense, right?

Well, sort of. It certainly makes sense that the equity would reprice, but equity is not debt. And from an actual credit perspective, the loans are still performing about as well as they were before:

Default rates in either of the syndicated loan market or private credit both look no worse for the wear.

Now, present default rates aren’t necessarily predictive of future default rates, but the point still stands: software debt is selling off, even though software lenders are still getting paid, and that’s a bit odd. Just because the value of all future cashflows of a company may be worth less than they were before, doesn’t mean those cashflows are so impaired as to make the debt something less than money-good.

So, what’s going on?

Well, there are, of course, anecdata to make lenders nervous, like this one: Thoma Bravo recently forfeited its SaaS company Medallia back to its creditors. Thoma Bravo bought Medallia in 2021 for $6.4 billion, financing the deal with about $3 billion in debt. As interest rates rose, however, Medallia’s annual debt service roughly tripled from $100 million to $300 million. Fast-forward to the present, and Blackstone and other lenders refused to extend a lifeline, so Thoma Bravo decided to write down its $5.1 billion in equity and hand over the keys.

Medallia’s core business is a feedback-and-survey product, which is exactly the sort of thing in the crosshairs of AI-disruption. A big discount on the equity probably makes sense. But will disruption come so quickly and so intensely such that the company will lack the cashflows to repay the debt, as well? Perhaps, but perhaps not, and what’s true of Medallia isn’t necessarily true of the entire universe of software-backed loans.

But, that hasn’t prevented the sector-wide pall. Asset prices have dropped, fundamentals be-damned. And for the credit-pickers willing to be discerning, that means it’s “go time.”

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Today we’re introducing the a16z Design Engineer Fellowship - an 8-week cohort of AI-native design leaders at the forefront of how software will be designed and built in the future.

We’re looking for cracked designers who have absorbed engineering into their craft and are thinking in systems, not screens. People who are spending their weeks vibe-coding, shipping new tools, and creating new AI processes and workflows that scale across the org.

Right now, the best practitioners are scattered across companies. This fellowship is our way of bringing everyone together to demo their latest work and level up.

If this sounds like you, apply here by Friday, May 22, 2026.

Applications will be accepted on a rolling basis, and the fellowship will begin in June 2026.

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How it works

• 8 weeks long

• Starts in June 2026

• Time commitment is flexible, all opt-in

What you’ll get

• Connect with AI-native design engineers who are redefining how software gets built

• Dinner series in SF (and more cities, by demand)

• Demos, feedback, and live working sessions

Who you’ll be joining

You’ll break bread with founding fellows and industry leaders like:

• Ian Silber - Head of Design at OpenAI

• Benji Taylor - Head of Design at xAI

• Yuliya Gorlovetsky - Head of Product Design at Stripe

• Joshua To - VP of Product Design, AR, AI & Wearables at Meta

• Dana Cho - VP of Design at Pinterest

• Ben Blumenrose - Co-founder and Managing Partner at Designer Fund

• David Hoang - VP of Design & AI at Atlassian

• Ben South - Co-founder/CEO of Variant, Prev VP at Postmates

• David Webster - Head of UX at Google Labs

• John Phamous - Design Engineer at Vercel

• Diego Zaks - VP of Design at Ramp

• Ammaar Reshi - Product & Design Lead at Google DeepMind

• Seth Weisfeld - Co-founder and Chief Design Officer at Function Health

• Jenny Wen - Design at Anthropic, prev Dir of Product Design at Figma

...and many more.

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AI is revolutionizing design

We’re seeing three big shifts in this space.

1. Design is going from output-focused to systems-focused.

Teams aren’t making assets anymore. They’re building systems that generate assets continuously. Brands aren’t campaigns anymore. They’re living, dynamic systems. The future about designing how things get designed.

2. There is a new 80/20 rule for design.

Vibe coding gets you to 80% fast, but what matters most (and what separates “good” from “great” design) is how you close the last 20%. This is where judgment, systems thinking, and craft become the whole game.

AI is commoditizing execution - everyone can make stuff now. So, the defensible value is shifting from exploration to context and taste-making - choosing what meets (or exceeds) the bar, what’s on-brand, and what’s right for your company in a world of infinite output. A distinct POV and refined judgment are becoming the real differentiators.

3. Finally, the roles themselves are collapsing.

Traditional design and engineering roles are merging into new hybrid roles that combine taste, systems thinking, and technical fluency. Proprietary workflows - how you combine tools, structure prompts, build repeatable systems - are becoming the new craft.

Right now, the practitioners defining what design engineering actually means are scattered across companies. We’re launching this fellowship to connect and bring everyone into one place.

We’re looking for fellows who:

• Are closing the gap between design and engineering - whether they came through Figma and learned to build in Cursor, or came through React and developed real product sense

• Ship real products, not just prototypes - and understand the gap between the two

• Hold both truths: AI changed everything and broke things along the way - the security vulns, the unmaintainable codebases, the distance between demo and production, and more

• Bring taste, systems thinking, and technical fluency to everything they build

You might call yourself a design engineer, a designer who codes, a frontend engineer with product sense, or something else entirely. Titles don’t matter - what does is that you’re building at the intersection of taste and craft, and the work speaks for itself.

If you’re interested, apply here. We’re accepting applications on a rolling basis.

Questions? Contact Katie Kirsch. Thank you!

Apply Now

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

As someone who goes back and forth between San Francisco and the Old World pretty frequently, I want to point something out without everybody getting mad. It’s that we’ve lost touch with mainstream, regular doomers.

I’m simplifying a lot here, but AI doomerism is mostly a fear of what will get built; like we went through with nuclear weapons two generations ago. Whereas mainstream doomerism, the kind I still hear from my normie friends, is about what will fall apart: the “Everything is going to collapse!” variety.

I have never had any patience for this way of thinking. Not only because thinking this way robs you of your agency, but also because for the 99% of people in the world who aren’t Balaji, I find it to be completely devoid of any critical thought or insight. The “Fall of the Roman Empire, Back to the Dark Ages” type doomer story is too smug and lazy to be useful. BUT! There is a subtler form of it that I do think is valid, and from which we can learn something. It’s the recognition that many of the load-bearing abstractions of the world aren’t going to collapse per se but they are going to leak a lot in the coming years.

The concept of “Leaky Abstractions” (popularized in our circles by Joel Spolsky’s blog years ago), is a good way to get a handle on the following forces in the world:

1. Why abstractions both large and small, in domains like energy, money, and the internet, are getting the curtain pulled back on the sausage-making underneath

2. What happens when people (and their elected governments) see the details, and try to directly grab the controls themselves

3. The costly second-order consequence, which is rational actors everywhere hardening their defenses against these reflexive attacks on their autonomy.

A few things you didn’t know about online shopping

What are ‘abstractions’, and why do they matter so much?

Abstractions, the way I’m going to use the term non-technically in this essay, are “reliable interfaces built out of less-than-reliable parts”. TCP (a reliable information protocol) is built on top of IP (an unreliable information protocol); global commodity markets (with reliable liquidity and prices) are built on top of not-always-reliable contracts and marketmaking. At their best, they are robust yet simple for the end user; this requires non-trivial engineering to get right.

Let me give you an example. Prior to coming to a16z I worked at Shopify for five years, and one of the great privileges of working there is getting to look behind the curtain at how much complexity there is in commerce. Shopify takes great pride in maintaining what many consider the best version of a critical online abstraction: the humble ecommerce checkout. A checkout page does not look like it has much to it - it’s a form you fill out; how hard could it be?

You learn at Shopify that checkout is a more complex negotiation than you’d think. To give you one example, when you check out a product from an online store, the retailer likely won’t know some details yet, including “from what warehouse am I going to ship this item?” They may not know this answer for a few days. But it matters because, depending on which warehouse they send from, the tax calculated on the item might be different if it was sent from the warehouse on the New York side of the line vs the New Jersey side of the line. (This is why, if you look carefully next time you buy something online, you’ll see estimated tax in small print somewhere.) The checkout may have to guess how much you’re going to pay. Which begs the further question, “Well, what if they guess wrong? Does the merchant have to eat the difference?” This takes you down another abstraction, which is how credit card payments work; I’ll spare you the details. Anyway, there is ample complexity under the hood of a checkout. But if it all goes well, you never see it.

The real miracle of online checkout isn’t just that it works at all; but that it works despite the efforts of various sovereign entities (European countries; Apple) to impose their own ideas on what they feel consumers want in a checkout experience. For example, there is a recurring assumption by those sovereigns that “users care about privacy, and we need to protect it for them”, but in practice, some of those privacy measures (e.g. around shipping addresses), however well intentioned, can poke holes in the abstraction.

With hands-on experience, you’ll find many abstractions like this, that perform non-trivial work everywhere. Behind the simple interface, there’s a complex, intricate mechanism for providing slack in the system, and progressive finality towards the outcome, that makes it look and feel and behave like a simple system to the end user. Some of these mechanisms are contracts and conventions of private actors; some are legal constraints written in public policy. Some of them are social licenses that are granted by a population but could be revoked if moods change, or (more likely) interfered with unwittingly.

Checks and DOGE

In early 2025, following President Trump’s return to office and at the height of DOGE zeal to streamline government services, one of the directives we got from Treasury was, “The government needs to stop writing paper checks. It costs us $2 each to process them, enough is enough, let’s move on from this antiquated technology.” The second I saw this news, I thought two things: one, “Oh no, this is going to affect me personally in an annoying way”, and two, “There has got to be a Patrick McKenzie thread on this,” which sure enough there was.

Paper checks are a remarkable abstraction, by the definition I’m using here. Out of unreliable inputs - an account routing number of a sender, the name and address of the receiver - we have a reliable mechanism for transferring funds from one individual to another, that is resilient to all kinds of exceptional circumstances.

The topic is close to home for me. As an American living abroad, I still pay taxes to the IRS like the rest of you, but for various reasons I no longer have a US bank account from which I can easily arrange direct deposits. Prior to recently, this wasn’t much of a problem, because I could walk to my nearest bank branch and arrange for a certified check made out in US Dollars without any fuss. But my bank stopped providing this service recently, and it’s become tricky to actually get money to the IRS, without paying exorbitant credit card fees. (Another resilient system, albeit a more expensive one.) I have had to find alternate arrangements, however trivial it sounds, in order to harden and defend my ability to remain in the IRS’s good graces every year.

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Sure enough, as Patrick documents with some satisfaction, the US Treasury has since conceded that there remain quite a few edge cases where “put a check in the mail” works a lot better than anything else we’ve got. A few examples he offers include “Unbanked individuals, emergency hardships, national security and law enforcement, international taxpayers, temporary US residents without social security numbers, estates and trusts with complicated signatories…” You get the idea. Nonetheless, the political promise of “We’re going to stop issuing paper checks” took on an inertia of its own, and the US Treasury now quietly pays a third party to do it for them.

Two weeks to flatten the curve open the strait

It is not a good thing, for anyone, that “paper barrels versus physical barrels” has made it into popular consciousness.

The global energy market is one of the most sophisticated, resilient, impressive human accomplishments of the 20th century. Even at the current moment (as I write this on April 27th, the Strait of Hormuz has been essentially closed off to commercial shipping for 8 weeks now), it has held up remarkably well. And over this period of time, everyone monitoring the situation online has become much more aware of how the oil market works; although not necessarily smarter about how it works.

In addition to the literal supply crisis unfolding before us, the other funny thing that’s happening at the online meta-layer above it all, is that oil is now the current thing, so everyone now has an opinion about it. And when popular opinion (or presidential opinion) latches onto specific details within a complex system, and says “Let’s focus on this”; you have the potential for some serious reflexivity. By reflexivity here, I mean the kind of feedback loop where someone’s perception of a situation is an important causal input into the situation itself.

My friend Rory (who’s a great follow, if you’re into this sort of thing) recently got some firsthand experience with this. A few weeks ago shared a map he made on X, showing empty oil tankers heading towards the US Gulf Coast to get product where it’s still available. It got sent out in the context of his nuanced energy market commentary; but the map escaped containment, ended up being prominently posted on Truth Social, where it may have played a role in shaping a select few upstream perceptions, and therefore, downstream events. A similar error of public perception occurred on a few days where the headline WTI (i.e. American) oil price rose higher than Brent (international), inviting the narrative, “The world wants our oil so badly now!” (The real reason why this happened is left as an exercise to the reader; if you want a hint, it’s that these prices reflect futures contracts that roll over on particular days of the month.)

We don’t yet know how the Hormuz crisis is going to resolve, but one thing that’s clearly happening is countries scrambling to not only secure fuel supplies, but also form bilateral deals of mutual interest, like one recently announced between Brazil (a producer) and India (a refiner and consumer). A year ago, a deal like this wouldn’t have registered in public perception; but today it has meaning, as a detail announced in public, on purpose. And just as I write this, the UAE just announced they’re leaving OPEC; I mean, that’s news no matter when it happens. Again, same story - countries want to reassert control over their destiny here. We’re also seeing countries rapidly reevaluate some of their domestic policies that concern their energy sovereignty, as public opinion now allows. The reliable abstraction (the global commodity markets) looks an awful lot like a collection of component parts, at least for the moment.

Leaky abstractions lead to private deals

This past January, former Goldman partner, central banker and now Canadian Prime Minister Mark Carney spoke to the World Economic Forum in Davos, concisely and irrevocably closing the door on the old world order. He did not flinch: ‘The old world and its unified institutions - where we had one global market for trade and one standard for global norms - is finished. The new situation in front of us is a world of individually negotiated deals. There is no “single global anything” anymore; there are only arrangements of mutual interest. Good luck out there.’

From afar, it’s easy to label Carney an ‘anti-Trump’ type character. His approach, aesthetics, and career pedigree are certainly different. But his governance has been consistent with the new Trump world order, where the great 20th century abstractions like free trade have suddenly been exposed, as if under a bare light bulb, to be no more than the sum of many individual agreements.

Now, it’s fair to say, “It always was.” Of course it always was. But the great 20th century global abstractions - free(ish) trade, global energy and commodity markets, the US Dollar system, and maritime Pax Americana - were so smooth, so functional, that outside observers and beneficiaries could just treat them as “one solid thing”. The abstractions held for a very long time. But, just as I’ve had to find alternate arrangements for paying the IRS, don’t be surprised to see countries and other sovereign actors take matters into their own hands, to harden their capacity to do their thing. Will some of that hardening be done technocratically, in the manner of Joel Spolsky’s “software engineer who has to plug the leaky abstraction?” In some cases yes. Will some of that hardening be the worst kind of impulse populism? Also yes.

Closer to home, the tech industry is going through a similar experience, when it comes to “digital sovereignty” and the future of the internet. Like the global energy market, the internet is a modern miracle of universal access and resiliency, built out of highly obscured technological pieces and long-held-by-convention American laws. And in recent years, this abstraction has started to leak - like, for instance, Cloudflare IP addresses (which are a lot of the internet) getting blocked in Spain during soccer games.

The publicly visible consequences of this kind of internet-police legislation are predictable: you train a new generation how to use VPNs, and then the government tries to ban VPNs. But below the surface, the architecture of the internet has already been changing for a long time. The original, public internet is a thing of the past, at this point; it’s more accurate to imagine the internet as actually many private internets that seamlessly hand off and interact with each other.

This new state of affairs wasn’t what anybody wanted; at least at first. But it proved necessary; if for nothing else, for security reasons. Layer by layer, the internet reinvents itself every year - as the saying goes, “by interpreting censorship as damage and routing around it.” The iterative nature of it, where those defenses are built and maintained one layer at a time, is why the internet still almost always works; it’s the best example we have of “you can’t build a complex system from scratch, you can only iterate it out of a simple system that works.”

One possible beneficiary here, over the long run, is what Byrne Hobart calls “Deeply horizontal companies” - an odd collection of tech giants that address the need for “end-to-end defenses” like this. Another world I would study, if you have the time, is other eras of history where our modern abstractions were not yet formed up, yet we got by anyway - early maritime insurance is an example. Across all of these problem spaces, the more you learn about them, the less the doomers make any sense - the world is too motivated, and too skilled. But the world’s abstractions will leak; a lot. So get to know some plumbers.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Workday is arguably the most important and least loved product in enterprise software. More than 10,000 organizations run it, tens of millions of employees live inside it, it’s approaching $10 billion in annual revenue, and the public market values it at roughly $30 billion.

None of that success is because the product is beloved. HR admins spend their days on data entry, workarounds, and hand-holding: running reports across three screens, managing comp cycles in Excel because rolling them out in the system is too complicated, sitting on Zoom calls while business partners click through promotion workflows, waiting for IT to explain why an integration broke this week.

When customers renew at close to 100% every year, it’s usually read as a sign the product is delightful. In Workday’s case, it’s a sign of something else: leaving is close to impossible.

HCM is the last large enterprise software category without a serious AI-native challenger, and that’s about to change. A platform shift more transformational than the one that created Workday is now underway, and soon we’ll see Workday’s last workday.

The cloud shift that created Workday

Workday is itself the product of a platform shift. In 2005, after losing a hostile takeover fight to Oracle (a story worth its own post), the leaders of the then-dominant PeopleSoft, Dave Duffield and Aneel Bhusri, bet that the move from client-server to multi-tenant cloud would reset the HRIS category. They saw that Oracle and SAP would struggle to close the architectural gap, and within a decade every client-server HRIS was a legacy maintenance business. They also saw that large enterprises were turning away from massive upfront CapEx outlays and toward predictable annual OpEx subscriptions, moving off perpetual licenses and on-prem hardware onto subscription software.

Workday won the platform shift on two fronts at once: it was the only major HRIS architected for multi-tenant cloud from day one, and it was already priced for the subscription procurement model that enterprise buyers were converging on. Within a decade it became the default answer.

Why Workday has been so defensible

Every serious attempt to displace Workday at the enterprise layer has failed for the same reason: Workday’s moat isn’t really made of product. It’s made of everything around the product. This consists of:

• Deep technical and human wiring. Workday sits at the center of hundreds of integrations (payroll, benefits, ATS, expense, identity, finance, state tax), none of which migrate cleanly. Thousands of hours of muscle memory live inside each instance: the admin who’s run the same performance cycle four years in a row, the payroll manager who’s memorized the seventeen-step merit rollout. Adding a single new cost center can ripple through reporting, integrations, job architecture, comp grades, and benchmarking, and only works if every system and every person is updated in sequence.

• A proprietary configuration layer. Workday isn’t configured the way most enterprise systems are. Integrations are built in Workday Studio, a proprietary tool that takes 6 to 18 months and requires hands-on work from certified consultants. Reports run through Workday’s own BIRT implementation, calculated fields through Workday’s own expression syntax, and tenant configuration through business process and security frameworks unique to the platform. None of these skills have a market outside Workday: no open-source community, and no Stack Overflow, means both the developers and the customers who employ them are locked in by their own résumés.

• A services cartel. More than 10,500 certified consultants work on Workday implementations worldwide across Accenture, Deloitte, Kainos, PwC, KPMG, and 150+ smaller shops. Implementations take 6 to 18 months and cost $300K to $1M+, with implementation fees typically equal to 100% of annual software fees. That services economy is arguably a bigger asset than the product itself. It lobbies on Workday’s behalf, absorbs customer complaints, and gives the biggest firms in the world a vested interest in keeping Workday installed.

• Multi-year contracts. Workday locks customers into multi-year deals. Even a customer who would switch tomorrow is structurally forced to wait.

The combination is why Workday posts some of the best gross revenue retention in enterprise software, and why, for most large companies, it’s one of the stickiest products they own.

Why now

I can hear some people saying: “Sure, challengers have been trying to attack Workday for two decades. But it’s nearly impossible to break in.” And these people are right: the newer entrants have either gone after net-new startups as customers (a strategy pursued by companies like Rippling and Gusto) or competed orthogonally by picking up hard-to-serve markets, like Deel has done.

Meanwhile, Workday hasn’t been sitting idly while AI threatens it. It released more than 25 AI features in 2025 under the Illuminate brand, shipped more than a dozen agents, acquired Sana Labs and Pipedream, and rolled out a consumption-based Flex Credits pricing model that Accenture, Nike, and Merck have signed onto. AI ARR has crossed $400 million and is growing triple digits year over year.

While this seems like real commercial traction, Flex Credits and “AI ARR” are more of a procurement innovation than a deployment one. Signing onto Flex Credits isn’t the same as running core HR workflows through agents in production. Flex Credits exists because both sides need it. Every enterprise CIO and CFO has “AI investment” as a top-line KPI for 2026 and needs to show real spend against it; every legacy vendor has “AI revenue” as the headline question on every earnings call and needs to show real revenue against it. Flex Credits is the procurement structure that gives both sides what they need from the same dollar. The customer commits a credit pool that counts as AI spend in the budget; Workday books the commit as AI ARR; the question of which agent actually runs which workflow gets deferred to whenever Illuminate ships something useful. Both sides leave the room having delivered against their KPI, no one had to commit to a specific deployment, and they still got to go to Ruth’s Chris to celebrate the deal.

You don’t have to take our word for it: there are clear signs this is what’s happening with customers today. A large Workday services partner recently wrote that “most organizations have no idea these capabilities exist, let alone how to activate them.” Customers are already pushing back on paying incremental tokens on top of a subscription they already renewed, and the long-time Workday admins we’ve talked to describe Illuminate as the same manual admin work with a chat surface bolted on. Every Illuminate feature is an additive overlay on the same forms-and-approvals engine, which means AI ARR can grow triple digits while the underlying product stays exactly what it was.

But this moment is different: three things have changed that make Workday finally vulnerable.

First, enterprise IT is finally revisiting its core systems. Large enterprises are running AI-readiness reviews across the systems they thought were locked in for a decade — ITSM, ERP, HCM. The pace of change in the AI stack has turned legacy architectures into liabilities, and a company that wants to be forward-leaning on AI can’t do it on an HRIS built for 2005. When a CHRO and CIO ask “what’s the AI-native version of this?” and the answer from Workday is consumption credits on top of the same engine, that’s an opening.

Second, the tools to actually rebuild this now exist. The same motion is already running one tier up the enterprise stack: companies like Tessera are doing AI-native SAP migrations at Fortune 500 scale, a category an order of magnitude harder than HCM, where a single ECC-to-S/4HANA upgrade can cost $700M and take three years. HCM is the same shape of problem at a smaller surface area. Pair that with a forward-deployed services model owned by the vendor instead of Accenture, and the implementation layer stops being the moat it used to be.

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Third, Workday structurally can’t close the gap from inside. The company is making three specific bets: that Illuminate is the agentic product customers want, that Sana is the new “front door for work,” and that its forthcoming Agent System of Record will be the governance layer for every agent in the enterprise. All three are layered on top of the same forms-and-approvals engine, which is very powerful, but its twenty-year-old, ossified legacy infrastructure that’s hard to configure and modify to accommodate changes a modern HR organization actually needs to make. Bolting AI on top of that engine doesn’t change what’s underneath.

The one real underlying asset, Workday’s trillion-transaction dataset, is thinner than it sounds; what actually matters at runtime is how data connects to workflows, permissions, and integrations, and every layer of that stack is now a liability. Workday can bolt AI on top of it, but it cannot become AI-native without starting over, and starting over is the one thing a public, installed-base company cannot do.

As we’ve discussed the F500 replacement cycle is about to open for the first time in two decades, thanks to a wave of AI-driven replatforming across enterprises. None of the next generation HR solutions were architected to be the F500 HRIS system-of-record. This is a unique opportunity to target the enterprise segment where Workday makes most of its money, and where every previous attempt to displace it has failed.

What an AI-native Workday should look like

We think the opportunity is to go head-to-head with Workday on Human Capital Management (HCM) and build an enterprise-grade AI-native HR system of record, designed for the F500 for the next twenty years.

The product we’d want to fund has six properties:

1. Deploy in one month: coding agents collapse 12-month implementations into 1.

2. Workbench-native: HR teams become analysts and builders, not ticket-writers.

3. Agent-first: employees work with HR inside the tools they already use.

4. Open: customer agents can read the HR data model directly.

5. Secure and permissioned: intuitive per-agent access controls built into the system.

6. Always-on compliance: monitoring agents track the regulatory surface in real time.

Deploy in one month. Implementation is Workday’s biggest weakness and the most significant reason enterprises don’t switch. A real enterprise Workday implementation requires specialists across 50-state US payroll tax, multi-country payroll across 60+ jurisdictions, ACA and SOX controls, benefits carrier quirks, union agreements, Workday Studio, BIRT reporting, and Extend configuration. No one person knows all of it, and most implementations run 12 to 18 months specifically because the knowledge is fragmented across a dozen specialists who have to be sequenced and coordinated. Coding agents collapse the fragmentation. A single agent can ingest the entire tenant (business process definitions, integration definitions, audit logs, payroll runs), reconstruct the rules in plain English, reconcile against live integration feeds, preserve the edge cases, and generate a configuration draft in days rather than quarters. Workday today is configurable within its allowed boxes. An AI-native HRIS should be customizable to the company’s actual policy, with coding agents doing the work a consulting firm used to quote at six figures.

A built-in workbench for HR teams. The best HR admins are product people at heart — they know which cross-system reports the CHRO actually needs, what a comp planning tool should look like, what an onboarding experience should feel like. Today none of that is one job. Pulling information across systems requires a data warehouse and a data team to map it. Building a real workflow or app requires a developer or a Workday Extend contract. The workbench collapses all of it into one agent-native surface: ask a cross-system question and get the answer, describe an app in plain English and have the platform generate a working one, propose a process change and see what it would do. HR teams we’ve talked to are already trying to build apps like this — a manager-onboarding flow that drafts the JD, assembles a 30-60-90 plan, and coordinates with IT on accounts and equipment.

Agent-first. In addition to having a portal, employees should interact with HR through the tools they already use. An employee traveling to Milwaukee should be able to ask in Slack who else from the company is within 50 miles and get the answer in the same thread. A manager approving a vacation request should see the employee’s full context (balance, recent time off, upcoming leave, team coverage) inline with the approval, instead of having to click through to a separate dashboard. Take something more involved: spinning up a new division. Today it’s weeks of work inside Workday: new cost centers, job architecture, headcount plans, benefits setup, payroll integration, approval workflows. In an AI-native system, an HR ops lead should be able to describe it in plain English (500 people in Austin and Dublin, reporting to this EVP, these job families, this comp band) and have the system map every dependency, flag the downstream changes, and generate the configuration and rollout plan in one pass. And the data should flow the other way: HR data should power agentic flows across the rest of the org, not stay trapped inside the HRIS.

Open. Connecting a new payroll vendor is a six-to-twelve-month custom integration through Workday Studio; adding a benefits carrier is a similar build; pulling data into a BI tool runs through a consulting firm. The practitioners we’ve talked to have stopped waiting. They’re building their own Claude MCPs to pull Workday data into the tools they actually work in, standing up custom apps to route approvals through Slack, and treating Workday as a read-only system of record behind an experience they built themselves. The HRIS these teams would build if they had the time is open by default: a data model the customer’s own agents can read directly, APIs that aren’t gated behind credit pools, and a connection layer that treats integrations as a first-class product. The ecosystem pull comes from shipping the best agent builder on top of the data, which is where the work gets done fastest.

Secure and permissioned at the agent layer. HR data is the most sensitive in the company (comp, performance, medical leave, PII), and agents acting on it need fine-grained access controls that are native to the system, not bolted on. A manager’s agent should see their team’s comp; an IC’s should not. An external recruiter agent should see open reqs; it should not see severance history. Getting per-agent permissioning right is the difference between AI that can actually touch production HR data and AI that’s blocked from it by policy, and it’s close to impossible to retrofit into an architecture that wasn’t built for it.

Always-on compliance. The regulatory surface around HR data (EU AI Act, GDPR, data residency) is expanding faster than any single admin can track. Inside Workday, staying current means a senior HR leader reading newsletters and hoping she didn’t miss something. An AI-native stack flips that: an always-on agent monitors for regulatory changes across jurisdictions, flags what needs to change in the tenant, and drafts the config update. That’s hard to retrofit into a 2005 architecture, and natural to build into a 2026 one.

How we’d start

Building all six will take time. Here’s how we would start.

Find a handful of F500 design partners running serious AI-readiness reviews of their HR stack. Lead with a mapping and eventual migration tool to surface the company- and geography-specific rules and edge cases that live inside the tenant, then start automating the human work that piles up around Workday (comp spreadsheets, performance hand-offs, ticket queues) before the full system-of-record swap is on the table.

The commercial architecture matters here. Workday’s multi-year contracts lock the HRIS line item for years at a time, but F500 HR organizations have adjacent budgets that aren’t locked: HR operations, HR technology, transformation, innovation, consulting. A scoped implementation project or an automation subscription can be sold cleanly into one of those, with a real SOW and real procurement, without fighting the Workday renewal on entry. By the time that renewal actually opens, the company is already inside the tenant and delivering value the CHRO can point to. At that point, the question stops being “try an unknown vendor” and becomes “expand the vendor we already trust into the budget we were going to spend anyway.”

It’s also important to consider Workday leveraging scope to crush startup competitors as most F500 Workday tenants are full-platform (HR, Finance, Payroll, Adaptive Planning). The CIO is not going to unstitch their entire tech stack to bet on an HR-only challenger that can’t solve their problems. The best answer is the playbook Workday itself ran against PeopleSoft: start narrow where the leverage is, partner into the adjacent scope where the customer already has a system, and build native where the strategic bet is. A new entrant can treat the customer’s existing Finance and Payroll instances as stable integrations to support on day one, replace the Workday HR modules the customer actually hates (performance, comp planning, reorg, natural-language reporting), and let the platform grow into the remaining scope over time. The sale leads with continuity: payroll keeps running, integrations don’t break, the renewal cycle doesn’t create a gap in how the company is run.

Each piece of this dissolves a layer of defense: agent-native workflows replace twenty years of muscle memory, natural-language configuration retires XpressO, a forward-deployed team routes around the services cartel, and the adjacent-budget wedge neutralizes the multi-year lock.

Don’t expect Workday to go quietly

Workday is already mobilizing. The company has cut more than 2,100 employees across two rounds of layoffs in the past fourteen months and co-founder Aneel Bhusri returned with an explicit mandate to pivot the company toward AI. A $30B company with 10,000+ customers and a services cartel that’s arguably bigger than the product itself will not roll over quietly.

Expect the full playbook. Aggressive bundling with Adaptive Planning, Payroll, and the Finance cloud to make the HRIS renewal feel like a package deal the CFO won’t unwind, steep multi-year discounts on renewals that happen to land in the middle of a challenger’s evaluation, and FUD from the consulting partners who have nine-figure Workday practices to protect. Contractual friction on data portability when customers try to export their tenant, and accelerated M&A where a challenger gets real traction. None of these moves address the underlying architectural problem, but any one of them can slow a design-partner deal by a quarter, and a challenger that hasn’t priced in the fight will burn runway on it. The thesis survives not because Workday won’t fight, but because the architecture they’re fighting with can’t be rebuilt into what F500 customers will actually need.

The opportunity

HR software is one of the last remaining enterprise categories where the incumbent is vulnerable, the architecture needs to be rewritten, and the buyer actively wants an alternative. The global HCM software market is more than $40 billion and growing, and Workday alone was worth close to $80 billion at its peak just two years ago. We think the AI transition creates an even bigger company.

And the stakes are bigger than the opportunity. As companies move to a workforce that’s a mix of humans and agents operating across the same systems, the HRIS becomes the substrate for how those companies are actually run — who reports to whom, who has what permissions, who gets paid, who’s accountable, who’s compliant. Building that on top of a 2005 architecture caps how much AI any company can actually deploy.

Right now, somewhere, an HR admin is copying seventeen comp adjustments from a spreadsheet into a Workday performance cycle, one field at a time, while the business partner watches on Zoom to make sure she doesn’t pick the wrong job code. That work is happening in every Fortune 500 today, on a product that costs the company multiple millions a year. Someone is going to build the next Workday — a system built for agents, not forms and approvals. Once that happens, no one is going back.

If you’re building this, reach out to jschmidt@a16z.com.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

We’re excited to welcome Lisha Li to the a16z Infra team. See her announcement here. -AD

Software ate the world

Obviously, we’re biased, but it’s hard to overestimate just how important technology is to the global economy.

You might even say that software, literally, ate the world:

The top 10 public companies by market cap are larger than the combined GDPs of the G7 (ex-US)--and that would be true, even if one excluded Saudi Aramco, which no one would consider a “tech” company. (Although it was founded in San Francisco!)1

To be fair, the Top 10 list is more “tech and semis [and however one would categorize Tesla and Apple]” than pure-play software, but the point stands: tech isn’t just a big deal, it’s the biggest deal.

And tech’s global takeover has all happened fairly recently:

The top 10 techcos were a small fraction of the G7 (ex-US), until cloud really began to hit its stride in ~16-’17. From that point, it took less than a decade for their combined market cap to eclipse the rest-of-world’s GDP (ex-China).

Tech’s ascendancy isn’t just a changing of the guard, either.

The biggest companies are much bigger than they were, even just 10 years ago:

The combined market cap for the 10 largest companies in the S&P is ~6x larger than it was in 2015, and comprises ~2x larger share of the total index.

To be sure, there was in fact a changing of the guard. The composition of the Top 10 changed-over dramatically, relative to prior decades. By 2025, there were only three holdovers from the previous decade, and only one (Microsoft, a tech company), from the decade before that.

If you were an investor back in 2015, and you were trying to model comparable outcomes for techcos based on the biggest companies in the index, you would have undercounted the upside by a country mile (or 6). Fundamentally, tech “busted the model,” by redefining the outer limit of how large companies could become.

And the outer limit still appears to be moving outwards!

Indeed, tech has become even more central to the global growth story, as of late. Last week, we showed that Tech earnings are expected to grow ~2x faster than the rest of the market. But, if you look back even further, you would notice that tech is contributing an historically large share of the market’s overall earnings growth:

Since 2023, Tech has been responsible for ~60%+ of earnings growth (give-or-take), market-wide.

Other than a brief moment for energy in the early aughts, no other sector has played such a central role in the earnings story (and for quite so long) this century.

At this point, it’s fair to say that tech isn’t just a cycle, it is the cycle.

Railroad GPT

We just told you that tech is an unprecedentedly large deal, but that’s not actually true.

In the industrial era, no sector has ever been quite as big a deal as railroads:

At their peak, railroads comprised ~63% of the US market-cap, which BofA calls the “most dominant innovation sector ever.”

Now, doomers like to point to that railroad chart as a cautionary tale: you see, railroads used to be 63% of the market, but the bubble burst, and now they’re barely a rounding error.

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Well, no, not really. Railroads are still a big deal, but what really happened is that railroads ushered in an entirely new economy that was previously unimaginable (and bigger than railroads ever could have been).

Railroads ceded their dominance to industrials, which eventually ceded their dominance to tech (with a brief interlude for finance and real estate, in the lead-up to the Global Financial Crisis).

And again, while tech is big today, it’s still not nearly as big (relatively) as Transport (or Real Estate and Finance) ever were at their peaks in the 19th Century.

The economy got a lot larger, and a lot more complex. In fact, ~70% of today’s market is in industries that were tiny or nonexistent in 1900.

In 1900, the economy was basically textiles, iron, coal, steel, and tobacco, the rails to transport them, and the banks to finance them. Now those sectors are a tiny fraction of the overall pie.

All of which is to say, the more interesting question isn’t whether such and such platform shift is a bubble, but to consider what new economies might be unlocked by the technological leap forward.

For example, railroads were an incredible General Purpose Technology. One dramatic (but unexpected) change they precipitated was the rise of the modern corporation, as a solution for organizational complexity. Before railroads, a business was usually small enough to fit in one person’s head. But railroads had too many crews, too many stations, and too many simultaneous decisions.

In 1855, the superintendent of the New York and Erie Railroad drew what is now considered the first modern org chart: a layered tree of reporting lines designed to solve the railroads’ increasingly intractable scheduling problems. In many respects, middle management, multi-divisional structure, the professional managerial class, the MBA – all of it – descends from the organizational problems railroads forced into existence.

The point being that railroads didn’t just change what America produced. They changed what the firm was. The railroad-driven emergence of middle management is what Alfred Chandler called the “the visible hand.”

What’s interesting about AI, especially in comparison to railroads, is that AI may once again rewire the dominant organizational template that railroads ushered in over a century ago.

Last month, Jack Dorsey and Block’s leadership published an essay arguing exactly this: the point of AI inside a company isn’t to give everyone a copilot, but to replace what middle management does. Instead of absorb and route information, maintain alignment, pre-compute decisions, etc.—the kind of coordination that management typically is responsible for—in an AI business, humans move to the edges, to focus their judgment on customer contact and human interactions.

In his framing, an 170-year-old model for firm and managerial alignment will be delegated to technology, creating an entirely new type of organization. That seems like a big deal.

Whether Dorsey’s right (and what that new type of firm ultimately emerges) is certainly an open question, but the implications are far more enduring than wondering whether tech will or won’t sell-off its highs, in any given quarter.

Stablecoins volumes are shifting from transfers to payments

When you strip out things like trading, treasury flows, and exchange mechanics — the bulk of stablecoin transactions — you’re left with an estimated $350–550B in payments between different parties last year.

The business-to-business segment dominates stablecoin payments by volume (unsurprisingly, given the scale), but B2C and C2B are growing, as well.

All of which is to say that stablecoins are increasingly playing a role in ordinary commerce. This is part of a broader shift has writing about here.

The Next Decade of News

Americans’ trust in mass media recently hit another new low, in one of the great slow-motion collapses in modern polling.

In 2025, only 28% of Americans said they had a “great deal” or “fair amount” of trust in the mass media (e.g. newspapers, TV, radio). In 1975, it was 72%.

The aggregate trust number doesn’t really tell the story, however.

The real story is in the generational splits, which are, in fact, massive:

The younger you are, the less trust you have in traditional media, and the more trust you have in social media (and the opposite is true, as well—the older you are, the more trust you have in traditional media, and the less trust you have in social media).

Unsurprisingly, not only is there a trust gap between young-and-old, social media-and-legacy media, but there is a consumption gap, as well:

76% of adults under 30 get news from social media at least sometimes. Among the 65-and-older crowd, it’s just 28% (which is slightly lower than it was ~5 years ago).

So, trust in mass media has certainly fallen off its peak, but a good chunk of the story is explained by the evolving media habits of the younger generations. Relative to their elders, the youngs have much less trust in mass media, and are far more avid consumers of social media alternatives.

Just to return to the original observation, the 72% era of peak media trust is typically remembered as a golden age of journalism. Whether it’s remembered as such, it’s nonetheless also true that the early 70s was a period when only a small handful of networks and newspapers dominated the information-mix, with little in the way of competition.

It’s fair to wonder then, how much of that “peak” trust was about great journalism, as opposed to lack of other alternatives. They’re not mutually exclusive of course—it’s possible that the late 60’s and early 70’s had both great journalism and a captured audience, but either way, it’s hard not to notice that the generation with the least trust for mass media, is also the one who’s come of age with the most alternatives.

That’s the argument Martin Gurri makes in The Revolt of the Public: the collapse of information monopolies across every domain – media, government, expertise – revealed authority that had never been fully earned. Once the public could see behind the curtain, trust fell accordingly.

Gurri also makes the argument that the public is better at tearing things down than it is at building things anew. He may well be right, but at the very least, the capital requirements for building new media alternatives have never been lower. Whether they can rebuild trust in news will be the story of the next decade.

See ya later, productivity gains

Zyn sales have entered uncharted territory: negative yoy growth.

On a 4-week rolling basis, sales of Zyn were ever-so-slightly negative yoy, for the first time ever.

The truth is, on a volume basis, Zyn sales are still increasing, but given lots of recent promotional activity, the total dollar value of sales was slightly lower.

Productivity gains, fully intact.

Another interesting aside: Zyn no longer represents the majority of nicotine-pouch sales:

Zyn’s share of the overall market dipped below 50% towards the end of last year.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

We are living in a golden age of AI-powered creativity. In the last two years, the quality of generative models has gone from interesting to extraordinary across image, video, audio, music, and voice. The raw capability to produce professional-quality creative work now exists, and it’s accessible to anyone.

But there’s a problem - the tool landscape has exploded. A marketer producing a short-form video might touch a half-dozen AI products in a single session: one for image generation, another for video, another for voiceover, another for music, plus ChatGPT to write the prompts in the first place. Each tool has its own interface, its own quirks, its own prompting style. The bottleneck to creative production is no longer talent or budget. It’s navigating the chaos.

This is the problem Glif solves, and why we’re excited to be leading their seed round.

Glif is a creative super agent - one AI agent with virtually every model built in. You describe what you want to make, and Glif helps you create it. Think of it as Claude Code for creative work: the same leap from “use a tool” to “direct an agent,” applied to the entire creative stack.

Glif’s method is analogous to Steven Spielberg’s approach to filmmaking. Spielberg doesn’t hold the camera himself. He directs - he focuses on vision, taste, and storytelling, and trusts his crew to handle execution. Glif lets you work the same way: you’re the creative director and the agent handles the tools and technical decisions.

What makes Glif’s depth unusual is a system called Skills: proven creative recipes built from the accumulated knowledge of millions of creative AI runs. The platform comes packed with a growing library out of the box - reliable workflows for short-form video, AI influencer content, infographics, music video lip sync, and the most viral AI-native formats. But you can also teach Glif your own taste, your own style, your own process. Once a skill works, your creative judgment is baked in from that point on.

Glif V2 is built on hard-won knowledge. The first version of Glif was an open AI sandbox where creators could chain models into reusable workflows. Millions of creators built on the platform, and entirely new creative formats were invented there - from miniature documentary videos to viral meme templates. The team took everything they learned and taught it to one agent.

The founding team is unusually well-matched to this moment. Fabian Stelzer studied cognitive science, then spent a decade as CEO of EyeQuant, an AI company using machine learning to predict human visual attention. He then became one of the earliest creators working at the intersection of AI and film, producing SALT - one of the first AI-generated film series, featured on CNN - before anyone had a playbook for it.

Jamie Wilkinson co-founded Know Your Meme, won a Primetime Emmy for the crowdsourced Star Wars Uncut project, and built VHX, a direct-to-fan video platform acquired by Vimeo. Between them, they bring deep AI expertise, a decade of building community-driven creative platforms, and firsthand experience making things with generative tools before most people knew they existed.

They’ve already proven they can build something millions of creators adopt. Now they’re building the product those creators need in the new age of media. We couldn’t be more excited to partner with Fabian, Jamie, and the Glif team.

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For startups, working with the Department of War (DoW) can feel like stepping into a fortress of bureaucracy — procurement is slow, compliance is daunting, and knowing who actually makes buying decisions is like navigating a maze in the dark. Yet, for those who learn to successfully navigate these hurdles, the rewards can be enormous.

The DoW is one of the world’s largest and most stable customers, spending hundreds of billions on modern defense systems and technology annually. Unlike volatile consumer markets, defense contracts offer not just funding, but also long-term sustainment. Winning requires persistence but results in predictable margins on predictable revenue streams. And beyond financial incentives, building technology that strengthens national security carries undeniable appeal in supporting our country and our interests as Americans.

But this market is not for the impatient. Sales cycles take years, compliance is non-negotiable, and understanding who buys what, and through which funding mechanisms, is as important as having a strong product. Startups must approach the DoW with a strategic, long-term mindset. The opportunities are vast, and so are the barriers to entry.

While we have high hopes for DoW’s recent procurement reform, the following is a high-level primer intended to arm you with a rough framework of how to approach selling to the DoW today.

How the DoW buys technology … today

Selling to the DoW isn’t as simple as pitching a product and securing a contract. The military operates on a rigid, multi-year budgeting system — PPBE: Planning, Programming, Budgeting, and Execution — that dictates what gets funded, how, and when. Unlike the fast pace of technology startups, DoW funding moves slowly through layers of approval.

The Future Years Defense Program (FYDP) is a rolling five-year budgeting cycle, updated annually, that outlines the Pentagon’s planned spending across programs, priorities, and force structure, thus shaping long-term defense investments. Each branch submits a Program Objective Memorandum (POM) — a funding wishlist — refined and approved at multiple levels before allocation. But even inclusion in a POM doesn’t guarantee funding; Congress must first approve it through the NDAA and Defense Appropriations bill. Ignoring this political reality can stall progress, and startups that engage early with lawmakers and DoW sponsors have a much better shot at securing long-term funding.

When Congress fails to pass a Defense Appropriation by the last day of the federal fiscal year - September 30th, for the following year, appropriations are said to have lapsed. This means either the federal government shuts down, or Congress passes a resolution to continue funding the executive branch incrementally, at levels equal to the previously passed appropriation. This law — called a Continuing Resolution (CR) — typically has substantial constraints on how the executive branch may use the funds, particularly when it comes to beginning new initiatives that have not been previously appropriated for. CRs are a persistent feature of the funding landscape and have occurred in every budget cycle over the last five years, typically lasting 2–3 months and in some cases as long as 6 months.

Equally important is understanding the “colors of money,” or the different categories of DoW funding.

The first three colors of money are allocated each year in the defense appropriations process, which starts with the FYDP. Research, Development, Test, & Evaluation (RDT&E) supports early-stage research and prototyping, making it the most accessible entry point for startups. But RDT&E itself is divided into eight budget activities (BA), ranging from basic research (BA 1) through advanced development and into system improvement. BA 4 covers prototyping, BA 5 covers system development and demonstration, and BA 6 is largely management support and contracting overhead. BA 7 (also referred to as 6.7) covers operational system improvement and is in fact the single largest budget activity within RDT&E by dollar volume, followed by BA 4 (6.4) and BA 5 (6.5). Startups often receive BA 3 funding (Advanced Technology Development) through programs like Small Business Innovation Research (SBIR). It is worth noting that there is no formal gate between these budget activities. Many programs co-fund work across adjacent BAs simultaneously, and funding streams are often co-mingled rather than strictly sequential.

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Startups often pursue Cooperative Research & Development Agreements (CRADAs) with government customers, as well. By law, CRADAs may not provide funds directly to non-federal entities (see 15 USC 3710a(d)(1)). However, they can offer significant in-kind value such as access to government test ranges and facilities and the sponsorship of classified work — resources that can be enormously useful for early-stage companies building and validating defense-relevant technology. CRADAs can be a great way for startups to jointly pursue R&D with customers and better understand end user requirements.

Collectively, the goal for startups is to progress out of RDT&E funding and move into Procurement, where products are purchased at scale.

Contracting pathways: SBIR / STTR

Unlike the commercial world, where companies sell directly to customers, breaking into the military procurement system requires navigating multiple entry points — each with its own trade-offs, timelines, and levels of control. The key is choosing the right approach based on a startup’s technology, funding needs, and long-term growth strategy.

Two of the most well-worn paths are the SBIR and Small Business Technology Transfer (STTR) programs. These programs provide grants to fund early-stage companies developing high-impact technologies for defense applications.

Phase I grants ($50,000 to $225,000) fund feasibility studies, while Phase II ($750,000 to $1.5 million) supports prototype development. However, the real opportunity is Phase III, where successful startups move beyond small-dollar grants into sole-source government contracts, bypassing competitive bidding and unlocking real procurement dollars. Some companies with strong commercial traction skip Phase I entirely through Direct-to-Phase II (D2P2), which requires proving feasibility and securing a customer memo from a DoW stakeholder.

SBIR awards are often called a “license to hunt,” because Phase I and II contracts don’t guarantee a long-term deal or always provide a path to large scale programs. Rather, SBIR awards grant startups access to DoW stakeholders to demonstrate customer demand signals and open doors to broader adoption. With an SBIR contract in hand, companies can engage directly with program managers who manage acquisition programs and end-users to find pathways into larger procurement programs (more on this later).

Many organizations within and outside the DoW have SBIR/STTR authority, each with distinct focus areas and funding priorities.

• Air Force and Space Force: AFWERX, SpaceWERX

• Army: Army Applications Lab

• Navy and Marines: NavalX, Marine Corps Warfighting Lab

• SOCOM (special operations): SOFWERX

• Cross-service efforts: Office of the Secretary of War (OSW) SBIR program

• Other agencies: DARPA, Department of Energy (DOE), NASA, NSF, DHS

For startups, winning a SBIR doesn’t guarantee success, but it provides the critical first step: credibility, funding, and a direct line to DoW buyers.

Contracting pathways: Additional R&D funding

The Department of War has established additional funding routes and pathways to help with the transition of innovative capabilities from prototype to production. A few key programs to track are:

• Strategic Funding Increase (STRATFI) and Tactical Funding Increase (TACFI) programs: AFWERX funding programs available to companies that have received Phase II SBIR awards. Importantly, these are Air Force-specific programs — there is no direct analog in the Army or Navy — so startups targeting other services will need to identify alternative bridge funding mechanisms. More about this program below.

• Accelerate the Procurement and Fielding of Innovative Technologies (APFIT): A program within the Undersecretary of Defense for Research & Engineering’s office to provide procurement funding to innovative capabilities that have completed prototyping and are ready to transition to operational use. APFIT is suitable for capabilities at high Technology Readiness Levels (TRLs) that DoW services, Combatant Commands (CCMDs), or agencies are interested in procuring.

Contracting pathways: Other Transactions (OTs)

Other Transactions (OTs) — sometimes referred to as Other Transaction Agreements (OTAs) — are another increasingly popular route. The terms “OT” and “OTA” are often used interchangeably in practice, though technically neither is formally defined in law besides by what they are not: contracts, grants, or cooperative agreements. They offer the government greater flexibility to engage with non-traditional vendors and explore innovative contract structures. While the Federal Acquisition Regulation (FAR) ensures standardized procurement processes, its complexity can slow down the adoption of cutting-edge technologies. FAR-based contracts often require lengthy approval cycles, rigid cost structures, and extensive compliance measures, making them challenging for startups and emerging tech companies. OTs provide a streamlined alternative, enabling the DoW to rapidly prototype, test, and transition new technologies while maintaining appropriate oversight and accountability.

For example, the Defense Innovation Unit (DIU) frequently uses OTs with commercial companies, which, if successful, can transition into larger-scale deployments. But while OTs can accelerate adoption, many DoW acquisition organizations still have a preference for FAR based procurement vehicles, though there are now several high-profile examples of production OTs.

Contracting pathways: teaming

Partnering with prime contractors like Lockheed Martin, RTX, Northrop Grumman, General Dynamics, and Boeing can also provide a critical entry point. These firms have established relationships, programs, contract vehicles, and deep institutional knowledge of military procurement — resources that can help startups navigate the complex DoW landscape.

The trade-off? Subcontracting often means less control, lower margins, and longer sales cycles, which can limit a startup’s ability to drive disruptive change. However, primes aren’t just bureaucratic gatekeepers; they are also massive customers in their own right, constantly seeking innovative suppliers to integrate modern technology into their own defense programs. For startups, the key is positioning themselves as indispensable enablers — leveraging primes’ scale while retaining a strategic path to long-term growth.

The most successful startups often combine multiple approaches: securing SBIR funding to build credibility, using an OT contract for rapid prototyping, and partnering with a prime contractor before ultimately scaling into a Portfolio Acquisition Executive’s (PAE) formal program — the ultimate goal for sustained DoW procurement.

The key players in the DoW buying process

Understanding who actually makes purchasing decisions is just as critical as knowing how to secure a contract. The DoW is a sprawling bureaucracy, and startups that fail to engage the right stakeholders risk spending years chasing dead-end conversations. Those that map the ecosystem early can dramatically accelerate their path to procurement.

At the highest level, the Pentagon houses organizations that shape DoW technology adoption:

• The DIU serves as a bridge to commercial tech, funding rapid prototyping with an eye toward service-wide adoption.

• The Chief Digital & AI Office (CDAO) drives investments in artificial intelligence, cloud, and digital transformation.

• USW(R&E) oversees DoW-wide R&D funding. For companies looking beyond early-stage funding, this agency plays a key role in long-term procurement decisions.

These offices all reside in the Office of the Secretary of War. OSW controls a relatively small portion of the R&D budget directly. In fact, the military Services (Army, Navy, Marine Corps, Air Force and Space Force) control over 75% of the R&D dollars. Each military branch runs its own innovation hubs to fund and pilot emerging technologies, shared above as having SBIR/STTR authority.

Within each Service is an Acquisition Executive who is the most senior decision maker for acquisition under the service secretary. The Service Acquisition Executive oversees all the programs in the Service, and the acquisition officials who manage them.

These acquisition officials are critical in achieving scale. They manage the lifecycle of major acquisition programs, overseeing everything from early technology assessments to full-scale deployment. Each branch has multiple acquisition officials often structured around mission areas like aviation, space, ground systems, intelligence, and cybersecurity. These offices report to service acquisition executives and control funding — which determines whether a technology moves beyond experimentation and into mass adoption.

Without a program sponsor, even the most promising technology risks falling into the “Valley of Death,” where innovations stall before reaching full-scale procurement. Startups that engage program officers early and align with an existing or emerging program significantly increase their chances of securing long-term funding. In many cases, working across multiple PAEs helps expand adoption beyond a single branch, ensuring a technology evolves into a multi-service capability.

Finally, Combatant Commands (CCMDs) — such as INDOPACOM, EUCOM, CENTCOM, and CYBERCOM — play a pivotal role in defining mission-critical needs and influencing DoW acquisition priorities. While Service programs control funding and execution, CCMDs generate demand signals, helping justify why certain technologies should receive long-term investment. The most successful startups align with both Service programs and CCMDs, ensuring they have both a clear funding pathway and a defined mission need — the two key ingredients for surviving the DoW acquisition pipeline.

Scaling up

However, as we’ve hopefully made clear, winning an initial DoW contract — whether through SBIR, OT, or a subcontract with a prime — is just the first step. The real challenge is scaling from short-term funding to long-term procurement, ensuring a technology moves from experimental pilots to widespread deployment. Many startups stall in the Valley of Death between R&D funding and full-scale adoption, unable to secure the necessary budget lines for sustained production. Breaking through requires understanding how the DoW funds procurement at scale — and how to navigate the political and bureaucratic forces that shape those decisions.

One of the most effective ways to bridge the funding gap is through Strategic & Tactical Funding Increases (STRATFI/TACFI), programs designed to extend SBIR-backed technologies into larger DoW contracts. STRATFI supports high-priority, long-term initiatives with funding between $3 million and $15 million, while TACFI provides smaller-scale transition funding between $375,000 and $1.7 million. Both require matching funds from either a DoW program office or commercial investors, reinforcing the need for early stakeholder buy-in. However, it’s worth noting that these are Air Force-specific programs, with no direct analog in the Army or Navy; startups targeting other services should plan accordingly.

But true scale requires securing a program — a fully funded, long-term acquisition program written into the DoW budget. Early funding through SBIR Phase III or OT contracts can provide a crucial bridge, but the final hurdle is Congressional support. The DoW budget is ultimately controlled by Congress, and securing inclusion in the NDAA and Defense Appropriations Bill is essential for locking in multi-year funding.

Role of government relations and compliance

Scaling in defense is not just about technology — it’s also about politics. Funding decisions are made at the intersection of DoW priorities and Congressional oversight, meaning startups must engage with both military leadership and lawmakers to ensure their technology remains funded. The House and Senate Armed Services Committees (HASC/SASC) set DoW policy via the NDAA, while the Defense Appropriations Committees (HAC-D/SAC-D) control actual spending. Successful startups don’t just wait for the Pentagon to buy; they actively work with DoW sponsors to influence budget requests and build relationships with Congressional staffers who determine which programs receive funding.

But even with the right strategy, security and compliance can present a major roadblock. FedRAMP (cloud security), CMMC (cybersecurity maturity), and ITAR (export controls) are non-negotiable for defense sales. Startups that neglect these requirements risk losing contracts late in the process or being disqualified entirely. Investing in compliance early — before it becomes a bottleneck — is essential. FedRAMP certification is required for cloud-based solutions; CMMC compliance is mandatory for handling sensitive DoW data; and ITAR restrictions must be considered for any technology with export controls.

The startups that succeed understand how to move from SBIR to procurement, secure Congressional support, and eliminate security roadblocks before they become deal-breakers. Those that don’t? They remain trapped in perpetual pilots, watching as their technology gets shelved while someone else’s product makes it to the battlefield.

Case studies: Startups that have succeeded

The startups that have made it through the bureaucratic gauntlet have done so not just with strong technology, but by mastering the intricate dance of contracting, procurement, and political buy-in. Two companies — Anduril and Palantir — stand out as prime examples of how to navigate the system effectively.

Anduril, best known for its autonomous surveillance and defense systems, leveraged SBIR contracts to bypass the traditional, slow-moving defense acquisition process. Initially focused on Customs and Border Protection (CBP) rather than the military, the company transitioned from small-scale pilot projects to a full-fledged, multi-million-dollar production contract. Today, Anduril has deployed more than 300 autonomous surveillance towers along the U.S. border, proving that non-traditional players can break into federal security markets — not by waiting for the DoW to buy, but by first proving operational value elsewhere in the federal government.

Palantir, a software company long at odds with the defense establishment, achieved a historic win against RTX when it secured the U.S. Army contract to develop and deliver prototypes of the Tactical Intelligence Targeting Access Node (TITAN) ground station system. This contract was awarded after a multi-year competition and made Palantir the first software company selected for a prime agreement. It was awarded through an OT and includes a team of traditional and non-traditional partners like Anduril, who is a subcontractor leading hardware development and scaled manufacturing for the program.

It’s not just Palantir and Anduril that have had big wins in the DoW Market. Shield AI, Skydio, and Applied Intuition have all been awarded OT prototyping contracts through DIU, and companies like Apex Space and Inversion Space have been awarded STRATFI contracts through the Air Force. We’ve also seen exciting moves from Prototype to Production OT Contracts with companies like Vannevar Labs.

Fundamentally, all these companies understand that having the best technology isn’t enough — winning in defense requires navigating the budget process, leveraging flexible contracting mechanisms, aligning with the military’s operational end user needs, and engaging Congress. Their success provides a roadmap for other startups: demonstrate impact, leverage flexible contracting pathways, and find champions within the system who can push a technology into real deployment.

Locked and loaded: final takeaways

The defense market isn’t built for speed, though the DoW is working to adapt to today’s threat environment. Startups that grasp how it operates can unlock massive opportunities—but success depends on mastering DoW funding cycles. Understanding how money flows through Planning, Programming, Budgeting, and Execution (PPBE) and the Program Objective Memorandum (POM) process ensures a startup isn’t just pitching an idea, but securing a place in future budgets. As with any customer, the key is knowing how decisions are made, who holds the purse strings, and how to navigate the system — except, in this case, there are more layers, more steps, and public elections in the mix.

Small Business Innovation Research (SBIR) is an excellent entry point, but it is not an endgame. Startups should treat it as a stepping stone to full-scale procurement, leveraging Phase III SBIR contracts, Strategic Funding Increase (STRATFI) and Tactical Funding Increase (TACFI) funding, or OTs to transition from grants to sustained revenue. The best technologies often die in the Valley of Death — not because they don’t work, but because they fail to secure a formal program or long-term procurement funding.

Security and compliance are not optional hurdles — they are prerequisites. FedRAMP (cloud security), CMMC (cybersecurity standards), and ITAR (export controls) can become roadblocks if not addressed early. Defense sales are high-stakes, and non-compliant vendors will be eliminated before they even get to contract negotiations.

Additionally, relationships matter as much as technology. Winning contracts isn’t just about selling to DoW program managers — it’s about building relationships with PAEs and Capability Program Executives (CPEs) who control funding and aligning with congressional stakeholders who influence appropriations. Without internal DoW advocates and external congressional support, even the most promising startup can find itself excluded from long-term funding cycles.

Above all, startups must be patient and strategic. Selling to the DoW takes time, but a well-executed defense strategy can lead to billion-dollar contracts, sustained revenue, and technological impact at a national scale. The companies that succeed are not just those with the best ideas, but those that understand how to make those ideas indispensable to the U.S. military.

Glossary

There is no shortage of acronyms in the DoW. Here are some frequently used terms — many of which we reference in this piece — and why they matter:

• Accelerate the Procurement and Fielding of Innovative Technologies (APFIT): A program that provides procurement funding for ready-to-field technologies with high TRL levels.

• Broad Agency Announcement (BAA): A competitive solicitation method used by DoW agencies to seek innovative research proposals. BAAs focus on scientific and technical challenges rather than specific product acquisitions.

• Commercial Solutions Opening (CSO): A competitive solicitation method designed for commercial companies, allowing the DoW to acquire innovative solutions with streamlined procurement. Frequently used by the Defense Innovation Unit (DIU).

• Combatant Commands (CCMDs): Geographic or functional military commands (e.g., SOCOM, INDOPACOM, EUCOM) that define mission-critical needs and influence DoW acquisition priorities.

• Cooperative Research & Development Agreements (CRADA): A written agreement between a federal laboratory and a non-federal entity to collaborate on research and development, formalizing government-industry partnerships.

• Cybersecurity Maturity Model Certification (CMMC): A DoW-mandated cybersecurity standard required for handling sensitive defense data.

• Defense Innovation Unit (DIU): A DoW organization that partners with commercial tech companies to rapidly prototype and transition solutions into military use.

• Defense Production Act (DPA) Title III – A government program that funds the expansion of domestic industrial capacity for defense-critical technologies, including semiconductor manufacturing, energy storage, and materials.

• Direct-to-Phase II (D2P2): A fast-track SBIR pathway that allows startups to skip Phase I feasibility studies if they have commercial traction and a DoW customer.

• Federal Acquisition Regulation (FAR): The primary set of rules governing DoW procurement, ensuring transparency and competition but often slowing innovation.

• Federal Risk and Authorization Management Program (FedRAMP): A security framework for cloud-based solutions selling into the DoW.

• Future Years Defense Program (FYDP): A rolling five-year budgeting cycle that outlines the DoW’s planned spending across programs, priorities, and force structure.

• Indefinite Delivery, Indefinite Quantity (IDIQ): A contract vehicle that allows the government to place multiple task or delivery orders over a specified period without re-competing each purchase.

• International Traffic in Arms Regulations (ITAR): Export control laws governing military-related technologies, restricting sales to certain foreign entities.

• Lowest Price Technically Acceptable (LPTA): A contract evaluation method where the government selects the lowest-priced bid that meets the minimum technical requirements. Often a challenge for startups competing with incumbents.

• Middle-Tier Acquisition (MTA): A streamlined acquisition pathway designed to accelerate the development of critical military capabilities outside of traditional procurement timelines. MTAs fall under two categories: Rapid Prototyping (TRL 6+) and Rapid Fielding (TRL 9).

• Multiple Award Contract (MAC): A contract vehicle that allows multiple vendors to compete for individual task orders under an overarching agreement, often used in IT and professional services.

• National Defense Authorization Act (NDAA): The annual bill that sets policy and funding priorities for the DoW, influencing what programs receive Congressional backing.

• Operations & Maintenance (O&M): Funding that supports long-term system maintenance and logistics, typically where mature technologies receive large-scale procurement dollars.

• Other Transaction Authority (OTA): A flexible contracting mechanism that allows the DoW to engage non-traditional vendors without full Federal Acquisition Regulation (FAR) compliance.

• Planning, Programming, Budgeting & Execution (PPB&E): The DoW’s resource allocation process, ensuring funding aligns with strategic priorities.

• Procurement Technical Assistance Center (PTAC): A federally funded resource that helps small businesses understand and navigate DoW procurement processes, including proposal development and compliance requirements.

• Program Objective Memorandum (POM): A branch or agency’s funding proposal, refined and approved at multiple levels before inclusion in the defense budget.

• Rapid Prototyping Fund (RPF): A funding mechanism aimed at accelerating the development of emerging technologies by bypassing traditional acquisition processes.

• Research, Development, Test, & Evaluation (RDT&E): A DoW funding category that supports technology development, often a key entry point for startups.

• Small Business Innovation Research (SBIR) / Small Business Technology Transfer (STTR): DoW programs providing non-dilutive funding for startups developing defense-relevant technologies.

• Strategic & Tactical Funding Increases (STRATFI / TACFI): Programs that extend SBIR-backed technologies into larger DoW contracts, bridging the funding gap before full-scale procurement.

• Technology Readiness Level (TRL): A nine-level scale used by DoW to assess the maturity of a technology, with TRL 1 being basic research and TRL 9 representing full operational deployment.

• Undersecretary of War for Acquisition & Sustainment (USW(A&S)): Oversees DoW-wide acquisitions, contract administration, logistics, and sustainment, and the defense industrial base.

• Undersecretary of War for Research & Engineering (USW(R&E)): Oversees DoW-wide research and development, managing transition programs like APFIT and RDER.

• U.S. Africa Command (USAFRICOM): Oversees military operations across Africa, excluding Egypt, to counter threats and strengthen regional security.

• U.S. Central Command (USCENTCOM): Manages U.S. military operations in the Middle East and Central Asia, including counterterrorism and regional stability efforts.

• U.S. Cyber Command (USCYBERCOM): Leads cyberspace operations, network defense, and offensive cyber capabilities to protect U.S. national security.

• U.S. European Command (USEUCOM): Covers military activities in Europe, parts of Asia, and the Middle East, focusing on NATO partnerships and deterrence.

• U.S. Indo-Pacific Command (USINDOPACOM): Oversees military presence and operations in the Indo-Pacific region, countering strategic threats from China and supporting regional allies.

• U.S. Northern Command (USNORTHCOM): Focuses on homeland defense, disaster response, and security cooperation across North America, including the U.S., Canada, and Mexico.

• U.S. Southern Command (USSOUTHCOM): Directs military operations in Central America, South America, and the Caribbean, addressing narcotics trafficking, security cooperation, and humanitarian efforts.

• U.S. Space Command (USSPACECOM): Conducts operations in, from, and through space to support joint force operations, space security, and satellite protection.

• U.S. Special Operations Command (USSOCOM): Develops and deploys special operations forces for counterterrorism, unconventional warfare, and global crisis response.

• U.S. Strategic Command (USSTRATCOM): Oversees strategic deterrence, nuclear operations, and global strike capabilities, ensuring U.S. military superiority in high-threat environments.

• U.S. Transportation Command (USTRANSCOM): Manages global logistics and transportation of military personnel, equipment, and supplies across all domains.

• Valley of Death: The funding gap where promising defense technologies stall between R&D and full-scale procurement due to lack of budgetary alignment.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.