Some Simple Economics of Open versus Closed AI
If the weights are free, who pays for the next run, and who will keep us safe?
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Today we’re excited to share a piece by guest author Christian Catalini. To view more of his work, subscribe to his Substack. — Danco
In May of 1851, millions of people traveled through the hallways of a glass building to see a preview of the future. The Great Exhibition brought together the best of what the world was tinkering with at the time, including steam hammers, telegraphs, reaping machines, and flush toilets. Anyone could get close to the tech to try to reverse engineer it, and nations sent their brightest engineers to distill as much as possible from their rivals. Its sponsor, Prince Albert, was a strong believer in diffusion being crucial to accelerating economic progress.
A century and a half later, economist Petra Moser turned the exhibition catalogs and almost 15,000 inventions from the fair and its 1876 American sequel into a dataset. Crucially, the inventions came from countries both with and without patent protection. What she found unsettled the staunchest defenders of strong intellectual property rights: patent systems had no effect on the level of innovation, only on where inventors placed their attention. At the time, Switzerland had no patent protection, so innovators crowded around domains such as scientific instruments and food (Nestlé was founded there in 1866) where secrecy, lead time, and complementary assets gave them enough of an advantage. Under strong IP regimes, innovation spread more widely. Same amount of innovation, just allocated differently across fields.
The same tension now sits at the center of the battle between closed- and open-weights AI models. Anthropic and OpenAI argue that “distillation attacks” from Chinese companies threaten both the industry’s ability to finance the next generation of models and US national security. Proponents of open weights counter that diffusion is essential to a competitive and innovative market for machine intelligence.
But Moser’s evidence suggests that the debate is focused on the wrong margin. Open weights are unlikely to change the level of investment in AI, only its direction: what gets built, who builds it, and who captures the returns. Closed labs may keep pushing the most general frontier, while open weights let experimentation spread across the firms and domains that can combine machine intelligence with scarce data, distribution, and tacit knowledge. The same allocation problem shapes safety. The relevant question is not whether openness is dangerous in the abstract, but when diffusion strengthens defenders and where harmful capabilities can actually be constrained.
Competing Visions for the Market for Intelligence
The economic argument against open-weights is simple: distillation is IP theft and erodes incentives to innovate. If the United States government does not step in and use any available tool to stop it, not only will US labs be unable to fund their next large training runs, but China will free-ride on our progress and take over. According to this worldview, open-weights are deeply decelerationist.
The counterargument relies instead on zooming in on how general-purpose technologies historically diffuse through the economy, and concludes not only that open weights are fundamentally pro-competitive and accelerationist, but also that without them, the United States would rapidly fall behind. Openness and experimentation are how we led in the internet era, and this time is no different.
Safety concerns complicate the discussion further. Dario Amodei has been on a multi-year crusade against open-weights on the grounds that they make us incredibly unsafe. He believes that once some threshold of intelligence is crossed, it will be impossible for society to defend itself from bad actors, and rogue models will inflict potentially existential damage. Only by gatekeeping access and imposing guardrails can the country of “geniuses in a datacenter” ever be allowed to exist. But it gets worse: because open weights cannot be “recalled”, we may suddenly find ourselves in a doomsday scenario without much notice or recourse.
Amodei’s detractors point out that without open weights, the market for intelligence would rapidly become extremely concentrated, and that it is rather convenient for Anthropic that its business incentives happen to be perfectly aligned not only with AI safety goals, but also with US national security concerns. Security researchers regularly jailbreak protections on closed models too, and Anthropic and OpenAI’s models have already been used extensively by hackers, including to breach sensitive government infrastructure. While closed models may give us the illusion of safety, they argue that at best they may buy us the illusion of time.
Each side has its own concerns about how the wrong actions today would irreparably get us into trouble. And both sides honestly believe that their approach is the only way to keep us safe (or as safe as realistically possible). Luckily, the economics is loyal to neither.
The Appropriability Regime That Never Was
Anthropic and OpenAI have been targeted extensively by other labs trying to catch up with the frontier. Anthropic went as far as publicly asking Congress to go after Alibaba for what it called brazen and illicit attacks designed to steal its technology. How can the US labs keep funding the necessary training runs if the Chinese labs can, by hook or by crook, replicate within months the performance of their models for cheap?
The challenge with Anthropic’s request is that, beyond fraudulent accounts and systematic abuse of their APIs, the patent-like appropriability regime it seems to desire never existed. Distillation is a legitimate industry practice, and is different from the type of espionage and trade secret theft that US defense, aerospace and chip contractors had to deal with in the past. Chinese labs are not shoplifting the labs’ secret weights; they’re prompting the US models to act as teachers for theirs.
Outputs are not copyrightable, and AI labs typically assign ownership of them to their customers. Terms of service can still prohibit customers from using those outputs to train competing models. But that invites an obvious question: if the technology is so advanced, why can’t the labs use it to stop distillation? The answer is that neither the content nor the source gives the attack away. Each individual request looks like the work of legitimate customers, and an organized operation can scatter its volume across farmed accounts, aggregators, and jurisdictions. Without more onerous frictions for everyone, enforcement becomes a game of banning accounts that are trivially replaced. The trade-off is already palpable with Fable, whose restrictions on assisting with frontier AI R&D frustrated customers enough that Anthropic had to adjust them within days. Anti-fraud controls aggressive enough to make a difference would inevitably affect an even wider range of legitimate work. In the long run, preventing customers from training on outputs they have paid for is a losing business proposition: tighten the restrictions too far, and power users will migrate to open-weights models.
Last, singling out the very technique responsible for a significant share of AI progress over the past decade as illegitimate places the top labs in a very hard spot, as they rely today on extensive fair use arguments for their own distillation of massive amounts of internet material, media, and books.
But if we suspend disbelief, is the fact that top AI models can act as teachers to improve lesser models bad for American AI? If the government could use its soft-power, policy and diplomatic levers to enforce it, would we even want that?
To Monopoly or Not To Monopoly?
Both Richard Nelson in 1959 and Nobel laureate Kenneth Arrow in 1962 struggled through a version of this exact problem. Knowledge is non-rival: my use of an idea doesn’t prevent you from doing the same. Once shared, ideas are also non-excludable. As a result, the social value of an idea exceeds what its inventor can capture, which leads to the canonical worry that some types of ideas will never get properly explored and funded in the first place.
Model weights, at least without any additional software around them, behave a lot like ideas.
For society, it then boils down to a simple, intertemporal tension: once an idea has arrived, society wants it to be spread as widely as possible. After all, its marginal cost, like the cost of downloading a model’s weights, is close to zero. But before it is found, society needs someone to truly believe that they will be able to appropriate significant returns and recoup their R&D costs.
Joseph Schumpeter battled with this conflict his entire life, oscillating between the value of monopoly rents to encourage the initial investments, and the need for startups and creative destruction to undo the monopoly and drive massive growth later on. Ultimately, he concluded the best society can do is somewhat schizophrenic: grant a temporary monopoly, and then let it rip.
Everything since then in the economics of innovation has been essentially an endless debate about how temporary that monopoly should be, and what, if anything, should enforce it. But there’s one important catch that can significantly tip the scales toward open versus closed.
Idea Compounding
Most, if not all, innovation is the result of some form of idea recombination. In domains where cumulativeness and being able to remix past work are particularly important, the length and strength of the rights granted to the first movers have to be carefully weighed against the additional costs of delayed exploration by the followers. The leading AI labs, no matter how well resourced, can only pursue a select number of promising paths, and this also applies to AI safety.
AI has always been a fruit of rapid distillation: today’s marvelous models are as much a gift from decades of academic research on neural nets during “AI winter” as they are from Google’s publication of the transformer architecture. In that light, open-weights models acting as students of the closed models are a direct continuation of that lineage. Model outputs are a key research input for the ecosystem to advance.
The cleanest natural experiment on how restricting access to R&D inputs affects innovation comes from a different race: the one between the for-profit Celera and the publicly funded Human Genome Project to sequence our DNA. When Celera was first to sequence a gene, it restricted access through fees, limits on redistribution, and licensing requirements for commercial use. Heidi Williams found that these genes attracted 20 to 30 percent less follow-on research and product development than comparable genes that had been public from the outset. Although the restrictions were short-lived and disappeared within two years when the public project independently sequenced the same genes, the gap persisted. As late as 2009, Celera-sequenced genes still lagged behind the others.
When the value of follow-on work is high, even small initial frictions compound. Symmetrically, the removal of friction can have big consequences for both the rate and direction of innovation: when looking at biomaterials, Jeff Furman and Scott Stern found a 57 to 135% boost to cumulativeness when inputs were made more easily available. In the context of genetically engineered mice, Fiona Murray, Philippe Aghion, and co-authors studied what happened when the NIH negotiated away DuPont’s restrictions on hundreds of Cre-lox and Onco strains, ending the reach-through royalties and reporting that had limited academic access. Follow-on research rose. More tellingly, it fanned out: new researchers entered, and they explored more novel trajectories. Upstream, the creation of new engineered mice was unaffected.
Overall, whenever the benefits to cumulativeness and broad exploration are high, openness is the dominant strategy. This also means that society benefits the most from openness when uncertainty is still relatively high, as in AI today. When all that is left is execution along a known trajectory, then closed is far less harmful. And even in that narrow case (e.g., a molecule with well-understood clinical benefits that now needs to be commercialized), society only grants a temporary monopoly in exchange for relevant information. Patents themselves are instruments for diffusion and for avoiding trade secrets leading to no disclosure at all.
Now you may wonder if these examples from science-heavy domains also apply to AI. After all, serving models at scale requires massive investments in infrastructure, and that buildout is where most of the risk for the labs lies. But the lesson from telecommunications is exactly the same. In 1956, an antitrust settlement forced AT&T to license, royalty-free, one of the most valuable patent portfolios of all time. As measured by Martin Watzinger and coauthors, once AT&T’s inventions, which included the transistor, were suddenly available for others to build upon, inventive activity rose 17 percent in five years. Interestingly, the effect was not driven by other large firms free-riding on AT&T’s intellectual property, but by new firms going after completely different markets. Bell Labs also doubled down on its core business and was not deterred from innovating: the laser in 1957, the communications satellite in 1962, Unix in 1969.
While the idea of letting others compound on your ideas might be frustrating for Anthropic and OpenAI, the good news is that with a very limited number of exceptions, this is exactly how economic progress works. William Nordhaus found that innovators capture, on average, only ~2.2% of the social surplus they bring to the world. AI will be no exception. But this also places Anthropic’s ask to the US government in context: society does not owe the labs a stronger appropriability regime. As it turns out, there already is a different one in place.
The Innovation Last Mile
When a new general-purpose technology comes about, it initially has to be forcefully retrofitted into the existing system. These initial “point solutions” only realize part of the value of the new paradigm, and it is only when the architecture can be redesigned from first principles that the technology becomes truly transformative.
The rewiring requires control over and adaptation of key complementary assets, including infrastructure, trust, access to distribution, and relationships with regulators. This gives incumbents a second chance: while the new entrants may have caught them by surprise and out-innovated them up to this point, as the dependency on complementary assets becomes the limiting function, they may be able to imitate or acquire, catch-up, and preserve their position.
In the 1980s, Richard Levin and colleagues at Yale asked executives in innovative sectors across America a basic question: what protects your R&D bets? Patents ranked at the bottom. At the top? Learning, secrecy, lead time, and complementary assets. The study was repeated a decade later: same answer. The exceptions? Pharma and chemicals, sectors where a specific molecule can define an entire product class, and narrow exclusivity has teeth. But the vast majority of the economy runs on a limited ability to exclude others from the underlying ideas.
Whenever appropriability is weak, there are still significant profits to be made; they just flow to whoever controls the complements to what is suddenly better, cheaper, faster. A classic example is the Beatles’ music label EMI, which doubled as an electronics firm and patented the very first CT scanner. Its top engineer, Godfrey Hounsfield, collected a Nobel for it. EMI captured none of the value. Within a few years, it was acquired, and the CT market was in the hands of GE and Siemens. GE’s complementary assets? Hospital distribution and services networks, the “last mile” for scanners to actually make it to market.
AI was born on the weak side of the appropriability scale. Despite fierce competition in the race to ASI, research insights regularly leave the confines of the labs, talent rotates between firms continuously as per Silicon Valley tradition, and the models’ APIs inevitably leak valuable information. Across all of these vectors, even draconian countermeasures would only buy the frontier labs a little extra lead time. Moreover, they would miserably backfire given the leverage top talent and large enterprise customers have in this phase.
Of course, not all of a lab’s secret recipe makes it into the public domain. According to Epoch AI, final training runs account only for 10% to 23% of the total compute costs, and the labs are accumulating meaningful tacit knowledge across data collection and curation, infrastructure design and optimizations, and learning from failed experiments and trajectories. But the lesson is the same: if the labs cannot rapidly get ahold of the key complementary assets needed to scale AI in the market, value will accrue elsewhere.
This explains why AI labs have been trying to increase customer lock-in by tightly coupling models and harnesses, and by expanding downstream into the application layer with tools such as Claude Cowork, Claude Tag, and ChatGPT computer use. Not only do these tools collect meaningful traces to replicate human skills, decisions and judgment, but they also amplify the value customers get from committing all their interactions and memories to the same family of products rather than multi-homing. If you can be the entry point for all of a customer’s AI needs, there are many ways you can evolve that experience to become stickier, even if the underlying intelligence is commoditized.
Beyond expansion at the application layer and vertical integration on compute to gain a cost advantage, regulation is probably the most effective way for frontier labs to force a complementary asset into the picture: if, in the name of safety, they can significantly raise the bar for model evaluation and approval, they can make it much harder for others that do not have access to the same well-staffed, all-star policy, regulatory, compliance and safety teams to compete.
It’s a lesson that has worked well in other heavily regulated industries, from financial services to healthcare. It is also what happened in digital platforms after the introduction of supposedly “pro-consumer” EU privacy rules: rather than limiting data collection by the largest players, GDPR had a chilling effect on everyone else that does not have the resources to comply with a labyrinth of rules.
But the possibility of regulatory capture does not settle the safety question. The labs’ claims could be self-serving and correct at the same time. Are open-weights models necessarily riskier than closed ones, or does concentrating control in a handful of players create an even greater systemic risk?
The Safety Last Mile
The United States has a comparative advantage in enabling permissionless experimentation rather than prematurely regulating new technologies or picking winners. But what worked well for the internet may be a terrible fit for AI: what if open weights are simply too dangerous, and strong guardrails and identity verification are the only safe way to harness frontier capabilities?
Stronger controls raise the bar for bad actors seeking to abuse the system. But because the technology is dual-use, those controls inevitably restrict access for a broader set of good actors as well. Depending on how important the rapid diffusion of defensive capabilities is within a particular domain, closed models may not always be safer.
The balance turns on two economic questions: who benefits most from diffusion at the margin, and where can access actually be constrained? In cyber, defense is distributed across a very large number of organizations and devices. Open weights lower the cost of auditing code, patching vulnerabilities, and upgrading legacy infrastructure, while sophisticated attackers may be capable of circumventing model-level controls. Restricting access would therefore tax a wide population of defenders without effectively excluding the most capable adversaries.
In biology, the calculus is likely different. A single dangerous capability may produce irreversible harm that society would struggle to defend against. Delaying diffusion therefore has particular value if the time is used to strengthen screening and control the scarce physical inputs (synthesizers, specialized equipment, and biological materials) needed to turn a model output into physical harm at scale. The lesson from complementary assets applies to security too: when the bottleneck is made of atoms, it offers a far more effective chokepoint against bad actors than software guardrails do.
For truly systemic and existential risks, the balance might tilt even further in favor of openness. When unknown unknowns dominate, the knowledge needed to detect or defuse an attack or major side effect of AI is likely to be distributed rather than concentrated among a small number of AI-lab employees. If so, open weights may foster a much more resilient and diverse AI immune system, one in which defensive capabilities are widely distributed rather than locked inside a few monocultures. Concentrating talent, ideas, and the ability to evaluate and test vulnerabilities within a small population would leave society with few options when problems arise.
Of course, this does not mean that highly capable open-weights models should be released without the same independent third-party testing that closed models should undergo. Staged releases would also allow the ecosystem to learn and adapt as new capabilities emerge. More broadly, safety researchers should invest more in understanding what can be done during the different phases of training to scale back or remove harmful capabilities, rather than relying solely on refusals.
Our current approach relies heavily on guardrails. But if the most realistic starting point is to assume breach, it is important to advance research on alternatives. The choice may not be between openness and safety, but between safety that relies on our ability to contain bits and safety that plans for containment’s failure.
That still leaves the business-strategy question: whatever the merits, can the AI labs capture safety regulation to slow open-weight progress, at home and abroad? Or are the economics of the technology pushing in a different direction?
Own the Weights of Production
A number of enterprises see potential regulatory capture by the leading AI labs as a threat to their business, and have started advocating for “sovereign” approaches to the technology, starting with open-weights not being placed at a regulatory disadvantage. The focus on open-weights is aimed both at reducing vendor lock-in, as enterprise deployments involve a non-trivial amount of custom work to squeeze performance out of a model, and at protecting a company’s most valuable information.
For models to tackle increasingly advanced specialized workflows, more fine-grained and proprietary data is needed. Today, that data is still in the hands of industry leaders, but across engineering, design, finance, accounting, and law, the AI labs are aggressively pursuing creative ways to collect more of it. Some of it is tacit knowledge encoded in the “weights” of the domain experts’ heads; some of it is in long-running systems of record; and some of it hasn’t been measured yet—although enterprises often have the pre-established relations with the right suppliers and clients to collect it.
As a result, while the first wave of conversations between labs and enterprises was centered around collaboration (our tech, your domain expertise), many customers are increasingly feeling that they are one step away from having to compete with their AI provider. Losing control over the “weights of production” is a real concern, especially because platform providers have strong incentives to renege on their promises of neutrality and favor their own products. A good example is the now tense relationship between Anthropic and Figma, where Figma started as a customer, but now has to compete to avoid disintermediation.
The initial enterprise interest in open-weights models was mostly cost-driven, and came as a natural correction to months of employees “tokenmaxxing” their way to greater productivity. By switching prompts to lower-cost, open-weights models, enterprises can shift a meaningful share of their token demand away from more expensive frontier models. AI labs have started fighting this by progressively slashing prices for entry-level models, and by training their models to perform best with their products.
But in the long term, cost is not the reason enterprises should care about openness: control is the critical dimension. Yes, labs promise not to train on inputs and outputs, but the reality is that interactions with their tools still leak valuable information. Furthermore, as tooling evolves and as companies invest significant resources in customizing their AI flows, vendor lock-in only increases. For most market leaders, open-weights are therefore not just a cost escape hatch, but existential. Over the next few years, every firm will need a way to train, optimize, and own the models that support its most strategic operations. If it cannot retain its most tacit knowledge and learnings, it will be disintermediated.
A glimpse of what the future may look like under a more sovereign construct can be seen in a recent collaboration between Thinking Machines and investment firm Bridgewater. For a hedge fund, the alpha is the business, so we would expect Bridgewater to be particularly cautious in selecting an AI architecture. In this case, the two entities collaborated to train a custom model as an extension of Thinking Machines’ base one. The lab brings generalized machine intelligence and AI training tooling, the hedge fund the proprietary insights. This specialization of labor protects Bridgewater and gives it more control over the weights that capture its core intellectual property and tacit knowledge.
From a strategy perspective, while Anthropic and OpenAI are sometimes seen as potential competitors by their own clients, Thinking Machines is taking a value-chain-enhancing approach, in which it only wins if its customers do too. Microsoft and Palantir have made similar bets to become infrastructure and AI tool providers for enterprises that want to keep their machine intelligence within their own perimeter, and many more will follow.
Which approach is a better fit for the market for intelligence? The one that starts with general intelligence built by a few top AI labs and works downstream, or the one that starts bottom-up from the application layer and feeds ideas and improvements back to a multitude of open-weights models? It depends on the shape of the demand for intelligence.
Mapping the Curvature of Intelligence
Any general-purpose technology brings benefits that are so widespread across sectors that it is impossible for any single firm to appropriate them. In theory, this could lead to underinvestment. The more general the AI models of Anthropic and OpenAI, the greater the gap between value creation and capture, which may explain the more recent focus on specialized cyber and life-sciences models. One may worry that without the labs, a number of areas of machine intelligence that are less monetizable would be neglected, as everyone has an incentive to free-ride on base capabilities and build where there’s high willingness to pay.
But this view misses a key consequence of open weights commoditizing yesterday’s frontier capabilities: as costs collapse, a number of applications that would otherwise not be economical, suddenly become viable. This accelerates adoption, and with adoption the number of companies that have a reason to support shared technology inputs also increases. We’ve seen this play out before with open source software like Linux, where companies with complementary business models contribute back to the commons, and it likely explains why NVIDIA was able to bring together a large and diverse coalition in support of open-weights.
This leads to a particular way the market could split. The relevant curve is the payoff to an additional unit of model capability. Across much of the economy, that payoff will diminish quickly: once a model is good enough, further gains matter less than price, reliability, and ease of deployment. Intelligence will behave like a commodity and be priced “at the meter,” like the electrons in our electricity bills. Open-weight models, or closed models run at cost, will serve this part of the market, and firms with advantages in infrastructure or compute will compete on cost. Some labs may even bundle or cross-subsidize commodity tokens to retain customers for higher-margin ones.
But producing value-added tokens will be different. As companies turn into token factories that turn input tokens into more refined versions of them, they will only be able to charge a mark-up if they have access to better ground truth, data, and talent. Those ingredients are needed to expand what is safely automatable and deliver machine intelligence that is more attuned to current conditions. Ultimately, this is exactly where complementary assets will play a key role, and some of the most valuable models will be advanced by the companies that are on the front lines of converting friction with the real world into better tokens. Agentic work only adds value when it can be trusted, and trust requires better verification infrastructure. The only path to more efficient verification is having better ground truth than your competitors. Distribution is, of course, one way to collect that faster than others.
Other domains have the opposite curvature. In cyber, finance, frontier R&D (including AI development) and patentable matter, a small capability advantage can produce disproportionate returns. These convex payoffs can sustain temporary frontier rents because buyers will pay heavily for the best available model rather than a second-best one. Whether those rents accrue to generalist labs or specialized providers depends on who reaches the right data first.
But the highest-payoff domains have a property that many who are long frontier labs miss. In the most convex domains, the marginal buyer is not a user or a company, it’s a country. And when it truly matters, countries don’t need to use the market. We’ve seen a preview of this with the Fable and Mythos bans. As the United States and China race across AI, robotics, quantum, and space, they will do whatever it takes to be ahead. So the steeper the curve, the higher the likelihood that if a lab were to achieve something truly unprecedented, it would be tightly managed or even nationalized. Ironically, for any AI lab to retain its freedom, it needs to be successful, but not too successful.
Which leads to a natural question about AGI/ASI. While many would argue that we already have AGI-like superintelligence across any domain that is verifiable, it is fair to wonder if everything discussed so far does not apply once a lab reaches ASI. If anything, ASI is exactly the reason why the labs are chasing recursive self-improvement over profitable industry verticals. The argument goes that if you solve the intelligence piece first, the rest of the economy will bend the knee to code. But if you believe that distributed, tacit knowledge is still relevant for ASI to be productive, then you quickly realize that the value of complementary assets does not disappear with ASI. If anything, they might become more of a last-mile bottleneck to deployment. Last, if ASI is made of ideas, containing it forever will be impossible. It will leak, the curve will be commoditized, and value will again accrue where there’s remaining friction.
Generalists versus Specialists, and Unknown Unknowns
By now, it is hopefully clear that it is not the level of investment in AI that is challenged by open-weights, only its direction.
A world where open weights dominate sustains progress in every domain where there is at least one company with a complementary business model or assets to guarantee appropriability. Some companies, like NVIDIA, naturally care about any domain that leads to more demand for intelligence. Others care because of how AI lowers costs, improves quality, increases engagement and retention, and so on. An open-model-centric outcome is one where machine intelligence is still very capable, but specialized. In this scenario, depending on how SOTA is achieved and renewed in each domain, generalist AI labs may face the same fate as EMI: it invented the CT scanner, but was unable to monetize it.
On the opposite side of the spectrum, a world where all the progress stays within a few large labs is one where society would benefit from the most general form of intelligence possible. The condition for this to be sustainable is that the labs gain enough of an advantage and lead time at each round to recover their R&D costs and fight commoditization. It is a world where the labs have significant market power, but open-weights keep them in check by making their position at least partially contested.
Depending on what the shape of the demand for machine intelligence actually looks like, both scenarios may continue to co-exist for a while: the closed labs corner the top of the market, the open-weights models serve the bulk of the tokens, and the process repeats with each generation. But if AGI/ASI becomes too cheap to meter, then the last surviving moat will be information that has not been discovered yet, and everything that has not been measured. The race would then evolve into a contest to see who can find friction with the universe and collect that signal first to feed it back to the superintelligence. And that’s a game where many distributed forms of intelligence may have an unfair advantage over a centralized one.
Friedrich Hayek’s insight into the fatal weakness of central planning was not that planners lacked intelligence or compute. It was that no planner, however intelligent, could possess all the relevant knowledge. That knowledge is dispersed, local, often tacit, and continuously changing: “knowledge of the particular circumstances of time and place.” Even a superintelligence would have to venture continually into the world to discover it.
Markets outperform central planning because they transform countless independent encounters with reality into a distributed process of discovery, adaptation, and selection. Many competing superintelligences, each probing a different frontier of the universe with different assumptions and values, will discover more than any single intelligence, however vast. Open weights extend the same logic: they allow intelligence to be adapted everywhere rather than optimized once and rationed from the center. Regulatory capture may slow their diffusion. It can weaken the gradient. It cannot reverse it.
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Interesting read. One nuance I’d add: open weights are not the same thing as open AI. If the training data, filtering, reward models and post-training pipeline remain closed, we can inspect the artifact, but not fully audit how it came to exist. Is that really “open” AI (vs closed AI)?
Here is my take on it:
https://bhaveshsenedhun.substack.com/p/open-weights-are-not-open-source