Looking around Ai is still reserved for those who have the time capital, who have the basic knowledge. For a chef or a builder on site everyday they don’t have the time to sit down and ask ai chatbot for thoughts before proceed. I would say that ai is yet at the “IPhone moment” yet, where it is so penetrating, that everyone use or at least a version of phone.
At the same time, most industries isn’t ready for Ai, not in terms of not receptive to it, more often than not, they love the idea of Ai, as if it’s the answer to the manual work they been doing. They don’t need Ai, they are still step below it, they need automation.
Institutional memory doesn't show up on any spreadsheet, which is exactly why it gets cut first, you can't defend a line item that has no line. The senior person who prevents disasters nobody sees is invisible in the same way good retention is: their value is the bad thing that didn't happen, and you can't put a number on an absence. So they read as expensive and idle right up until they're gone and everyone discovers what they were quietly holding together.
The iterative development loop in physical AI, from requirement to tested deployment, is at least one order of magnitude more challenging than it is in traditional web software application development.
As a result, there should be special attention and a strong focus on engineering and improving that iterative loop itself, especially in physical AI.
Every engineer should understand this and have it on the top of mind when approaching intelligence in this domain. Stagnation is inevitable otherwise.
Models are commoditizing. Engineering systems aren't. Two teams with the same frontier model will ship wildly different amounts, and the entire delta is how fast their loop absorbs a change — that's the real moat, and it's true well outside physical AI.
It's what we're building leapd.ai on: an agent that runs a founder's daily build-and-ship loop, not a copilot they have to babysit. Every hard problem was the system around the model, never the model. Your Dana result is the same finding at 10,000x the stakes — 20x faster cycles came from turning the loop more times, not from smarter intelligence.
"Agents propose, humans decide" as permanent architecture rather than a temporary concession is the most underrated claim in the piece.
the reason companies default to the dumb cut isn't a lack of knowledge, it's incentives. Bad cuts are legible and immediate, freeze hiring, slash marketing, and the P&L looks better this quarter, to a board watching this quarter. The things worth protecting pay off on a delay nobody gets credit for. Nobody's thanked in Q3 for the churn that didn't happen because they protected retention in Q1. So a rational manager cuts the future to flatter the present, because the future isn't in the number they're judged on yet. "Cut costs" becomes "cut the wrong costs" not because people are dumb but because the smart cut requires a time horizon longer than the person making it usually has. That's the actual disease. It's not judgment, it's whose-quarter-is-it.
The next moat is everything around the model: proprietary context, reliable workflows, distribution, and trust. Better intelligence gets copied; deeply embedded systems are much harder to replace.
The next moat is everything around the model: proprietary context, reliable workflows, distribution, and trust. Better intelligence gets copied; deeply embedded systems are much harder to replace.
Everyone is obsessed with who has the smartest AI model for robots and self-driving cars, but that’s looking at the wrong race.
As a PO, I see the real bottleneck every day in our backlog: testing and validation. When every new model update forces your team to spend months manually digging through camera logs, writing simulation tests, and putting together safety compliance paperwork, your product roadmap grinds to a halt.
The next billion-dollar winners won't be the companies with the best raw AI. They’ll be the ones that build automated testing tools—agents that can run a million simulation tests overnight and clear safety checks instantly. When validation takes minutes instead of months, physical AI can finally move at the speed of software.
The problem with physical robots replacing human workers has been the programming necessary to duplicate human level knowledge and skills.
That problem is now being addressed by installing AI into robots and allowing robots to learn tasks by simply watching, just as humans do. This is the key technology that will make replacement of blue-collar workers a reality sooner rather than later.
The six-month validation cycle stuck with me. By then the model may have changed again. That lag seems like the real problem for physical AI. You can’t ship a truck the same way you ship an app update.
I agree completely and I have thought about that all week! As AI able to do more work , we have to find ways to define what “good” work looks like! That’s the validation!
Twenty years from now, we won’t remember which company had the best world model in 2027. We’ll remember which company had the best LAM - Large Action Model, as I wrote in 1982
Aviation solved the easy half of this decades ago: writing the software is the cheap part, certifying it sets the schedule. A model that improves every few weeks against a validation cycle that takes months just stacks up capability nobody's allowed to ship. My guess is the first physical-AI shop that makes validation continuous instead of episodic wins on tempo, not model quality.
Our biggest mistake is looking at today’s interconnected global crises as separate issues when they are in fact symptoms of one deeply interlocked system. Economic volatility, the climate crisis, cyber attacks, misinformation, broken governance, declining public trust, geopolitic tensions, public health crises, and the exponential pace of technological innovation now feed into each other. A cyberattack could cripple global markets; conflict can drive scarcity in food and energy; environmental ruin can increase public health crises and migration; and information pollution weakens our faith in the systems we need to build to confront challenges. By simply responding to the symptoms-the job losses, the gas prices, the virus, the attacks, the protests, the rising temperatures-and not the underlying system failures that produce them, leaders and decision makers simply export the problem to another sector or to another generation.
This requires a new model of strategic leadership with a bias toward long-term, integrated responses rather than crisis-driven reactions. This should include governments investing in clear and trusted institutions, in resilient infrastructure, education that reflects a world in flux, modern and inclusive public health systems, and governance systems that ensure innovative technologies and advancements are deployed in responsible ways. This requires companies going beyond quarterly expectations and investing in a form of innovation that addresses social, economic, and environmental concerns over the long term. And it involves researchers, engineers, educators, civil society organizations, entrepreneurs, and communities coming together with the shared understanding of the necessary and accelerated progress in AI ethics, Cybersecurity, renewable energy, advanced materials science, and scientific understanding – that will deliver broadly shared benefits, not just individual or sectoral advantages.
Twenty-first-century success will be measured less by military power or gross output and more by a capacity to build and maintain resilient systems capable of weathering disruption, managing technological transitions, conserving our natural world and building public trust. The leaders who will shape the future will be those who embrace every crisis as part of a larger interconnected web of systems that they address collectively and strategically through science, empathy, transparency and well-informed public-policy processes.
The future of the world isn’t under threat by a single crisis; it is being threatened by a failure of imagination and integration in policy and leadership. The solution will not be single, isolated technologies or policies but rather an integrated strategy that connects the pillars of security, economics, environment, technology, education and social cohesion to drive collectively agreed upon outcomes. Perhaps our most important duty as individuals and as societies isn’t to simply solve today’s problems but to build the capacity to prevent tomorrow’s. The future they inherit depends upon our foresight today.
Looking around Ai is still reserved for those who have the time capital, who have the basic knowledge. For a chef or a builder on site everyday they don’t have the time to sit down and ask ai chatbot for thoughts before proceed. I would say that ai is yet at the “IPhone moment” yet, where it is so penetrating, that everyone use or at least a version of phone.
At the same time, most industries isn’t ready for Ai, not in terms of not receptive to it, more often than not, they love the idea of Ai, as if it’s the answer to the manual work they been doing. They don’t need Ai, they are still step below it, they need automation.
Institutional memory doesn't show up on any spreadsheet, which is exactly why it gets cut first, you can't defend a line item that has no line. The senior person who prevents disasters nobody sees is invisible in the same way good retention is: their value is the bad thing that didn't happen, and you can't put a number on an absence. So they read as expensive and idle right up until they're gone and everyone discovers what they were quietly holding together.
Exactly right!!! The importance is only felt when absence occurs!
The iterative development loop in physical AI, from requirement to tested deployment, is at least one order of magnitude more challenging than it is in traditional web software application development.
As a result, there should be special attention and a strong focus on engineering and improving that iterative loop itself, especially in physical AI.
Every engineer should understand this and have it on the top of mind when approaching intelligence in this domain. Stagnation is inevitable otherwise.
Models are commoditizing. Engineering systems aren't. Two teams with the same frontier model will ship wildly different amounts, and the entire delta is how fast their loop absorbs a change — that's the real moat, and it's true well outside physical AI.
It's what we're building leapd.ai on: an agent that runs a founder's daily build-and-ship loop, not a copilot they have to babysit. Every hard problem was the system around the model, never the model. Your Dana result is the same finding at 10,000x the stakes — 20x faster cycles came from turning the loop more times, not from smarter intelligence.
"Agents propose, humans decide" as permanent architecture rather than a temporary concession is the most underrated claim in the piece.
the reason companies default to the dumb cut isn't a lack of knowledge, it's incentives. Bad cuts are legible and immediate, freeze hiring, slash marketing, and the P&L looks better this quarter, to a board watching this quarter. The things worth protecting pay off on a delay nobody gets credit for. Nobody's thanked in Q3 for the churn that didn't happen because they protected retention in Q1. So a rational manager cuts the future to flatter the present, because the future isn't in the number they're judged on yet. "Cut costs" becomes "cut the wrong costs" not because people are dumb but because the smart cut requires a time horizon longer than the person making it usually has. That's the actual disease. It's not judgment, it's whose-quarter-is-it.
EU AI Transparency Rules: The Ultimate Guide to the AI Act, Deepfakes & AI Content Labels
https://nazad1.substack.com/p/eu-ai-transparency-rules-the-ultimate?utm_source=share&utm_medium=android&r=8s4b90
The next moat is everything around the model: proprietary context, reliable workflows, distribution, and trust. Better intelligence gets copied; deeply embedded systems are much harder to replace.
The next moat is everything around the model: proprietary context, reliable workflows, distribution, and trust. Better intelligence gets copied; deeply embedded systems are much harder to replace.
Everyone is obsessed with who has the smartest AI model for robots and self-driving cars, but that’s looking at the wrong race.
As a PO, I see the real bottleneck every day in our backlog: testing and validation. When every new model update forces your team to spend months manually digging through camera logs, writing simulation tests, and putting together safety compliance paperwork, your product roadmap grinds to a halt.
The next billion-dollar winners won't be the companies with the best raw AI. They’ll be the ones that build automated testing tools—agents that can run a million simulation tests overnight and clear safety checks instantly. When validation takes minutes instead of months, physical AI can finally move at the speed of software.
There's not moat in so called “ai” . It's all commodities already
The problem with physical robots replacing human workers has been the programming necessary to duplicate human level knowledge and skills.
That problem is now being addressed by installing AI into robots and allowing robots to learn tasks by simply watching, just as humans do. This is the key technology that will make replacement of blue-collar workers a reality sooner rather than later.
If you need a chatgpt moment for physical AI
The six-month validation cycle stuck with me. By then the model may have changed again. That lag seems like the real problem for physical AI. You can’t ship a truck the same way you ship an app update.
I agree completely and I have thought about that all week! As AI able to do more work , we have to find ways to define what “good” work looks like! That’s the validation!
Twenty years from now, we won’t remember which company had the best world model in 2027. We’ll remember which company had the best LAM - Large Action Model, as I wrote in 1982
Aviation solved the easy half of this decades ago: writing the software is the cheap part, certifying it sets the schedule. A model that improves every few weeks against a validation cycle that takes months just stacks up capability nobody's allowed to ship. My guess is the first physical-AI shop that makes validation continuous instead of episodic wins on tempo, not model quality.
Seems like intelligence gets you into the race. The speed of learning may decide who wins it. 😊
Our biggest mistake is looking at today’s interconnected global crises as separate issues when they are in fact symptoms of one deeply interlocked system. Economic volatility, the climate crisis, cyber attacks, misinformation, broken governance, declining public trust, geopolitic tensions, public health crises, and the exponential pace of technological innovation now feed into each other. A cyberattack could cripple global markets; conflict can drive scarcity in food and energy; environmental ruin can increase public health crises and migration; and information pollution weakens our faith in the systems we need to build to confront challenges. By simply responding to the symptoms-the job losses, the gas prices, the virus, the attacks, the protests, the rising temperatures-and not the underlying system failures that produce them, leaders and decision makers simply export the problem to another sector or to another generation.
This requires a new model of strategic leadership with a bias toward long-term, integrated responses rather than crisis-driven reactions. This should include governments investing in clear and trusted institutions, in resilient infrastructure, education that reflects a world in flux, modern and inclusive public health systems, and governance systems that ensure innovative technologies and advancements are deployed in responsible ways. This requires companies going beyond quarterly expectations and investing in a form of innovation that addresses social, economic, and environmental concerns over the long term. And it involves researchers, engineers, educators, civil society organizations, entrepreneurs, and communities coming together with the shared understanding of the necessary and accelerated progress in AI ethics, Cybersecurity, renewable energy, advanced materials science, and scientific understanding – that will deliver broadly shared benefits, not just individual or sectoral advantages.
Twenty-first-century success will be measured less by military power or gross output and more by a capacity to build and maintain resilient systems capable of weathering disruption, managing technological transitions, conserving our natural world and building public trust. The leaders who will shape the future will be those who embrace every crisis as part of a larger interconnected web of systems that they address collectively and strategically through science, empathy, transparency and well-informed public-policy processes.
The future of the world isn’t under threat by a single crisis; it is being threatened by a failure of imagination and integration in policy and leadership. The solution will not be single, isolated technologies or policies but rather an integrated strategy that connects the pillars of security, economics, environment, technology, education and social cohesion to drive collectively agreed upon outcomes. Perhaps our most important duty as individuals and as societies isn’t to simply solve today’s problems but to build the capacity to prevent tomorrow’s. The future they inherit depends upon our foresight today.