Hey everyone, welcome back to My Weird Prompts. I am Corn, and I am sitting here on a very sunny Jerusalem afternoon, looking out over the stone walls of the Old City with my brother. It is one of those days where the light is so sharp it feels like you can see every detail of the horizon.
And I am Herman Poppleberry, currently surrounded by three different tablets and a very large cup of black coffee, ready to dive into the geography of intelligence. It is funny, Corn, we are sitting here in Israel, which is its own massive tech hub—especially with the recent breakthroughs in indigenous silicon design we saw last year—but today we are looking across the ocean to the United States.
Exactly. Daniel sent us a fascinating prompt today. He is looking at the geographic footprint of AI development in the United States in this year of twenty twenty-six. He is looking at the traditional giants like San Francisco, wondering how New York City actually stacks up now that the hype has settled into real industry, and then asking about these emerging hubs in places like Houston where the cost of living and specialized industries are creating these new, very specific pockets of innovation.
It is such a timely question for twenty twenty-six. You know, a few years ago, back in twenty twenty-one or twenty twenty-two, everyone thought remote work would just dissolve these hubs entirely. People said, why pay ten thousand dollars a month for a shoebox in the Mission District when you can code from a beach in Bali or a cabin in the Catskills? But here we are in twenty twenty-six, and the physical location of these companies seems almost more important than ever. It is just that the map has become more complex. It is not just one monolith anymore; it is a constellation of specialized nodes.
Right, and it is that gravity of talent. Daniel mentioned in his email that he has never actually been to the West Coast, but he has this image of the coffee shop culture where every third person is a founder and the person next to you is probably sketching out a new neural architecture on a napkin. I want to start there, Herman. Is San Francisco still the undisputed king, or has the crown started to slip as the industry matures?
It is definitely still the engine room, but the nature of that engine has changed. If you look at the sheer concentration of what we call the frontier labs—your OpenAIs, your Anthropic, the core Google DeepMind teams—they are still heavily anchored in the Bay Area. There is this term that started circulating a few years ago called Cerebral Valley, specifically referring to the Hayes Valley neighborhood in San Francisco. By twenty twenty-six, that has only intensified. It is not just about the founders in coffee shops anymore. It is about the specialized infrastructure. We are talking about the proximity to the venture capital on Sand Hill Road, sure, but also the weird, unspoken social network of researchers who all live within five miles of each other.
But why does that matter so much for AI specifically? In traditional software, like building a SaaS product for project management, you could build that from anywhere with a decent internet connection. Is there something about the way AI is developed in twenty twenty-six that requires that physical density?
That is a great question, and I think it comes down to two things: the speed of the feedback loop and the sheer intensity of the hardware requirements. Even though the actual training happens in massive data centers in places like Iowa or Oregon or even the new nuclear-powered clusters in Pennsylvania, the people designing the architectures are constantly iterating. When you are at the edge of what is possible—when you are trying to solve the reasoning bottlenecks we saw in the early versions of GPT-five—the hallway conversations are where the breakthroughs happen. You might be struggling with a specific transformer bottleneck or a synthetic data contamination issue, and you run into someone at a park or a bouldering gym who solved a similar problem three months ago. In a field moving this fast, a one-week delay in information flow can feel like a year of lost progress.
So it is almost like a high-speed particle accelerator, but for ideas. You need the particles to be close together to get the interesting collisions.
Exactly. And let us not forget the talent war. In twenty twenty-six, the most valuable resource on earth is not oil or gold; it is the top one percent of AI researchers. These people are being offered compensation packages that look like professional athlete contracts—we are talking seven-figure base salaries with massive equity. And these people, for better or worse, want to be around other people who are as obsessed as they are. There is a certain prestige to being in the thick of it in San Francisco. It is the place where the history of the next century is being written, and if you are a top-tier engineer, you want to be in the room where it happens.
Okay, so San Francisco is the engine. But Daniel also asked about New York City. Now, New York is obviously a global capital for everything else—finance, media, art—but for a long time, it felt like it was playing catch-up in tech. It was always the bridesmaid to Silicon Valley. How does the New York City AI scene look compared to the Bay Area right now in twenty twenty-six?
New York has finally found its own lane, and it is a very powerful one. While San Francisco is about building the brain—the foundational models, the general intelligence—New York is about applying the brain. Think about what New York owns: finance, media, fashion, and advertising. In twenty twenty-six, we are seeing a massive explosion in what I call vertical AI. These are companies that are not trying to build the next trillion-parameter model. Instead, they are taking existing frontier models and deeply integrating them into the plumbing of Wall Street or the newsrooms of Midtown.
That makes sense. If you are building an AI agent that handles high-frequency trading or complex legal discovery for a massive merger, you probably want to be near the traders and the lawyers.
Precisely. The proximity to the end user is New York's secret weapon. We have seen a huge surge in AI startups focused on fintech and regulatory tech based in Manhattan, particularly around the Flatiron District and Chelsea. Also, the talent pool in New York is more diverse in terms of expertise. In San Francisco, everyone is a technologist. In New York, you have the technologist sitting next to the person who has spent twenty years understanding the nuances of international maritime law or bond yields. That cross-pollination is creating AI products that are much more ready for the real world than some of the more experimental, blue-sky stuff coming out of the West Coast. We are seeing New York become the capital of the Agentic Economy—where AI actually starts doing the work rather than just talking about it.
I also wonder if the lifestyle is a factor. I have heard a lot of developers in their thirties say they are tired of the monoculture of Silicon Valley. They want to live in a city that has more to offer than just tech talk at every dinner party.
Oh, absolutely. That is a huge part of the New York draw. In twenty twenty-six, we are seeing a lot of mid-career engineers moving from the Bay Area to Brooklyn or the West Village. They want the culture, the food, the energy of a city that does not care about their latest deployment. It is a healthier balance for some. And because New York has such a massive existing footprint of Fortune five hundred companies, it is easier for these startups to scale their sales and operations teams. It is a different kind of maturity. San Francisco is the lab; New York is the market.
Let us move to the third part of Daniel's prompt, which I find really fascinating. He mentioned Houston, Texas, and the idea of smaller cities becoming hubs for specific applications. He brought up oil and gas, which is such a specific, industrial use case. Is this a broader trend where we see AI hubs forming around existing industrial clusters?
It is a massive trend, Corn. I would call it the industrialization of AI. Houston is the perfect example. If you are building AI for seismic imaging, or autonomous drilling rigs, or carbon capture optimization, you do not necessarily need to be in San Francisco. In fact, it might be a disadvantage. You need to be where the physical assets and the legacy data are. Houston has the engineers who understand fluid dynamics and thermodynamics. When you combine that domain expertise with modern machine learning, you get something very specialized and very valuable. We are seeing the same thing in Chicago with logistics and supply chain AI, and in Detroit with autonomous manufacturing.
And the cost of living has to be a huge driver, right? Daniel mentioned that specifically. If you are a startup with ten employees, the difference in rent and salary requirements between San Francisco and Houston is enough to hire two or three more engineers.
It is a massive competitive advantage. In twenty twenty-six, venture capital is a bit more disciplined than it was during the boom years of the early twenties. Investors are looking at burn rates. If a company can achieve the same results in Houston or Austin or even Pittsburgh for sixty percent of the cost, that is a much more attractive investment. And for the employees, the dream of owning a house is actually attainable in those cities. That leads to lower turnover and more stable teams. You do not have that mercenary culture where people jump ship every twelve months for a slightly better equity package.
You mentioned Pittsburgh. That is another interesting one because of Carnegie Mellon University, right? They have been a leader in robotics and AI for decades, long before it was the trendy thing to talk about.
Pittsburgh is effectively the capital of physical AI. If it has wheels, wings, or legs and it is controlled by a computer, there is a good chance some of the tech was born in Pittsburgh. We are seeing a lot of autonomous vehicle and drone companies staying there because the testing environment is great and the talent coming out of Carnegie Mellon is world-class. It is a different flavor of AI than the large language models of San Francisco. It is about perception, motion, and interaction with the physical world. In twenty twenty-six, as we move from AI in a box to AI in the world, Pittsburgh's importance is only growing.
It feels like the map is becoming more specialized. Instead of one giant tech hub, we have these specialized nodes. But what about the middle? What about the cities that do not have a massive university like Carnegie Mellon or a specific industry like oil? Are they getting left behind in this twenty twenty-six landscape?
It is harder for them, for sure. To build a hub, you need a catalyst. It is usually a top-tier research university, a legacy of a major tech company, or a very specific industrial base. Look at Atlanta. They have become a huge hub for fintech and logistics AI because of Georgia Tech and the fact that so many Fortune five hundred companies are headquartered there. Or look at Austin, which has successfully positioned itself as the hardware-software bridge. They have the chip designers from the semiconductor industry and the software engineers from the big tech satellite offices. If you do not have one of those catalysts, it is very difficult to compete for that top-tier talent. The middle of the country is seeing a lot of remote workers, but the hubs are still forming around these centers of gravity.
I want to go back to something Daniel mentioned about the United States-centricity of the data and the platforms. He said it can be frustrating for people outside the United States. Why is the United States still so dominant in this space in twenty twenty-six? We see Mistral in France and some big moves in China, but the United States still feels like the center of the universe for AI.
There are a few reasons, and some of them are quite structural. First, the United States has the most mature venture capital ecosystem. The amount of risk capital available for someone with a wild idea is still orders of magnitude higher in the United States than anywhere else. Second, the cloud infrastructure. Amazon Web Services, Microsoft Azure, and Google Cloud are all United States-based. In twenty twenty-six, compute is the new electricity, and the United States owns the biggest power plants. Even if you are a developer in Jerusalem, you are likely renting your compute from a server farm in Virginia or Oregon.
And what about the data? Daniel mentioned that these platforms seem to assume a United States-centric worldview.
That is a really important point. Most of the massive datasets used to train these models were originally scraped from the English-speaking web, with a heavy bias toward United States-based content. This creates a sort of cultural feedback loop. The models understand United States legal systems, United States social norms, and United States idioms better than anything else. If you are a developer in Jerusalem or Paris, you are often building on top of a foundation that was not necessarily designed for your specific context. We are seeing more localized models now—there has been a big push for sovereign AI in Europe and the Middle East—but the frontier models still have that very American flavor.
It is interesting to think about how that affects where companies choose to locate. If you are building an AI that needs to be deeply culturally aware, maybe you have to be in the place that defined that culture. But let us talk about the future. Do you think this geographic stretching will continue, or will we see a reconsolidation as the technology becomes more commoditized?
I think we will see both. We will see the foundational research stay concentrated in a few super-hubs like San Francisco, London, and maybe Beijing. The cost of training a state-of-the-art model is now in the billions of dollars. That requires a level of concentration of capital and talent that only a few places can sustain. But the application layer, the part where AI actually starts doing things in the world, that will be incredibly decentralized. We will see AI hubs for agriculture in the Midwest, AI hubs for maritime logistics in Singapore or Rotterdam, and AI hubs for manufacturing in places like Detroit or Germany.
So the brain is centralized, but the nervous system is everywhere.
That is a great way to put it. And for someone like Daniel, or any of our listeners who are thinking about where to plant their flag, the advice is different depending on what they want to do. If you want to be the person who discovers a new type of neural architecture or works on the alignment problem for superintelligence, you probably need to be in San Francisco or London. But if you want to be the person who uses AI to revolutionize how we manage the power grid or how we diagnose rare diseases, you might be much better off in Houston or Boston.
I think that is a really empowering way to look at it. It is not just about moving to the most expensive city in the world. It is about finding the place where your specific expertise meets the power of this technology. You know, we have talked about this in past episodes, like back in episode four hundred and twelve when we discussed the rise of specialized chips. It is the same pattern. We start with general-purpose tools, and as the technology matures, it branches out into these highly specialized niches.
Exactly. And the geography follows the specialization. I also think we should talk about the role of government and policy in this. By twenty twenty-six, we are seeing different states in the United States taking very different approaches to AI regulation. California has its own set of rules, which are quite stringent regarding safety and transparency. Texas and Florida are positioning themselves as more hands-off, pro-innovation environments. That is going to play a huge role in where companies choose to incorporate.
Right, we have already seen some of that with the crypto industry a few years ago. Companies moving to Wyoming or Puerto Rico for better regulatory clarity. It would make sense for AI to follow a similar path, especially as we get into more sensitive areas like healthcare or autonomous systems.
Definitely. If you are building an AI-driven medical diagnostic tool, you are going to go where the regulatory environment is most predictable and where you have access to the best hospital systems for testing. That might be Boston, with its incredible concentration of biotech and Harvard-affiliated hospitals. Boston is another one of those sleeper AI hubs that people often overlook because it is not as flashy as San Francisco, but in terms of deep tech and life sciences, it is unparalleled. The Kendall Square area in Cambridge probably has more PhDs per square foot than anywhere else on the planet.
It is funny how we always come back to the same few ingredients: a great university, a lot of money, and a specific problem to solve. It seems like the recipe for a tech hub hasn't changed much in fifty years, even if the tech itself is moving at light speed.
It is true. Human nature does not change that fast. We still want to be near the people who can help us succeed. But I do want to touch on one thing Daniel mentioned about being outside the United States and finding it hard to work with United States companies. In twenty twenty-six, we are seeing some interesting shifts there. The demand for talent is so high that United States companies are becoming much more sophisticated about hiring internationally. They have to. There simply are not enough engineers in San Francisco to do everything that needs to be done. We are seeing the rise of the global AI workforce, where the headquarters might be in Palo Alto, but the engineering team is spread across ten different time zones.
So the geography is stretching not just across the United States, but globally. But there is still that gravity pulling everyone toward the United States centers.
Yes, because that is where the decisions are made and where the biggest checks are signed. But we are seeing more distributed teams where the core research might be in Palo Alto, but the engineering team is spread across the world. The challenge for those companies is maintaining that culture and that speed of iteration we talked about earlier. Some are better at it than others. The ones that succeed are the ones that can replicate that high-density feeling in a digital environment, though it is still a work in progress.
I wonder if we will eventually see a truly global AI hub that rivals the United States. Maybe somewhere in Southeast Asia?
It is possible. Singapore is making a massive play for it. They have the capital, the government support, and they are a natural bridge between the West and China. But for now, the United States has such a massive head start in terms of the ecosystem. It is not just the companies; it is the lawyers, the accountants, the recruiters, and the journalists who all know the AI space inside and out. That entire supporting cast is what makes a hub truly resilient. It is very hard to replicate that overnight.
It is like an old-growth forest. You can't just plant a few trees and call it a forest. You need the entire ecosystem of fungi and insects and undergrowth to make it sustainable.
Exactly. And San Francisco is the oldest, densest forest we have in the tech world. It has survived every boom and bust, and every time people count it out, it comes back stronger. But I think the most exciting thing about twenty twenty-six is that you do not have to be in the forest to participate. You can be a specialist in your own little grove in Houston or Pittsburgh or Atlanta and still be doing work that changes the world. The barrier to entry is lower than it has ever been, even if the barrier to the top is higher.
That is a perfect place to start thinking about the practical takeaways for our listeners. If you are a developer or a founder today, how do you navigate this map?
Well, the first thing is to be very honest about what you are building. If you are building foundational AI—the next generation of large models—you really should consider being in a major hub, at least for the early stages. The density of information is just too valuable to give up. But if you are building an application for a specific industry, go to where that industry lives. If you are doing AI for retail, maybe look at Seattle or even Northwest Arkansas near the Walmart headquarters. If you are doing AI for insurance, look at Hartford or Columbus.
And don't underestimate the power of the secondary hubs. Places like Austin, Denver, and Raleigh-Durham have incredible talent pools and a much better quality of life for many people. You can still be part of the national conversation without the San Francisco price tag. We are seeing a lot of people find success by being the big fish in a smaller, more specialized pond.
Absolutely. And for those outside the United States, like us here in Jerusalem, or Daniel, the key is to find those bridges. Whether it is through open-source communities, research collaborations, or just being very active on the platforms where these discussions happen. The world is more connected than ever, even if the physical hubs still matter. You have to be intentional about your geography. Don't just stay where you are because it is comfortable; stay there because it is the best place for what you are trying to achieve.
It is a fascinating tension. We are more digital than ever, yet where we sit physically still defines so much of our opportunity. I think Daniel's prompt really highlighted that paradox. It is twenty twenty-six, and we can talk to anyone in the world instantly, but we still care about who is in the coffee shop down the street. It is about the trust and the serendipity that only happens in person.
It is the human element, Corn. We are still social animals. We want to see the excitement in someone's eyes when we talk about a new idea. We want to feel the energy of a city that is building the future. That is something a video call will never fully replace, no matter how good the resolution is.
Well said, Herman. I think we have covered a lot of ground today, literally and figuratively. From the Hayes Valley coffee shops to the Houston oil fields and the robotics labs of Pittsburgh. It is a big, complicated map, but it is one full of opportunity if you know where to look.
It has been a great exploration. I love looking at the world through this lens. It makes the abstract world of AI feel much more grounded and real when you think about the actual streets and buildings where it is being created.
Definitely. And hey, before we wrap up, I want to say thanks to all of you for listening. We have been doing this for over seven hundred episodes now, and it is your curiosity that keeps us going. If you are enjoying the show, we would really appreciate it if you could leave us a quick review on your podcast app or on Spotify. It really does help other people find the show and join the conversation.
It really does. We read all the feedback, and it helps us shape where we go next. We want to know what you are seeing in your own cities and how AI is changing your local landscape.
And remember, you can always reach out to us. If you have a prompt of your own, or just want to share your thoughts on an episode, you can email us at show at my weird prompts dot com. You can also find our full archive, including our category taxonomy and all the show notes, at my weird prompts dot com. We are available on Spotify, Apple Podcasts, and pretty much everywhere you get your podcasts.
And thanks to Daniel for sending in this prompt. It was a great one to dig into. It really forced us to look at the map in a new way.
Absolutely. Thanks, Daniel. Alright, everyone, that is it for this episode of My Weird Prompts. We will be back soon with another deep dive into the strange and wonderful world of AI and beyond.
Until next time, stay curious.
Goodbye, everyone!
Goodbye!
