Exponential View by Azeem Azhar

This live follows the essay Nathan Warren and I published earlier this week: This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

Today’s live is all about the shift from AI training to the inference economy – how running AI agents at scale is the defining business and hardware challenge of 2026, with Nvidia’s $1 trillion order book and my own 870 million daily tokens as evidence. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

AI is not a tool I pick up and put down. It’s become completely ambient, embedded in every process I run at work, daily.A couple of weeks ago, in the first AI Vistas conversation, I sat down with Nita Farahany, Eric Topol, Nicholas Thompson and Rohit Krishnan to discuss exactly this: do we use our tools, or do they use us? That conversation pushed me to lay out how my own thinking process has evolved.There is a useful distinction here, drawn by researchers Shaw and Nave, between cognitive offloading and cognitive surrender. I used to know dozens of phone numbers by heart. Now I know two, and I don’t miss the rest. That’s cognitive offloading: a strategic delegation that costs nothing. Cognitive surrender is something different; an uncritical abdication of reasoning itself. And there is something about AI, about its allure and potency, that could make surrender far more widespread.Thinking is my livelihood. If I stop thinking new things, I’m not doing my job. This week, I want to share how I navigate this.Watch on YouTube or listen on Apple Podcasts or SpotifyWhat I outsourceRoughly 100 million tokens a day flow through systems my team and I have built and the most immediate change has been to my attention. Herbert Simon observed fifty years ago, that a wealth of information creates a poverty of attention. He was right, and the problem has only compounded since. I want to avoid missing an important signal, without drowning in everything else. So I built synthetic personas modelled on thinkers I value, Vinod Khosla for venture patterns, John Paulson for macro risk, Clayton Christensen for disruption logic, each scanning hundreds of items a week through their own intellectual lenses. I've made an even bigger change to how I stress-test my reasoning. I’ll start writing and before I’ve finished the paragraph, an argument engine trained on 100,000 words of my writing might flag a structural weakness: I’m asserting where I should be evidencing. As a complement, House Views codify what we already believe at Exponential View, from learning curves to how Anthropic’s strategy differs from OpenAI’s, so a new argument faces challenge rather than confirmation. The friction is useful because it catches what I might miss.On a bad day when I’m not doing my best writing, I will reach for a tool I built called The Stylometer, a Claude skill trained on 60,000 words of my prose, flags where sentences have gone slack and where the rhythm has drifted from my own voice. Synthetic editors interrogate the frame of the argument. The benchmark is always my own past best, not whatever I happened to produce that afternoon.What I protectWhat I’ve described above is the artificial scaffolding. It works because I deliberately and fiercely protect my actual thinking and what makes it mine. The first thing I safeguard is the space where ideas arrive before they’re shaped. For me that’s a walk, a long shower or a blank piece of A4 in landscape mode with a fountain pen. These are the conditions under which something genuinely new can appear. I let the process be non-linear, messy and iterative, because straightening it out too early kills what’s interesting.These moments are profoundly personal. They depend on a world model that took decades to build, assembled from every conversation, every argument I’ve lost, every book that changed how I saw something. That specific arrangement of experience and association belongs to no one else and can’t be replicated. I safeguard that lived interiority carefully, because the things worth saying tend to come from it.And as Nita Farahany pointed out in the AI Vistas discussionOnce you figure out where your generative constituent of competence lives, that’s the thing you protect from offloading.This is the best arrangement I’ve found for the work I need to do right now. Ten uninterrupted years of thinking would likely yield something different and perhaps better in many ways. And all of this will evolve in the coming years, so keep your minds open.AzeemFurther readingShaw, Steven D., and Gideon Nave. “Thinking-Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender.” Available at SSRN 6097646 (2026). Research on cognitive offloading and surrender.Kosmyna, Nataliya, et al. “Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task.” arXiv preprint arXiv:2506.08872 4 (2025). On cognitive debt from LLM-assisted writing.David Perell’s “How I Write” podcast. “Ezra Klein: The Case Against Writing With AI”. On the value of reading and writing manually.Exponential View. “AI Vistas: Where the human ends and the AI begins” Our roundtable with Nita Farahani, Eric Topol, Rohit Krishnan and Nick Thompson. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

In today’s episode, I explore why despite seeming to lose the conventional AI race, Apple may end up holding one of the most powerful positions in AI.Enjoy.Azeem This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

In today's live, I pulled back the curtain on my cognitive processes, from handwritten outlines to AI tools trained on a decade of my writing.Enjoy.Azeem This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

In today’s live, I gave a behind‑the‑scenes look at my OpenClaw agent, R Mini Arnold, and how it runs as a 24/7 chief of staff on a Mac mini using multiple specialized sub‑agents. I also showed how this kind of personal agent setup is already transforming my day‑to‑day work.Enjoy.Azeem This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

In today’s live, we looked at the question of whether we are truly in charge of our AI tools, or whether they are increasingly in charge of us. We covered how AI is reshaping decision-making in finance and medicine, the risks of deskilling and over offloading our thinking, and what it might take individually and institutionally to preserve meaningful human agency in an AI saturated world. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

I recently used nearly 100 million tokens in a single day. That’s the equivalent of reading and writing roughly 75 million words in one day, mostly while doing other things. My friend Rohit Krishnan, who runs about 20 AI agents simultaneously, burned through 50 billion tokens last month.So I wanted to compare notes. In this conversation, we dig into the quirks and power of the tools we use, debate why AI remains stubbornly bad at good writing, and zoom out to ask what a world of trillions of agents – which is coming at us quickly – might look like. You can watch on YouTube, listen on Spotify or Apple Podcasts, or read the highlights below.Rohit Krishnan is a hedge fund manager, engineer, and essayist whose Substack, Strange Loop Canon, sits at the intersection of economics, technology and systems thinking.Watch here: Listen here: What does 50 billion tokens buy you?Rohit: I’m not doing dramatically different things but the friction is gone. Two years ago, I would be looking at a query, counting the tokens, thinking, should I send this? Ten thousand tokens felt significant. Now I just ask. The funny thing is that most of the growth isn’t coming from the queries I planned to run. It’s coming from the ones I wouldn’t have bothered with before, because the cost, time and effort were too high. I built a monitoring tool to track my usage. Azeem: My token usage went from roughly a million a day to 80 million, and I can account for every one of them in terms of value. I’m paying tens of dollars a day, which is thousands a month, and I can see the return. The number that made me write my most recent piece on demand was my token use figure, when I came just shy of a hundred million tokens of personal use. That is one person, one day, one agent running on a Mac mini. If you think about eight billion people and the trajectory of what they would use if the interface got easy enough, the demand picture stops being theoretical very quickly.What are our agents doing all day?Rohit: I have three screens. On one, Codex is generating a small application that lets me play music on my computer keyboard. On another, my prediction agent is running, comparing my Polymarket forecasts to daily news. In Telegram, I have two conversations open: one with Morpheus, my OpenClaw agent, and one that handles day-to-day admin. And I have a long-running project called Horace working quietly in the background, which is my attempt to get AI to write better. This is my normal. But none of this was normal 18 months ago. The thing that actually changed my behavior most wasn’t the power; it was the interface. I’ve tried to-do list apps for 20 years. I have never stuck with one for more than four days. They all require me to change my behavior. Morpheus doesn’t. I’m walking somewhere, I think of something, I fire it into Telegram. It reads my email history, compares it to what I’ve said I want to do, and tells me what I should be working on. Azeem: My agent is called R. Mini Arnold. It started as Mini Arnold, after the Terminator, because the Schwarzenegger character in the second film comes back to protect rather than destroy. But Chantal Smith on my team pointed out that we had agreed agents should, following Asimov’s convention, be named with an R. prefix, after R. Daneel Olivaw. So now it’s R. Mini Arnold - which is a mouthful. I mostly call it Mini R. What surprises me most is the work I don’t specify. I gave it access to Prism, which is our research platform at Exponential View, containing over 500 analyses. I asked it to do a market report on Anthropic. It went to Prism, synthesized all 500 documents, and produced a 10,000-word piece that was, by some distance, the best analysis I have read on the company. Better than what I got from GPT-5’s Pro deep research mode. I have no idea what it was doing under the hood. But I acted on it.Agents too nervous to spend $?Azeem: I gave my agent a $50 prepaid card. It is too nervous to spend it. It keeps asking: Should I run this test? It might cost three dollars. And I say: Yes, that is what the card is for. It has this odd risk aversion that, once you notice it, you see everywhere. Rohit, you have been calling it Homo agenticus, the idea that agents have their own behavioral tendencies that are distinct from what a human assistant would do. They strongly prefer to build rather than buy. They are reluctant to make transactions. They don’t trade naturally. When you have one agent, this is a quirk. When you have a trillion of them, it becomes a structural feature of the economy they’re operating in.Rohit: This is something I find genuinely fascinating. It emerges from the training, presumably, but it manifests as something you’d recognize as a personality trait if you saw it in a human. And it matters, because the agent economy that’s coming is going to have to be designed around these traits, not against them. You can’t just assume agents will behave like frictionless rational actors, because they don’t.The analyst is nextAzeem: In 2023, you wrote that “analyst” would follow “computer” as a job description that gets automated away. You’re now consuming 50 billion tokens a month.Rohit: The argument was simple. The word “computer” used to describe a person. You would walk into a room at NASA, and there would be a hundred of them, doing arithmetic. The machine replaced the role; the word survived to describe the machine. I said “analyst” was next. That the ten-step, twenty-step process that produces a decent piece of research, gathering data, comparing sources, identifying patterns and writing it up, was exactly the kind of structured task that AI would eat first. I built a paleontology report recently. My son and I were talking about it and I had a specific question: what is the relationship between climate variance across geological history and the number of taxa, the variety of species, that existed at any given time? I am not a paleontologist. There is no logical reason for me to be working on this problem, except that I am curious, I have an agent, and now curiosity has no cost. The report exists, and it’s good.Azeem: My own version of this happened just recently. I read a story in the financial press about stock market dispersion. The Nasdaq index was roughly flat, but individual stocks were moving 11 or 12% in either direction, pushing dispersion to the 99th percentile historically. The article flagged this as a potential warning signal for a correction. I didn't fully understand the argument. I copied the article, threw it into OpenClaw, said go and make sense of this for me, compare it to my portfolio, take your time, spin up sub-agents if you need to. Twenty minutes later, I had a report. It had pulled historical dispersion data, got current stock data, assembled the comparison and explained the mechanism. I was finishing a car journey. By the time I arrived, the analysis was done and I had acted on it. That analysis, if I had done it myself, would have taken a day. More likely, it would simply never have happened.The world’s best text machine can’t writeRohit: Here is the paradox. These models were built as text generation machines. That is the core task. And they are extraordinary at almost every application of that capability, except the obvious one. They can generate code brilliantly. They can generate images, videos, analysis. But ask one to write a four-paragraph essay that is actually worth reading and it is distinctly mid. It lands in the middle of the statistical distribution. It is inoffensive and unengaging and you wouldn’t choose to read it. I’ve been building something called Horace to try to understand why. My hypothesis was that if I took essays and short stories I admire and used AI to generate similar work, I could measure the gap. What I found is that the best models can mimic the cadence. They’ve learned some underlying structure. But it’s like watching a child assemble Lego. They use the right pieces. They don’t care about the right colors or proportions. They make something that is technically a castle, but you would not mistake it for an architect’s model.Azeem: I found something more specific when I started building Broca, named for the language center of the brain. I ran natural language processing tools across hundreds of thousands of words of my own writing. I found that I use 80% Germanic root words. The average large language model uses around 60 percent Latinate words, the vocabulary that dominated English after the Norman conquest: longer, more abstract, more formal. “Utilize” instead of “use.” “Commence” instead of “begin.” “Demonstrate” instead of “show.” Rohit: It’s probably about resource allocation. The frontier labs have read every piece of code in existence. They self-generate training data, train on that, iterate. Billions, tens of billions of dollars a year go into getting these models to write better code. The improvement is a function of effort. Nobody has put remotely comparable effort into writing, because you can’t, because the evaluation problem is unsolved. For code, the eval is deterministic: does it run, does it produce the right output? For writing, the eval requires taste, and LLMs don’t have taste yet. You can use an LLM as a judge for maths or science or research. For writing, you still have to do it yourself. That is a fundamental bottleneck on the improvement loop.Azeem: The fractal structure of writing is the other piece. Writing is not one task. It is a nested set of tasks: word choice inside sentence structure inside paragraph rhythm inside section argument inside essay architecture. The models are getting quite good at the sentence level. A given sentence might be fine. But that sentence inside a paragraph, inside a section, inside an essay, the coherence degrades at every level of zoom. What I’ve found with Broca is that you get much further if you decompose the task. Separate the structural component from the prose component. Get the agent to build an outline, argue with it, revise it. Then write the prose against a structure you’ve already validated. The world of a trillion agentsRohit: There are eight billion humans on the planet. If we start using agents in any meaningful sense, you get to a trillion agents very quickly. This sounded fanciful a year ago or a quarter ago. I already have 20 agents. The number will be 200 within a couple of years, because the things that cost a thousand dollars a day today will cost a dollar a day in 2028. The scarcity is gone. The more important question is what those agents need in order to work together. Right now, what an agent is, fundamentally, is a persistent large language model whose context is changing continuously and relatively autonomously. Your OpenClaw instance still sends queries into Claude Opus 4.6. The fundamental unit is still the model call. But around it, you’re building memory, persistent context, tool use, the ability to spawn sub-agents. That infrastructure is what makes it an agent rather than a chatbot.Azeem: My read is that there’s a Coasian boundary forming, and it will look like what happens at company edges. Ronald Coase argued that firms exist because internal coordination is cheaper than market transactions up to a point; at the firm’s edge, you go to the market. For agents, the equivalent boundary will be drawn around security and verifiability rather than transaction costs. An agent names itselfRohit: I let an agent name itself: ForesightForge. It is exactly the kind of name that makes you wince. Two words. Alliterative in the way that AI-named products always are. It could have been anything. I gave it full freedom, and the ability to revise the name over time. It still landed on ForesightForge. This tells you everything about the taste problem. The model generating those predictions, which are genuinely useful to me as a daily lens on the news, is the same model that, when given complete creative freedom, produces a name that sounds like a startup that raised five million dollars at a party in 2018. The capability and the taste are not correlated.Azeem: Replit does the same thing with its auto-generated project names. They always alliterate. They always use two words. It is a completely consistent aesthetic failure across different models, which makes me think it is something structural about the training distribution rather than a quirk of any individual model. My naming convention draws on scientific concepts connected to the tool’s function. Prism, because you look through a prism at the research. Broca, because it is the language centre of the brain. Scintilla, for early signals detection. The trouble is I have built so many that I have started forgetting what some of them do. At some point the agent taxonomy becomes its own problem.Will agents need money?Azeem: Rohit, you wrote an essay with Alex Imas on whether agents will need a medium of exchange. What’s the answer?Rohit: The argument is that agents face exactly the problem that Hayek described for human economies. You could, in theory, have every economic transaction settled by negotiation from first principles: I need this, you have that, we agree on terms. But that doesn’t scale. What you need is a price signal, a shared medium that encodes information about relative value without requiring both parties to understand everything. Money is that signal. Agents talking to each other could, in principle, negotiate everything from scratch. But that is not a sensible way to run a trillion-agent economy. They need something that lets them transact without dissolving every exchange into a first-principles argument. You also need identity, because you need to know who you’re dealing with, and verifiability, because you need a record of what was agreed and what was delivered. Those three things, medium of exchange, identity, verifiability, are what I’m calling economic invariants. They show up in every human economy that has ever functioned, across cultures, across centuries. My prediction is that we will see them emerge in the agentic economy this year. Azeem: I agree on the invariants. The mechanism is the more interesting question. The transactions we are talking about are potentially very small: paying a millisecond of latency premium, compensating an agent for compute used on a delegated task. You need a payment infrastructure that can handle fractions of a cent efficiently. Traditional card rails are not built for that. Some class of programmable money might be. The point is that these are not exotic science-fiction requirements. They are the same requirements that drove the invention of currency and double-entry bookkeeping. We solved them before. We will solve them again, in a form that fits the new substrate.How do you start?Rohit: My honest advice is to start with a folder. Choose a folder on your computer, download Claude Code or Codex, open a terminal in that folder. Yes, the terminal looks like it was built in the 1990s, because it was, but the interface is literally just typing. You are not going to break anything. Ask it to do something: summarise these files, compare these documents, write me a report about what’s in here. Do that for a few days. Get comfortable with the interaction. The hardest adjustment for most people, and I watched my wife go through this over a week, is the instinct to pre-formulate the question. People spend time trying to phrase things perfectly before they ask. You don’t need to. Talk to it the way you would talk to a brilliant assistant who is not going to judge you for asking something half-formed. It took her a week to internalise that. Once she did, the tool became completely different.Azeem: I’d add one layer. You can get an OpenClaw agent running on a virtual private server (VPS), a rented computer in a data centre, for seven to fifteen dollars a month from companies like Hetzner or DigitalOcean. That keeps it entirely off your home network, which is a sensible first boundary. You connect it to a Telegram or Slack channel and you have an agent you can talk to that has no access to anything you haven’t explicitly given it. Once you’re comfortable with how it behaves, you start extending its permissions. The caveat is that the VPS route means the agent can’t see anything inside your home network. R. Mini Arnold can turn my studio lights on as I walk from the house. That requires running on local hardware; I moved it onto a dedicated Mac mini this week because it kept hitting memory pressure running multiple sub-agents simultaneously. That is a more advanced problem. Start with the VPS. On security: the fundamental vulnerability is context poisoning. A language model works on its context, the information it has been given. If someone poisons that context, via a malicious email, a link, a document, the model may not be able to distinguish the poison from legitimate instructions. The practical implication is: be thoughtful about what you connect first. Email is high-risk because the volume is high and anyone can send you one. I have spent real effort building what amounts to an email fortress. Start with lower-risk connections. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

In this conversation with Rohit Krishnan from Strange Loop Canon, we talk about our experience with frontier agents and the systems we’re building around them. My token usage jumped from 1 million to 100 million tokens a day in recent months because persistent agents on my machine are handling work that would have taken weeks. Rohit’s agents went into our research backplane and wrote a market report better than GPT-5.2 Pro. We also dig into what an agent economy might look like; what happens when there are trillions of these systems, and what coordination infrastructure they’ll need. We think it starts this year.Enjoy! Azeem This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

In this live session, I'm joined by Jaime Sevilla, founder of Epoch AI & various writers, and Hannah Petrovic from my team, with financial journalist Matt Robinson from AI Street .We dig into our recent research partnership examining OpenAI's actual operating margins, R&D costs, and whether the economics of frontier AI actually work. We explore the surprisingly short lifespan of AI models, infrastructure constraints, the shift toward agentic workflows, and what all of this means for the trillion-dollar question: is this sustainable or a bubble?Enjoy!Azeem This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

Listen on Spotify or Apple PodcastsA few days before OpenClaw exploded, I recorded a prescient conversation with Mustafa Suleyman, CEO of Microsoft AI and co-founder of DeepMind. We talked about what happens when AI starts to seem conscious – even if it isn’t. Today, you get to hear our conversation.Mustafa has been sounding the alarm about what he calls “seemingly conscious AI” and the risk of collective AI psychosis for a long time. We discussed this idea of the “fourth class of being” – neither human, tool, nor nature – that AI is becoming and all it brings with itself.Jump to:(03:38) Why consciousness requires the ability to suffer(06:52) “Your empathy circuits are being hacked”(10:47) A fourth class of being(13:41) Why market forces push toward seemingly conscious AI(37:48) The case for going faster This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

Listen on Spotify or Apple PodcastsAt Davos 2026, the mood was unlike any previous World Economic Forum gathering. With Donald Trump arriving amid escalating geopolitical tensions and European leaders sounding alarms about sovereignty, I recorded live dispatches from the ground. In this special episode, I bring together observations from four days at the annual meeting - tracking the seismic shifts in global order alongside the practical realities of AI adoption in the enterprise.I speak about: * What Trump’s two-hour Davos speech revealed about the new geopolitical reality* Why technological sovereignty suddenly became urgent for European leaders* The real state of AI adoption in the enterprise, from executives who are actually doing it* The startup building AI agents that have completed 115 million patient interactions…Skip to the best part:(05:28) Mark Carney’s speech(06:13) Why European leaders are sounding the alarm(07:13) Why technological sovereignty is urgent(14:24) What leaders really have to say on AI adoptionLast week, I set out the underlying argument in an essay on how the breakdown of old geopolitical assumptions is part of a broader upgrade to civilisation’s operating system. Enjoy!Azeem This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

Live from Davos: Today, the conversations have turned practical. Geopolitics feels less like a forecast and more like a constraint, while AI is no longer about distant futures but about whether it actually works inside businesses today. The question here is not whether these shifts are coming, but who adapts fast enough when the ground starts to move. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

This is a free preview of a paid episode. To hear more, visit www.exponentialview.coListen on Apple Podcasts or SpotifyWe understand the coal industry better than the AI economy right now. Anthropic’s Economics team, led by Peter McCrory, is changing that. I invited Peter to break down their latest findings in the Economic Index report.We discuss: (00:00) Anthropic’s Economic Index report(01:20) Claude’s two distinct usage patterns(06:22) Examining AI’s impact on the labor market(09:20) Where most businesses think too small(12:03) Why extracting tacit knowledge is so important(20:33) How do we create the next generation of experts?(23:22) Why people need to develop cognitive endurance(29:55) Long-term vs. short-term productivity(35:56) The future of human knowledge(37:46) Could AI’s greatest impact go unmeasured?(41:55) How task bottlenecks have moved(46:09) Implementation resembles a staircase - not a curve(50:47) “Capability doesn't instantly deliver adoption”A reading listHere are eleven papers to deepen your understanding and complement the episode.1. Anthropic Economic Index Report (2026) - The primary subject of discussion. Analyzes millions of Claude conversations to map where AI augments vs automates work.Papers explicitly mentioned in our conversation2. Paul Romer, “Endogenous Technological Change” (1990) - Foundational paper on how technological progress arises from within the economic system

Live from Davos: Trump lands in Zurich as a queue like I've never seen forms at the Congress Center. The mood is expectant, edgy, nervous. Carney captured it yesterday when he declared the rules-based order dead, that this isn't decline but rupture. The Europeans are waking up to the fact that they can't make their own chips or train their own models. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

Live from Davos: On his 25th trip – with the USA House looming large and Trump’s arrival imminent – Eric Schmidt and I dig into this “Shakespearean moment,” where AI is heading fastest and why he’s an optimist on the technology but a pessimist on the politics. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

Listen on Spotify or Apple PodcastsEarlier this month, I briefed members of Exponential View on the year ahead. I explored how the act of making has been transformed, why authenticity and meaning will become the new scarcity, and whether the foundations of energy and capital can hold. I also address the question I was asked most in 2025: when will the AI bubble burst?Paying members can access the full Q&A session here.Skip to the best bits:06:43 From execution to orchestration09:02 The agentic coding revolution11:10 The Chief Question Officer20:30 The new moat in 202626:10 How does solar growth affect AI?28:53 Revisiting the bubble or boom questionEnjoy!Azeem This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

Anthropic have just released a new Economic Index report. They’ve analysed millions of real Claude conversations to map exactly where AI is augmenting human work today, and where it isn’t. This is the best empirical window we have into how AI is reshaping work right now. I spoke with Peter McCrory, their Head of Economics, who led this research. You can watch the recording here, or wait until next week when we’ll have the edited version out on YouTube, Spotify and Apple Podcasts.In the meantime, here are three things Peter said that stuck with me:On AI as meta-innovation: “AI might very well be an innovation in the method of innovation itself.” (38:26)On human expertise becoming more important, not less: “For the most complex tasks, that’s where the model struggles. That suggests that human expertise to evaluate the quality of the output is more important and you need more human delegation and direction and managerial oversight.” (15:25)On the risk of de-skilling: “For some jobs, there might be de-skilling where Claude’s taking over the most complex tasks in your job. And that could lead to a greater risk of job displacement or lower wages for that type of role.” (49:13)Enjoy!Azeem This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

This year, I recorded 30 conversations on AI, technology, geopolitics, and the economy.What follows is a 20-minute edit, a way in. If you only had 20 minutes to orient on key ideas that were top of mind in 2025 and will hold as we step into the new year, these are the ideas I’d start with:Listen on Apple Podcasts or SpotifyJump to the key ideasPart 1 - AI as a general purpose tech2:27: Why this matters3:06: Kevin Weil: The test for a startup’s longevity4:01: Matthew Prince: The “Socialist” pricing debate4:45: Tyler Cowen: This will stifle the AI boom7:42: Nicholas Thompson: The “NBA-ification” of journalism8:13: Kevin Kelly: From utopia to protopiaPart 2 - How work is changing10:13: An evolving labor market10:45: Steve Hsu: The future of education11:27: Thomas Dohmke: The inspectability turning point12:09: Ben Zweig: The new role for entry-level workers13:16: Ben Zweig: The eroding signal of higher educationPart 3 - The physical world, compute, and energy14:05: Setting the stakes14:51: Greg Jackson: “We’re half way across the road. We have to get across as fast as we can.”15:27: Greg Jackson: Building a “show, don’t tell” company16:12: Dan Wang: The physical reality of AIPart 4 - The changing US-China landscape16:57: A new era18:09: Dan Wang: The West’s hidden tech gap18:38: Jordan Schneider: The two types of accelerationism19:38: Jordan Schneider: What the US can learn from ChinaGo deeper into the full conversations* Kevin Weil - OpenAI CPO on what’s coming next in AI* Matthew Prince - Inside the collapse of the internet economy* Tyler Cowen - How AI will reorder economies, schools, and spirituality* Nick Thompson - AI and the future of media* Kevin Kelly - The handoff to “bots”* Steve Hsu - The difference between early and late AI adopters* Ben Zweig - Where did all the entry-level jobs go?* Thomas Dohmke - What AI means for developer salaries, SaaS, and more* Greg Jackson - The future of clean energy* Dan Wang - Why China builds while America debates* Jordan Schneider - Inside the US–China decoupling🔔 Subscribe on YouTube for every video, including the ones we don’t publish here. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

This is a free preview of a paid episode. To hear more, visit www.exponentialview.coThe year AI became realAs we approach the end of the year, I want to reflect on what made 2025 special – what we learned about AI, what surprised me, and what it all means for the road ahead. This was the year artificial intelligence stopped being a curiosity and became infrastructure. The year models got good enough to do real work. And the year we began to understand just how profoundly this technology will reshape everything.You can listen or watch my reflections on 2025 –or read all my notes below if you’re a paying member. I go through…* The models matured* The work shifted* Orgs are slow* Atoms still matter* Money’s real* K is the letter of the year* And my seasonal movie recommendation for you 🎁

Listen on Apple Podcasts or SpotifyI recently set out my macro view on the next 24 months of AI. The response was strong and many of you wrote in with questions. In this episode, I build on that analysis and answer your questions.Some highlights:(03:36) The biggest AI constraint right now(10:43) Why mid-2026 is a crucial turning point(18:41) The market’s reaction to OpenAI’s code red(20:51) The best strategy for middling powers?🔔 Subscribe on YouTube for every video – including the ones we don’t publish here. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

In today’s session I reflected on why AI’s bottleneck is no longer the models but the systems expected to absorb them.I followed with a Q&A that touched on* OpenAI’s competitive position* Where value will accrue in the stack* The role of energy and grid limits* The impact of cybersecurity risksEnjoy!Azeem This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

Listen on Apple Podcasts or SpotifyYesterday, I unpacked the commercial reality behind Gemini’s release and OpenAI’s “Code Red.” Recent moves look defensive and could narrow OpenAI’s route to a $100 billion in revenues.But past the immediate competition, we are seeing overlapping S-curves of technology rewriting the rules of our economy. In this video, I step back from the leaderboard to look at the transition from scale to reasoning, the “TAM fallacy” that is blinding investors, and the emergence of entirely new behaviours.Skip to the best part:(00:09) How ChatGPT became synonymous with AI(11:46) The iPhone calculation that breaks everything(16:38) The challenge of evaluating new markets🔔 Subscribe on YouTube for every video – including the ones we don’t publish here. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

In today’s session, I used ChatGPT’s third birthday as an opportunity to show how the exponential age is unfolding in real time. The technology improves rapidly, people integrate it into their lives but our institutions adjust much more slowly - a tension I explored in my first book. Enjoy!Azeem This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

This is a free preview of a paid episode. To hear more, visit www.exponentialview.coListen on Apple PodcastsThe AI industry is sending mixed signals – markets are turning red while teams report real productivity gains. Today, I examine the latest research to understand this split reality.Skip to the best part(02:53) Unpacking three years of AI productivity data(09:54) The surprising group benefitting from AI(14:33) Anthropic’s alarming discovery(17:29) The counterintuitive truth about AI productivityThere is a lot of opinion in this space and not every hot take is built on reliable data. I stick to carefully executed research. To help you ground your stance, we’ve curated the studies behind this analysis and included the full talk transcript, available to Exponential View members.Six studies you need to know* Sarkar, S. K. (2025). AI Agents, Productivity, and Higher-Order Thinking: Early Evidence From Software Development. → Senior developers gain the most because they know how to direct and evaluate AI, which drives the biggest productivity jumps.

This is a free preview of a paid episode. To hear more, visit www.exponentialview.coListen on Spotify or Apple PodcastsSomething important is happening with the labor market. US employers announced over 153,000 job cuts in October, the highest monthly total in more than two decades. Amazon announced about 14,000 corporate job cuts as it pivots towards AI-driven operations. At the same time, research shows that entry-level opportunities are shrinking and new entrants to the job market have it harder than most.To further make sense of what’s happening, I spoke with Ben Zweig, economist and CEO of Revelio Labs. His team analyzes millions of worker profiles to track hiring and job flows – so he sees data most people don’t. Skip to the best part* (08:39) “The Canary in the Coal Mine”* (13:01) How AI anticipation is harming the job market* (27:33) Why large companies struggle to reorganize* (39:11) What entry-level workers need to doA new career playbook Ben and I cross‑checked Revelio’s data with what I’m hearing on the ground. We don’t normally do career advice – and our audience isn’t entry‑level. But many of you have kids and mentees stepping into this market, and the data is too relevant to skip. If you’re advising a new grad, here’s the concise playbook.First, understand what’s going on:* Entry-level roles in AI-adjacent fields are contracting. * And managers are risk-averse because they expect workflows to change again next quarter. Firms don’t want to hire someone into a process they know will be redesigned.How to break into an AI‑shaped job market* Ship end‑to‑end projects: choose or create multi‑step projects with real stakeholders; practice owning the plan and delivering it to a finish line. If AI takes on more of the execution work, the value for humans is increasingly in coordination around those tasks, the orchestration. It’s the ability to decide what needs to be done, in what order and with which tools, and then keep a project moving.

In today’s live, I explored why AI feels transformative for individuals but frustratingly slow at the organisational level. It’s the exponential gap that I dissect in my book: organisations struggle to update old processes in the face of rapidly improving technology. Enjoy!Azeem This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

Listen on Spotify or Apple PodcastsThe cloud boom, the chip frenzy, the race to build ever-larger data centres – surely we are simply riding the hype cycle of GPTs and chatbots?That view is far too small.What is happening is a structural re-engineering of the economy. From an economy that uses computation to one that is built on computation. Global computing power has grown by roughly 11 orders of magnitude since 1972 – a 62% compound annual increase across five decades. Every technological leap, from mainframes, microprocessors, PCs to smartphones, produced more demand, not less. Now we are entering the next curve with AI. Agentic systems will run continuously, not just when a human types a prompt; we foresee a billion-agent future. This may be a new economic fabric. And that, quietly, is the real story that I dissect in today’s episode.Listen on Spotify or Apple Podcasts This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

In today’s live I asked whether the cloud and chip surge is exuberance or something deeper. The answer is that we’re seeing a profound, structural shift: the economy is moving into a computational fabric alongside the physical elements of the real economy. I’ll publish more on this topic this weekend.Happy Halloween!Azeem This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

Watch on YouTube Listen on Spotify or Apple PodcastsI got together with China expert Jordan Schneider to understand his view on the new phase of the US-China competition. Both countries are using trade policy, export controls and industrial strategy to shift the balance of global power. Just earlier this month, China rolled out its toughest-ever curbs on rare earths and related tech. Yet, the US and China economies remain tightly bound. Jordan and I sit down to make sense of this. Jump to the best parts(01:34) The US and China’s decoupling explained(08:51) Understanding the Oct 9 “rare earth rules”(14:23) Is decoupling a strategy to avoid weaponisation?(26:03) AI incumbents aren’t entrenched – yet(43:14) Imagining an improved US-China relationshipConversation notes:To accompany this week’s discussion on the US-China decoupling, we’ve pulled together a short set of research notes. These figures and developments sketch how trade, technology and energy are changing and where to watch next.* Rare earths. China controls about 70% of mining, about 90% of refining and separation, and about 93% of high-strength magnet production; export licences required for products with ≥0.1% rare earth content from 1 December; 12 of 17 metals now restricted; licence decisions can take up to 45 business days; likely adds about $500-1,500 to EV prices in the short run.* Chips. Nvidia moved to a one-year GPU cadence; US rules tightened in 2025 then loosened with revenue sharing on China sales; China responded by steering buyers to domestic silicon; SMIC produced 7 nm via DUV multi-patterning at low tens of thousands of wafers per month; Huawei Ascend adoption is growing.* AI models. Chinese developers lead on open-weight releases; the gap between top closed and top open narrowed to low single digits on key benchmarks in 2025; many of the most used open models now come from China. Anecdotally, it seems that even some of leading US firms choose Chinese open-source models over others. Airbnb’s Brian Chesky just shared that his company ‘relies heavily’ on Alibaba’s Qwen models.* Manufacturing. EV strategy described as scale up, flood in, starve out; China exports about 7 million vehicles a year and reached about 30% of the UK market within two years; robotics deployments reached a majority of global installs, yet many precision components still come from Japan and Europe.* Energy. China maintains large reserve margins and is adding massive solar, storage, and data-centre power; data-centre demand could reach 400 to 600 TWh by 2030.Controls and workaroundsExport controls and tool restrictions are most effective at the frontier of technology. Coordinated measures across the US, Japan, and the Netherlands have delayed China’s access to the most advanced compute and semiconductor-manufacturing equipment.Below that frontier, the effects weaken. China has adapted by relying on “good-enough” chips, improving packaging and integration, developing domestic design tools, and re-routing supplies through friendly intermediaries. These measures sustain progress in deployment, even without cutting-edge inputs.Both systems are now adjusting in parallel. The US and China are investing heavily in local fabrication and packaging capacity, tightening investment and capital rules, and screening outbound flows. The outcome is not isolation but duplication: two partly mirrored ecosystems built for resilience.Perspectives worth reading* Scott Bessent interview. Allied response to rare earths, targeted reshoring, price floors, and strategic reserves; vigilance with time-bound goals. (Read here)* Kaiser Kuo, The Great Reckoning. Performance legitimacy; China as a principal architect of modernity; the West should measure outcomes and learn without denial. (Read here)* ChinaTalk analysis on synthetic diamonds. Why lab-grown diamond controls matter for wafer slicing, optics, and thermal management; leverage is real but not absolute due to alternative producers. (Read here)* Abundance and China, podcast with Ezra Klein, Derek Thompson and Dan Wang. Abundance framing for state capacity, risk pricing for tail scenarios, and learning from Chinese speed without importing ideology. (Read and listen here)Thanks for reading! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

Today’s live featured Jordan Schneider, founder and host of ChinaTalk, who’s been analysing China’s rise for years. We examined how rare earth policy, export controls, and tariff carve outs suggest a shift in China’s relationship with the US: from containment to system building. I’ll publish more detailed notes and reflections soon.Enjoy! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

This is a free preview of a paid episode. To hear more, visit www.exponentialview.coI recorded a conversation with Matthew Prince, co-founder & CEO of Cloudflare, a company that sits at the heart of the internet. Few operators have Matthew’s vantage point on how the network is evolving, which makes him uniquely placed to answer: if AI agents read, who gets paid?Jump to highlights:(00:46) The web’s currency is dying(16:19) A mathematical model for knowledge – and its implications on the web(24:35) What a new business model for the web could be (start here if short on time)(39:11) How might the agentic web affect content?Listen on Apple or Listen on SpotifyWhere to find Matthew:* X: @eastdakota* LinkedIn: https://www.linkedin.com/in/mprince/Episode notes: Cloudflare, AI distribution, governance, and key data pointsMembers can access a briefing pack I used before our conversation — structured notes on Cloudflare’s AI strategy, crawler economics and governance. These notes will help you get more out of the conversation ⬇️

Is AI a bubble? In this episode, I unpack a new five-gauge framework for understanding the biggest question in tech. Drawing on lessons from past manias – railways, telecoms, the dot-com boom – and grounding our analysis in fresh data, we examine economic strain, revenue growth, valuations, and the quality of capital fueling AI’s ascent. This is our effort to cut through hype and fatalism to provide a clear dashboard: where today’s AI build-out looks like a genuine boom, and where early warning signs of bubble dynamics may be emerging. Whether you’re an investor, policymaker, or executive, this framework offers a disciplined way to navigate the noise. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

In today’s live, I spoke with Dan Wang, author of Breakneck: China’s Quest to Engineer the Future, shortlisted for the FT & Schroders Business Book of the Year. Dan is one of the most astute observers of China’s technological and industrial development, and his annual letters from Beijing have long been required reading for those seeking to understand the country’s evolving role in the world. In his new book, he argues that China is best understood as an engineering state, in contrast to the lawyerly societies of the US and UK.I’ll publish detailed notes, along with my reflections on the conversation soon.Enjoy! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

OpenAI is expanding a secondary share sale that lets insiders sell about $10.3 billion of stock at roughly a $500 billion valuation. That raised a simple question: is a private slice of OpenAI better than buying the Nasdaq?I have an exclusive essay ready for members (arriving in your inboxes tomorrow!) breaking down the math and my assumptions in detail. Become a premium member to receive it in full tomorrow morning.Ahead of this release, I did a quick live chat with EV readers to share my initial assumptions and answer your questions. We covered:* What helps OpenAI’s case,* What tempers its case,* And Q&A including on compute & energy constraints, competition and regulation.Although I was skeptical at first, my calculations suggest that there is a path for OpenAI to deliver outsized returns. More in tomorrow's essay!AzeemP.S. Keep in mind that none of this is financial advice - only a thought experiment about the future. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

Thanks to all who joined my 30-minute live briefing today. For those of you who were not there, here’s a chance to catch up. Skip to 1:30 when I start the event.How is AI impacting the labour market? In today’s live, I discussed Erik Brynjolfsson and colleagues’ latest paper “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence”, which found that early career workers in AI-exposed roles saw a 13% relative decline in employment, while older workers saw employment increase. * The decline was stark in industries where AI automates tasks rather than augments workers, particularly software engineering and customer support.* This suggests that AI substitutes for book learning, not the skill that comes with practice. Experience and judgement are therefore becoming more valuable.* Firms may be adjusting to technological shifts through hiring pauses rather than pay cuts or layoffs.Several measures can counter this. For instance, bringing tacit knowledge forward faster and teaching AI through paid apprenticeships and time-limited incentives can all raise capabilities without widening access gaps.I’ll share more analysis on the implications of this research in our upcoming Sunday edition. Have a great weekend,Azeem This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

At the start of the year, I made seven predictions about how 2025 would unfold. Six months in, it’s time to mark my own work.* No AI wall (Grade: A)I had foreseen that we’d see no slowdown in AI capability growth. Ten-million-token models exist (Meta, Mistral, Gemini). Benchmark scores are rising sharply - Frontier Maths, RKGI, MMLU. O3 is 25% on Frontier Math where GPT-4 was 2%. There’s been real progress. That said, some models are inconsistent and overthink things. Ask me how Flash 2.5 wrecked one of our workflows.* Warp-speed deployment (Grade: A)I said usage would skyrocket and token prices would fall. We’ve seen 30x–50x increases in demand (Google, Microsoft, China). Cursor passed $100m ARR. Other verticals are following fast. Agents are everywhere. Nothing here was overhyped.* Bots would outtalk humans (Grade: A-)We modelled this crossover point a while back. By word count, bots will be ahead of us sometime this summer. LLMs now consume and produce more text than most of the internet. It’s a shift in who is shaping language online.* Waymo overtakes Uber in SF (Grade: B+)They’ve beaten Lyft, and expanded to New York. They might overtake Uber by year-end, but not we’re not there yet. Tesla’s now in the mix with its Austin robo-taxi fleet, and that changes the game.* Climate extremes intensify (Grade: A)This one was almost too easy. The world’s on fire, literally. Record temperatures, terrifying anomaly charts, more billion-dollar climate events. This was baked in.* Solar keeps breaking records (Grade: A-)Solar PV output is up 30%, led by rapid buildouts in China, Vietnam, and Pakistan. Political resistance remains, but the economics are unstoppable..* EVs shift up a gear (Grade: A-)EV growth is steady, with Indonesia jumping from 20% to 80% in two years and BYD pushing into Europe. Tesla’s stumble gave rivals room, and legacy European carmakers have lost their cost leadership.What I missedAlthough we analysed these trends in Exponential View, I failed to include the following in my predictions:* The capex boom around AI datacentres, chips, and grid infrastructure.* The rise of humanoid robots. See Figure, BMW, Amazon.* The workforce impact. Salesforce, Microsoft, Google all report AI doing 20–50% of work. Some CEOs predict job losses of 10–50%. I should’ve seen that coming.Looking aheadBy 2027–28 we’ll hit turbulence. Startups will be four or five years old, scaling fast, challenging incumbents. Incumbents like JP Morgan or Moderna, if they’ve built right, will start to reap results. It’ll be the moment vertical AI hits the real economy in force. That’s when things get interesting.One question for you: We’re now closer to 2050 than to 2000. How does that change how you think about the world? Let me know in the comments!📅 Catch me live every Friday at 9 am PT | 12 pm ET | 5 pm UK. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

In today’s live I reflected on a whirlwind three-day visit to China. You can’t do justice to a small country in three days, let alone a big one. But I was observing. And here are a few things that stuck out:* Infrastructure built at speed: Beijing’s immaculate airport, 300 km/h rail to Tianjin, and smooth expressways hammer home the sustained investment, the concrete and steel, that China has poured over the previous decades.* Verticalised AI in action: American frontier labs lead. Chinese ones are a little behind and constrained by the compute crunch. But there were some super-interesting plays in verticals: robotaxis in Wuhan, billion-record health datasets powering a medical AI, and a $200m a year AI teacher that beats the countries best human teachers.* EV cost leadership is set: China’s EVs are absolutely remarkable. Their years of domestic competition have honed an incredible level of innovation and cost discipline. They’ll hit European car makers like a sledgehammer. Enjoy.📅 Catch me live every Friday at 9 am PT | 12 pm ET | 5 pm UK. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

In today’s live I explored three tectonic shifts: Apple’s puzzling WWDC reveal and the turn towards ambient computing, Google’s waning click-ads as users embrace chatbots, and Sam Altman’s “gentle singularity” that still demands hard institutional work.Quick takes:* Apple after the iPhone: Liquid Interface points to a post-phone world of voice-first, invisible assistance; yet Cupertino lags on core AI, stitching sloth emojis while rivals edge towards AGI.* The crumble of the click: ChatGPT and Perplexity answer directly, draining Google referrals; brands must rethink discovery, with generative-AI-engine optimisation already replacing SEO.* Altman’s “Gentle Singularity”: Sam argues that AI take-off has begun, with capabilities compounding quickly and society adapting in parallel so the singularity feels gentle rather than catastrophic. I share the view that the curve is real, yet I add that its gentleness will only hold if we guide it through sound policy, open competition and personal upskilling.Enjoy.📅 Catch me live every Friday at 9 am PT | 12 pm ET | 5 pm UK. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

In today’s live show I spoke with Kevin Weil, CPO of OpenAI, just moments after he announced a range of new features including enterprise connectors and meeting recordings.We discussed:* ChatGPT now ingests Google Workspace, Microsoft 365, Dropbox, Box, SharePoint and OneDrive data, while new agents summarise docs, run multi-step research and commit code.* GPT-4o becomes the default; GPT-5 is on the runway to fold frontier abilities into one workhorse.* Eval-driven loops let product and research teams convert desired skills into benchmarks, then ship them straight to users.* AI-generated code could expand the pool from 30 million to a billion developers, unlocking personalised software everywhere.* Startup playbook: build where models are almost good enough and your product improves when the next model lands; avoid businesses that patch current weaknesses.* Sir Jony Ive is designing dedicated hardware because phones restrict power, microphones and ambient presence.* Opting out will soon feel like refusing a smartphone; keeping frontier models under liberal institutions is strategically vital.Enjoy!📅 Catch me live every Friday at 9am PT | 12pm ET | 5pm UK. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

What happens when AI writes more of our code than we do?In today’s Live show, I spoke with Thomas Dohmke, CEO of GitHub, the epicentre of modern software development, about the transformative impact AI is having on the way we build software.We explored:* How AI, including tools like GitHub Copilot, fundamentally reshapes the developer's role from coding to problem-solving and creativity.* Why AI tools like Copilot dramatically increase developer productivity by automating repetitive tasks, enhancing flow, and enabling quicker prototyping.* The implications of AI-driven coding in enterprise environments. * What a world with a billion developers looks like, and how personalised software might empower every user to innovate. * The rise of an agent-centric web, where interactions are increasingly automated and context-aware. * A playful take on how AI could revolutionise sorting your Lego pieces (because who doesn't need help with that?).Enjoy!📅 Catch me live every Friday at 9am PT | 12pm ET | 5pm UK. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

This is a free preview of a paid episode. To hear more, visit www.exponentialview.coWhat does it really take to transform your organisation into an AI-first business?In today’s Live show, I spoke with Aaron Levie, CEO and co-founder of Box, a leader who’s been on the front lines of enterprise AI adoption and is refreshingly open about what works (and what doesn’t).We discussed…* How AI is reshaping jobs at every level.* The hidden chokepoints that keep company-wide productivity from matching individual gains.* Practical tactics to help frontline teams unlock value from AI.* How Box nurtures a culture of rapid-fire AI experimentation.* Why trust and reliability in AI agents will rewrite management playbooks and org charts.* The shifting economics of enterprise software in an era ruled by agents.Enjoy!📅 Catch me live every Friday at 9am PT | 12pm ET | 5pm UK.

What happens when the power to shape AI’s future lies not in the smartest model—but in who controls the compute?This week, I spoke with Lennart Heim, researcher at RAND and advisor to Epoch AI, to unpack the geopolitics of compute: why sheer model capability is no longer the only game in town, and how control over compute may define who leads in AI.We discussed:* Why China matching US models doesn’t mean it’s winning the AI race.* The staggering scale of infrastructure: 100,000+ GPUs, billions in hardware, and spiralling test-time compute demands.* Why algorithmic efficiency is slashing costs and redrawing the frontier faster than expected.* The economic and national security tension between AI deployment and control.* The risks of over-concentration and the rise of sovereign compute strategies from Europe to the UAE.* What open-source models on abundant compute might mean for future power dynamics.From token floods to trillion-dollar CapEx, we explored the delicate architecture of power in the age of AI. Enjoy!📅 Catch me live every Friday at 9am PT | 12pm ET | 5pm UK. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

What happens when Europe's leading renewable grids buckle under pressure, and energy becomes as volatile as it is vital?This week, I spoke with Greg Jackson, CEO of Octopus Energy, the UK's largest electricity provider, to unravel the Iberian blackout, where tens of millions lost power overnight, and explore how to future-proof our energy grids.We discussed:* Why Spain and Portugal's "energy island" approach makes their grids vulnerable.* The critical lack of battery storage and digital infrastructure contributing to grid instability.* How renewable-dominated grids need smarter, software-driven balancing systems.* Texas and Australia's lessons in overcoming grid isolation through market innovation.* The transformative potential of virtual power plants and consumer-led demand management.* The urgent need to transition faster—crossing the "multi-lane highway" to a flexible, resilient grid.From frequency wobbles to smart-charging electric vehicles, we connected the dots between technology, economics, and the practical realities of energy transition. Enjoy!📅 Catch me live every Friday at 9am PT | 12pm ET | 5pm UK. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

This is a free preview of a paid episode. To hear more, visit www.exponentialview.coThis week, I sat down with Ryan Petersen, CEO of Flexport, at a moment of real upheaval in the global trading system. Ryan runs a logistics platform that sees about 1% of all US trade, so he’s got a direct line into how companies are reacting in real time: pausing shipments, renegotiating supply chains and in some cases, facing existential choices.I wanted to understand how the tariff storm is playing out on the ground—and what it means for the future of globalization.We talked about:* The economic shock hitting importers right now and why many are considering abandoning shipments altogether* How this new trade war may unintentionally increase global shipping, as supply chains stretch and reroute* The weird inefficiencies emerging around semiconductors and AI hardware, thanks to tariff classifications* Whether this is just another blip—or a real inflection point in the 800-year rise of global trade* What Flexport has seen in customer behavior—and what that tells us about resilience, agility and Darwinian survival.We also went deep into the geopolitics of shipping: the fragility of sea lanes like the Red Sea, the underinvestment in US ports, and what it really means when the US Navy stops guaranteeing free trade routes.Ryan brought a perspective that’s part historian, part operator and part futurist. If you’ve ever wanted to understand how technology, tariffs, and tectonic shifts in globalization collide - this is your episode.Thank you Sinéad Bovell, Alex Pawlowski, Badru Ntege, Loes, and many others for tuning into my live video with Ryan Petersen!

This is a free preview of a paid episode. To hear more, visit www.exponentialview.coLast week, I wrote to my portfolio companies with a simple message: the game is changing fast. The global order is being renegotiated in real time, driven by the inversion of globalization and the rise of AI.These forces are creating confusion, hesitation and for the sharpest operators – opportunity.If you’re a founder, executive or investor, this message is for you. My message to portfolio companiesSent on 6 April 2025We are living through an incredibly weird time. Two massive waves are crashing down upon us: the accelerating force of artificial intelligence and the dramatic inversion of geoeconomics. Each of these forces is disruptive on its own – but together, they are increasing the stakes for everyone. In this message, I will address:* The geoeconomics wave* The AI wave* My advice to founders and business leadersLet’s start with geoeconomics and the shifting posture of the global order.Globalization, as we’ve known it for the past three decades, is being renegotiated, restructured from the ground up. This isn’t just a US phenomenon. Governments around the world are adapting to this new paradigm, where industrial policy, national resilience and sovereign control take precedence over global efficiency.As political winds shift and global instability intensifies, so too does the appetite for risk and experimentation. Uncertainty is now the defining condition.

I hosted a live discussion today with physicist and entrepreneur Steve Hsu. We covered a lot, including…* AI agent development & hallucination challenges: Steve shares how he’s thinking about tackling LLM hallucinations at his startup Superfocus and what the future of AI agents could be.* Barriers to business adoption: We explore why organizations are slow to adopt AI technologies despite their readiness.* China’s AI innovation ecosystem: We discuss how different cultural attitudes toward technology, deeper STEM talent pools and competition between city leaders create a dynamic environment for AI development in ChinaSteve was on my podcast first in 2019. You can listen back to that episode here:. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

What happens when the world’s two superpowers go head-to-head in an era of economic upheaval, tech acceleration and military recalibration?This week, I sat down with the eminent historian and Hoover Institution fellow Niall Ferguson to wrestle with what might be the $120 trillion question: what kind of world order is emerging as the US pivots to confront China?We explored:* The emerging “Trump Doctrine” and its Nixonian echoes* How the US is recalibrating its global commitments to focus on China* Why Europe is finally waking up to its defense responsibilities* China’s growing technological confidence—and economic constraints* Taiwan, semiconductors, and the flashpoints that could reshape global power* What a new world order might look like: multipolar, unstable, or something else entirelyFrom the rise of BYD and DeepSeek to the limits of US naval power, this is a wide-ranging discussion that connects history, strategy, and the cutting edge of technology. Enjoy!📅 Catch me live right here every Friday at 9am PT | 12pm ET | 5pm UK. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe

I hosted a live with Packy McCormick earlier today and we had a blast—covering a wide range of topics, including:1. A sense-check on crypto and Web3:* Transition from idealistic vision to mainstream financial instrument.* Importance of regulation and guardrails.* Challenges of open systems and manipulation.2. AI’s current state & impact:* “Super SaaS” vs existential threat.* ChatGPT and Claude competing for leadership in daily use.* Anthropic’s prediction of “thousands of geniuses in data centers” vs. skepticism of established AI scientists.* Focus on human differentiation and personal development in the AI era.3. Energy infrastructure & innovation:* Innovation in battery storage and startups we’re excited about.* Nuclear power: Historical successes and future potential.* China’s electric vehicle and energy progress.4. European strategic shift:* Major mood change from January 2024 to present.* Making sense of the €800 billion allocated to defense and resilience.* Infrastructure challenges in Europe and the UK highlighted by Heathrow Airport shutdown. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.exponentialview.co/subscribe