Mathias Asberg

In 2023, OpenAI’s first DevDay felt like a milestone: 2M developers, 100M weekly ChatGPT users, and 300M tokens processed per minute on its API - within a year of ChatGPT’s release. Two years later, DevDay looks less like a developer conference, more like a sovereign address: 4M developers, 800M weekly users, 6B tokens per minute. This, to use a technical term, is "a lot." The numbers are staggering, but the story isn’t size - it’s shape. OpenAI isn’t just getting bigger, it’s changing form. The biggest DevDay reveal wasn’t a new model. It was that users can now invoke apps directly inside ChatGPT. Expedia, Figma, Canva, Zillow, Spotify to start, soon to be joined by Uber, DoorDash, Target etc. Not a UX tweak, a reordering of the digital value chain. They also launched a preview of the Apps SDK - a dev toolkit to build directly into ChatGPT. If you squint, it looks like Apple in 2008 inviting developers to build on iOS. Only this time, the platform isn’t for mobile apps - it’s for intent. ChatGPT is no longer an endpoint. It’s a gateway - a layer between you and everything online. When you book a flight or order dinner, ChatGPT decides which service gets the call. Every other company just became a supplier. This is precisely how operating systems take power: not by replacing apps, but by absorbing them. What makes this shift historic is what OpenAI is aggregating. Google aggregated links. Apple aggregated apps. Amazon aggregated transactions. OpenAI is aggregating intent itself - the moment before action. When you ask ChatGPT to “order sushi” or “call an Uber,” you’re not opening an app - you’re expressing a need that the system fulfills on your behalf. Uber, DoorDash, and Spotify become fulfillment nodes rather than destinations. It’s the first platform to sit above the app layer, dissolving the mobile grid into a single conversational surface that controls how digital intent becomes physical action. Value in AI is shifting fast: Models → Apps → Aggregators. OpenAI is building the new aggregator - an “app store for intelligence.” And once aggregation sets in, users stop caring which app fulfills a request. The app is the API call. The model is the brand. The rest of the internet is middleware. The monetization playbook writes itself: Transaction fees on bookings and orders Sponsored responses (“Try this option”) App-store style revenue share Tiered speed (“Faster thinking for $20/month”) Enterprise upsells with compliance badges It’s the oldest trick in tech: capture distribution, then tax it. Anthropic and DeepMind now need to decide whether to mirror this vertical integration - or double down on being neutral model suppliers. The frontier question has shifted from “Who has the best model?” to “Who owns the daily interface?” First, OpenAI built the models, then the APIs, then the apps. Now it’s building the environment. It’s a little terrifying - and completely predictable.The model era is ending. The platform era has begun.

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AI capital is consolidating fast. $31B went to just 16 startups in Q2. Tl-dr: This week’s Startup Riders drop breaks it all down: • Which AI markets have locked (LLMs, legal, search) • Which are still open (sales agents, infra, copilots) • $2B seed rounds are here (Thinking Machines Lab) • 80+ unicorns in H1 (15 from Europe) • YC’s GTM AI stack: 18 vertical sales agents • Consumer AI is shifting: memory, emotion, intimacy • Spain's €10M+ round heatmap • What it all means for early-stage founders 📩 Read the full issue → https://lnkd.in/dTwZY4Wg #ai #llms #agent #startups #founder #vc

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Since the release of ChatGPT (enabled by GPT 3.5) in 2022, the default narrative has been one of AI takeoff and divergence. The imagined scenario is one where a leading AI lab develops a recursively self-improving model. In doing so, there is an intelligence explosion. That leading lab (and the nation behind it) then uses that model to diverge from the rest of the market to become strictly dominant across domains. Whether or not you believe (and believe others believe) we are headed towards a divergent or convergent future of model performance, is core to any forward looking assessments of technology or (geo)politics. Thus what we found most significant about the release of GPT-5 is that, despite its impressive performance, it gave some of the best evidence for a convergent future of AI (based on current model architectures). Historically, when OpenAI releases a new frontier model, they have dominated across benchmarks. In a convergent scenario, instead of seeing a widening gap between the top frontier model and the rest, we would expect to see frequent swaps at the top of benchmarking leaderboards. Frontier performance clusters as improvements become more and more incremental. With GPT-5, OpenAI did not dominate. Whether or not you adopt the belief that foundation model performance is converging, at the very least you should re-weight your expectations. We have. Read the full essay for more on ... → The reliability problem with agentic AI → Why we believe in is selling services, not tokens → How this should update our views on energy, jobs, and chip policy https://lnkd.in/epnEspbq

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AI is no longer just trained on data. It’s being raised in environments. Billions are flowing into synthetic training grounds where AI agents practice: – Virtual offices to learn scheduling and negotiation. – Simulated warehouses to master logistics. – Entire artificial marketplaces to test decision-making. This is the shift: from studying the past (datasets) to shaping behavior in invented environments. And here’s the power play: whoever builds these environments sets the rules. Not just what AI knows — but how it acts. That’s the new empire being built. It’s not about bigger models or faster chips anymore. It’s about who controls the environments where AIs learn how to operate — and what hidden incentives and biases get baked in. Are we ready for a handful of companies to design the operating conditions of the machines that will run our future? And who has a role to play in this? #AI #Business #Transformation #Next https://lnkd.in/eSnz6bjy

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Positron AI is quietly carving out space in the AI compute race. 💡 Their Atlas system has already booked tens of millions in sales. 🖥️ Customers: Cloudflare (power-efficient inference) + Paracel (AI-as-a-service). ⚡ Strategy: a “warm socket” → prove it works → upsell to their Asimov ASIC (2026). 📈 Even getting traction with hyperscalers & financial trading. This isn’t just about chips—it’s a roadmap to challenge Nvidia. #AI #GPUs #Nvidia #Positron

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Why most AI scale-ups stall after the demo — and what to do about it A Series B founder shows me a brilliant AI prototype. Real use case. Strong team. Investors are interested. But underneath? • No governance in place • No explainability built in • No compliance mapping • No production monitoring • No path beyond pilot number one They’ve built an experiment. Not a system. And now they’re stuck in what I call the maturity trap. It’s more common than most people think. 88% of AI pilots never scale. Only 12% of companies reach operational maturity. And the problem isn’t the model. It’s the organisation: • Data is fragmented • Talent is siloed • Compliance is reactive • Strategy is disconnected from delivery The result? Wasted budget. Frustrated boards. Delayed raises. And teams are unsure what AI is actually for. Here’s what we’ve learned guiding Series A to D scale-ups: • Governance is not a luxury. It’s a launch condition • Strategy, data, and talent must mature together • Compliance must be built in, not bolted on • Maturity is about readiness, not theory You don’t need theory. You need guardrails that turn AI from a risk into a revenue driver. I’ve built a maturity playbook for Series A to D companies stuck in pilot purgatory. Message me ‘maturity’ and I’ll share the framework. Or drop a comment: What’s the number one thing that stalls AI adoption in your world? Would love to compare notes.

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