The first time we talked to @bernhardsson in 2023, we knew he was someone special and would be an exceptional founder. Proud to have backed them with high conviction since the Series A. Congrats @bernhardsson, @akshat_b and team on $1B run rate and the Series D! We believe @modal will be one of the most important AI Infra companies. We're grateful and look forward to continuing to be a small part of the journey 🚀

I work at a fast-growing startup that I am very optimistic about. And I still think joining a startup as a new grad is a high-risk career decision. Not because startups are bad. A great startup can compress years of learning into months. You can own real problems, talk directly to users, make decisions far above your nominal level, and watch your work affect the company immediately. But the downside is often misunderstood. The obvious risk is that the company fails and your equity becomes worthless. The more important risk, especially early in your career, is that your learning environment fails with it. Your first few years of work are when you build your internal definition of what good engineering, good management, good product judgment, and good execution look like. At a mature company, many of these systems already exist. There are imperfect but established review processes, senior engineers, historical context, clear ownership, customers, metrics, and people who have seen the same class of failure before. At an early startup, you may be learning while the organization itself is still learning how to operate. That can be amazing if the people around you are exceptional. It can also be dangerous. A company can call something ownership when there is simply nobody available to mentor you. You can report directly to a founder and still receive very little useful feedback. You can touch strategy, product, sales, and engineering and feel that you are growing incredibly fast, while never becoming excellent at any of them. You can become indispensable because only you understand a broken process. That makes you important to the company, but not necessarily more capable. And because everything changes so quickly, it is easy to mistake surviving chaos for learning. I have seen friends struggle with exactly this. Smart people joined exciting companies, worked extremely hard, wore five hats, and after a year or two had trouble answering a surprisingly simple question: What am I now much better at than when I joined? That is the question I would care about most. For a new grad, I would evaluate a startup less by how impressive the founders sound and more by the quality of the learning loop: Who will review my work? Who can tell me when my reasoning is wrong? Will I own something end to end? Will I see real users and real outcomes? Can the team explain what good performance looks like after 3, 6, and 12 months? If the company disappears in two years, what skills, judgment, and evidence of work will I carry with me? And of course: does the company actually have enough runway for me to stay long enough to learn any of this? The best startup jobs give you unusually tight feedback between action and consequence. You build something, ship it, watch it fail, talk to the user, fix it, and gradually develop judgment. That loop is incredibly valuable. But without good mentorship, clear ownership, and real feedback, the same speed can simply generate noise. So yes, I am very bullish on startups. I am also cautious when people tell new grads that joining one is simply a matter of being brave enough to take risk. Risk is only worth taking when it buys you something. For an early-career engineer, the most valuable return is not a title, proximity to a founder, or even equity. It is becoming meaningfully better at something that will still matter after the company is gone.

My biggest takeaway from SF Tech Week: the center of gravity has already moved. Last year everyone was talking about agents. This year, a lot of the interesting conversations were about everything agents need once they actually start working: routing, search, data, evals, observability, latency, inference cost, GPUs, deployment, reliability. The question used to be: Can an agent do this? Now it increasingly feels like: How do we make a million agents do this reliably, cheaply, and controllably? That shift matters. Companies like Tavily are a good example. Most end users may never know the name, but a huge number of agent workflows need search and web retrieval underneath. Once agents become real products, the boring-looking infrastructure around them becomes extremely valuable. Another thing Tech Week messes with is your perception of startup outcomes. You meet founder after founder who raised, got acquired, or somehow went from starting a company to becoming a VP / GM / Head of X at a much larger company in a few years. After hearing enough of these stories, your brain starts treating this as normal. It obviously isn't. Tech Week is basically the Instagram feed of the startup distribution. You are meeting people from the far right tail. The companies that ran out of money, never found PMF, or quietly dissolved are not usually on stage telling you their story. And even we got acquired can mean very different things: a great strategic outcome, an acquihire, investors getting some money back, or simply a soft landing. Startups don't just increase expected upside. They massively increase variance. One other thing I kept hearing was FDE: Forward Deployed Engineer. The role is interesting because it blurs boundaries that used to feel much cleaner. Engineer builds. Sales sells. PM translates. But more and more, the valuable person seems to be someone who can talk to the customer, understand the business problem, talk to engineers, turn ambiguity into something executable, and actually ship it. Engineer + Product + Consultant + Sales. I increasingly think this is less a job title than a survival skill. AI is already crawling into every industry and every workflow. So maybe the interesting question is no longer what AI can do. It is: What workflow has not been rebuilt yet? And after spending a few days around all of this, I came away with two contradictory feelings: How is everyone here doing something this ambitious? And: Maybe I should be doing more too. The Bay Area has never had a shortage of ideas or smart people. The scarce things are judgment about what is worth doing, the willingness to stay with it for years, and the ability to get other people to care.

We threw our first conference last week in SF. Tons of cool people and talks. A very high correlation between being cool and being there. Anyway, this is the keynote, in which I announced a bunch of Modal features. We also launched 1M sandboxes in a minute for fun!

Even in moments of stress, it’s so wonderful to work with the @modal team. They respond quickly, do what they say they’re going to do, and it feels like you’re talking to a real engineer rather than a support person.

Earlier this year, when asked by @tbpn what the most underrated market in venture was at the time, I said "inference." This is also why I have proudly backed companies like @togethercompute @inferact @reactorworld over the past few years. Per usual, @ttunguz provides a fantastic articulation of the why behind this statement.

We are in the fierce inference competition phase. To make money, owning GPU is not enough. You must: - know where to get cheap electricity (even: Finland, Philippines??) - ideally own GPU, not rent GPU - good at running inference optimization - sometimes even post train your version of the model to make it faster and efficient The inference market is getting more sophisticated. We used to say DeepSeek is cheap based on their listing price; now you can easily find heavily discounted token price off their already low listing price, from many inference providers. That’s being said, openrouter is a fucking huge business. I almost feel like they shouldn’t sell. Because on top of many labs, you also have way more inference providers for each model competing fiercely. Funny that people used to think openrouter is a niche business and Claude was gonna own all models just a few months ago.

🚨 BREAKING: Claude can now map out your retirement better than most people charging $2,000 ever will. Here are 10 prompts to figure out exactly when and how you can retire. (Save this before it disappears).

Excited to announce our new partnership with @DecagonAI to bring richer enterprise data intelligence directly into customer conversations. AI agents are only as useful as the context they can access. Enterprises already have an incredibly rich record of their customers in Databricks. Orders, payments, product usage, preferences, and countless other signals all help explain who a customer is and what they need. Through this partnership, Decagon agents can access governed data from the Databricks when they need it, while insights from those conversations can flow back into Databricks and become useful across the rest of the business. We’re excited to work with the Decagon team to make enterprise customer agents more context aware while keeping data governance at the center. decagon.ai/blog/decagon-and-…

this modal guy is cooking rn

Life update - I recently left my PhD and joined the @modal inference research team! TLDR on why I joined: 1. We have autonomy to solve hard and interesting problems 2. Everyone is on the critical path 3. The talent density is ridiculously high

Agent market is far from decided.... Fo is more trustable, safer with credit cards, much higher completion rates and much more! And humans in the loop help solve the harder tasks

WOW! Open AI's ARR nearing $70 BILLION This is up almost 3x from February and 75% from August! Codex and the pivot from consumer to enterprise has been a killer move for OpenAI At the same time, OpenAI is also working on monetizing its consumer audience via ads, expecting $2.5B in ARR from the ads business by EOY This jump in revenue brings some much needed confidnece back into the AI market, as partnered companies like Oracle are seeing a big jump in their stock price (up 8% on the news alone) We now have Anthroppic at around $75B and OpenAI near $70B, while Grok and Muse are seeing signivaint growth across their agent products, Grokbot and Muse The revenue for AI is showing up big time (As I've been saying it would!)