Sequoia almost never lets outsiders see what it actually tells its own LPs.
Pat Grady recorded this one for Boston College’s Investment Committee, an LP and his alma mater, after they asked for his read on AI. His partners heard it and told him to post it. He did, on his own account, two days later. Worth reading slower than a quick LP briefing usually gets.
The thesis is blunt. Companies building on top of AI models have to reinvent themselves every four months, because the platform underneath improves faster than anything built on it can keep up.
The internet and mobile changed how information moves. AI changes how it gets processed. The last shift this size was electronics moving to silicon in the 1960s. That comparison is Pat’s, not mine, and I think it holds.
Three Points, Not Ten
He marks three actual turning points.
ChatGPT, November 2022. Scale reached everyone at once, for the first time.
o1, end of 2024. Reasoning arrived, a model that thinks before it answers.
Claude, autumn 2025. Agents that hold a long task instead of answering one question and stopping.
The first two produced faster horses. The third produced something that finishes the job on its own. Zoom is the closest parallel I can think of. Video calls existed for years before Zoom. People just didn’t trust them yet.
1. The Implementation Gap
Between what models can already do and what companies actually run in production sits a canyon, and every applied AI company is living inside it right now.
The labs are closing that gap themselves. Price cuts on the API are one lever. The more direct one is people: armies of forward deployed engineers sitting inside Fortune 500 accounts, building the application on the client’s own tokens. What used to be a niche job title is now a lab strategy.
2. Expensive Knowledge Work Gets Cheap
Coding. Cybersecurity. Medicine. Finance. Accounting. Sequoia expects a giant to show up in each one.
One portfolio company closed last year at $200M in revenue and is on pace for $700M this year. In pharma, drug candidates that used to take years to shortlist now get found with a prompt in days.
3. In-House Fine-Tuned Models, Argued Both Ways
This is the implication I’d slow down on, because I watched two smart people argue it in opposite directions in the same week.
Sequoia’s case: companies are shifting load off frontier models and onto their own fine-tuned open-source models, for a plain economic reason. A vendor sells you three points on the price to quality curve, big, medium, small, and your actual workload almost never lands exactly on one of them. Your own model ends up cheaper and a better fit. Cheap tokens don’t mean everyone runs straight to OpenAI.
Boris Cherny, who leads Claude Code at Anthropic, made the opposite case on Lenny’s Podcast earlier this year. His rule: always bet on the general-purpose model, don’t use small models, don’t fine-tune. Separately, on scaffolding specifically, engineering built around the model rather than the model itself, he put a number on it: 10 to 20% improvement at best, usually wiped out by the next model release. You’re better off waiting.
Right now the trajectory backs Boris. But a countercurrent is building too: open-weight models, and companies that simply won’t send their context outside their own walls. One of our own clients, a large pharmaceutical company, doesn’t allow any external AI tool at all.
I wouldn’t bury either side yet. The more valuable and sensitive the corporate knowledge involved, the stronger the case for keeping the model next to the data instead of sending the data out to a frontier model.
4. Specialized Architectures
The world doesn’t need another general purpose lab. It needs labs built around one domain, or one new architecture.
TypeSafe AI showed up with Jev, a classifier model. Most corporate machine learning reduces to picking between a handful of options, and a classifier does that 100 times cheaper than an LLM. Grady’s own account puts Jev at $100M in run rate revenue within seven days of launch, a figure I haven’t seen independently confirmed yet. The Information has separately reported investor talk of a $10B valuation.
5. The Organization Becomes the Lab
The best people get pulled off day to day operations and handed room to break and rebuild the product, coordinated through AI instead of layers of managers.
Otherwise nothing gets rebuilt in four months. It just gets planned in four months, which is a different thing entirely.
The Number That Made Me Do a Double Take
Sequoia’s new normal is two rounds back to back on the same company. One led by a partner who builds alongside the founders. A second, a month later, that’s pure capital.
Sequoia counted seven of these over the past twelve months. The average first round priced the company around $110M. The second round, about a month later, priced it around $3.4B.
Pat says he’s never seen anything like it. In the same briefing, he calls it a bubble and, minutes later, the fastest and largest technology transition in history.
I think both are true at the same time.
Where the Real Risk Sits
Sequoia reads the implementation gap as a market opportunity for startups.
I read it as a market for accountability. The gap gets closed by specific people inside a specific company, people who signed their name to the outcome and who take the reputational hit the next time the floor shifts under them.
You cannot out compute a lab. What a lab will never take on for you is your context, your data, and the part where somebody has to explain why it didn’t work.
I’d ask my own company one thing: what we rebuild in the next four months, and whose name ends up on it.