Ivan Burazin
@ivanburazin • 16,761 subscribers
building @daytonaio
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The founder of Postman says you have to kill your existing org chart, especially if you're still operating with a pre ai hierarchy arrangement. The modern org chart, according to Abhinav Asthana: - wide span of control (even within exec team) - work directly with ICs, not through layers - either you're building, or you're selling Projects are led by staff/principal engineers with high agency. They see across the board as well as deep in the stack. Product managers are building APIs and prototyping in Claude instead of writing PRDs. Designers are shipping PRs through Cursor directly instead of relying solely on Figma. Everyone is building. And the management's job is to develop better judgment.
Ivan Burazin651,313 次观看 • 2 个月前
Dylan Patel says GPUs are no longer the biggest bottleneck. According to Dylan Patel, now CPUs are the constraint. In the early AI era, CPUs were the laggers. You used them for storage, checkpointing, pre-processing, etc. (pretty light workloads) The models weren't agentic and couldn't go step by step. Just string in and string out (simple inference) Then OpenAI launched O1 preview in September '24, and RL training loops have since tightened every month. - initially it was checking model output with regex - then running classifiers - followed by code unit tests + compilation - and finally agentic flows calling databases & scientific simulations The model outputs to an environment, gets verified, and trains on it. Coding agent revenue went from a couple billion to north of $10B in roughly 6 months. Something like Codex 5.4 can work agentically on its own for 6-7 hrs straight - doing all sorts of calls (databases, cron servers, scraping) That requires insane CPU capabilities. And over the last two quarters, the entire cloud market ran out of CPUs. - GitHub has been really unstable lately - Amazon's CPU server installations 3x'd year over year - Microsoft sold all of its spare CPUs to Anthropic & OpenAI Earlier, it was 100 megawatts of GPUs served by 1 megawatt of CPUs. Now that ratio is getting much closer for both RL training and agentic inference. There's simply no capacity anywhere, and it's causing massive instability.
Ivan Burazin303,474 次观看 • 2 个月前
The co-founder of a $3B+ application monitoring platform says they budgeted $15k/month for each developer's Claude Code usage, and it's still not enough. David Cramer revealed that they allocated more money for devtools this year than they ever have in the history of their company. His own spend is somewhere around $200-300 a day, and he's still nowhere close to generating the ROI to justify that kind of usage. But his broader point is that we're still very early. Using the technology, learning it, and understanding the negatives and positives is what's really important at this moment.
Ivan Burazin140,318 次观看 • 2 个月前
The CTO of a $1.5B agent-native software company says we're moving towards a world where the entire team builds the product engineering system that powers your product instead of directly working on the product. They will modify the agents and the constitution of the agents to help them prioritize tasks per Eno Reyes. They will also modify the review system so that it reviews more accurately. It will be akin to a dark factory where the lights are off because it's all robots with no humans inside. In that world, there will be way more things for humans to be involved in because the debates around prioritization/product will be harder and require stronger judgment.
Ivan Burazin64,292 次观看 • 2 个月前
The founder of a $4B inference company says that if you're building agents, foundational models could become your IP. According to Lin Qiao, 90% of the world's data is still private and locked inside applications and enterprises. Foundation models are trained on public internet + labeling company data, which is barely 10% of all the data. And this is why your application and foundation models are misaligned by definition. Companies building agents don't treat models as APIs. They opt for a product-model co-design. - Models continuously learn from your private data - Pick up domain-specific intelligence - Run faster - Cost less - Scale to millions of users
Ivan Burazin54,964 次观看 • 2 个月前
The founder of LangChain says both models and harnesses have gotten really good between December and now. According to Harrison Chase, the core idea of an agent before Christmas was a model running in a loop and calling tools. This had been the north star for 3 years. - langchain had this when it launched - autogpt was the same idea - openclaw is kind of a future version of it Then about a year ago, they started getting really good. Claude Code, Manus, and Deep Research were all launched around the same time. All of them use the same pattern: running in a loop with harnesses (planning tools, file systems, code execution, etc) Harness engineering became a thing. Then Opus came out in November and really unlocked it. - the harness let the model do more and more - less hardcoded logic - way more control Then everyone went on vacation, played around, and realized that the model and the harness finally worked reliably.
Ivan Burazin33,948 次观看 • 2 个月前
The ceo of Box says if you're not building for agents, you won't exist as a software company in 2-3 years. Aaron Levie recalled how in the 90s, it was mainframes built for institutions. If you didn't build for the CIO or IT org, you didn't exist. You could build software for users, but no one cared. Similarly, in the mid 2000s, it was software built for end users. If you didn't take that route, you got completely trounced. The likes of Yammer, Slack, Zoom, etc., ripped through enterprises with viral adoption. Bottom-up and completely user driven. And now it's software built for agents. We're going to have trillions of agent runs. The systems that optimize for agents the most will win. - lower latency - better API exposure - better system intelligence - serves the right information when called Agents will eventually choose their own tools on the fly and make purchasing decisions at runtime. All based on past experiences/online reviews/performance, sans any procurement process. The more pragmatic version is companies swapping out bad tools / tech that don't work well with agents. - crm doesn't expose the right APIs - auth can't handle agent scale requests - database has too much latency for agent workloads Being good enough for humans won't work because they have to pass the litmus test for agents to make the cut.
Ivan Burazin22,608 次观看 • 2 个月前
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