Chamath Palihapitiya
@chamath • 2,113,494 subscribers
God is in the details.
Videos
It doesn’t matter how fast your code is generated if the requirements are unclear. Shit in == shit out. That’s why we designed Software Factory as a discipline, not as a convenience tool. Our agents scrutinize your requirements and designs so that when you pass that context to a coding agent, it delivers as expected. Try it here:
Chamath Palihapitiya305,141 views • 4 months ago
Most documentation rots because it’s expensive to maintain and easy to ignore. Software Factory is documentation-first so teams can describe systems in English and let agents compile that intent into code. It detects drift on every code push and keeps documentation synchronized with reality. Context stays alive. Agents keep building. Try it here:
Chamath Palihapitiya197,716 views • 3 months ago
Software leverage doesn’t come from faster typing. It comes from stronger coordination. Software Factory is a multiplayer platform: shared requirements, blueprints, and work orders flow to coding agents via MCP. Alignment first. Then parallel execution. Try it:
Chamath Palihapitiya142,969 views • 4 months ago
On Friday, I hosted a Space with Jonathan Ross, the founder and CEO of Groq Inc - a company I invested in that is building custom chips for AI inference. Jonathan, a former high-school dropout, entered the chip industry while working on ad optimization at Google’s New York office. Jonathan overheard the speech recognition team complaining that they couldn't get enough compute. These were the early days of AI, and machine learning wasn’t really a thing yet. So he asked for some budget from Google and started putting together a chip-based machine learning accelerator for them. During the day, Jonathan would work in the normal ads part of the business, and at night, he would work with the accelerator team. After winning approval from Google, Jonathan and his team built a new chip called the Tensor Processing Unit, and began deploying it across Google’s data centers within a year. The TPU was a huge success within Google, eventually underpinning more than 50% of all of Google’s compute power. When the other hyper-scalers learned of this success, they tried to hire Jonathan to build custom chips for them too. During this process, it became increasingly clear to Jonathan that a gap would emerge between companies that had access to next-gen compute and companies that didn’t. So he founded Groq and set out to build a chip that would be available to everyone. I led Groq’s founding investment in 2016, and since then, Jonathan and his team have developed several types of AI hardware including the Language Processing Unit (LPU), a new type of silicon that is hyper-efficient at running inference for LLMs. In our conversation on Friday, we discussed the founding story of Groq, what you need for great AI hardware, large language models, and some of the implications for the key players in AI. It’s one of the most interesting conversations I’ve had on AI with a lot of learnings. You can listen to our conversation below:
Chamath Palihapitiya326,508 views • 2 years ago
Great chatting with students at The Wharton School. We talked about growing up in an immigrant family, the early days at FB, building Social Capital, the biggest economic drivers of the next decade, and what the next generation of tech investing can look like...
Chamath Palihapitiya294,653 views • 3 years ago
My take on the energy discussion with China and the U.S grid problem:
Chamath Palihapitiya59,970 views • 6 months ago
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