Founder @ AI Startup (Stealth) | Training Large-Scale Neural Networks | Curating the Best in AI & Tech | Building the Future
Joined December 2012
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Sinclair Ta retweeted
Your cafe’s QR code is a link to your books. Anyone who pays once can see every sale you've ever made.
Now: a fresh address per sale. Ring up 4.50, they scan, they pay, that address has one payment on it. Theirs.
Tap Close till at 6pm and the day lands in your wallet.
Two minutes to switch on:
privacyhood.org/merchant?pos…
- Private takings → Turn on
- write down the seed it shows you (your only backup)
- send a little ETH to the gas float, it pays for the close out sweep
Sinclair Ta retweeted
Lee Kuan Yew:
“Air conditioning was a most important invention for us, perhaps one of the signal inventions of history. It changed the nature of civilization by making development possible in the tropics. Without air conditioning you can work only in the cool early-morning hours or at dusk. The first thing I did upon becoming prime minister was to install air conditioners in buildings where the civil service worked. This was key to public efficiency."
Sinclair Ta retweeted
Some rough math! (All napkin math...)
Assume Colossus 1 has 220k GPUs
Assume 150k H100s, 50k H200s, 20k GB200s
Pricing Assumptions:
- $2.30 / hour for H100s
- $2.60 / hour for H200s
- $5 / hour for GB200s
- blended rental rate across the entire fleet of $2.60 / hour
Assume it's all take-or-pay style deals (you pay for 24x365 usage)
This translates to ~$5b of annual rev to Xai. We have a new neocloud!
On top of that - on recent Dwarkesh podcast, Dario ran through some napkin math on unit economics (he framed it all as industry math vs Anthropic specific - which is important, he wasn't disclosing anything Anthropic specific).
What he mentioned was take $100b of compute spend (he just picked a round number). There will be a mix shift of that spend between training and inference. Skew too much on training and you don't generate enough revenue. Skew too heavy on inference and you kneecap future R&D progress. He thought the industry is currently 50/50 on training / inference of compute spend. He said as in industry, could turn that $50b inference spend into $150b of revenue (called out these are most likely the unit economics of the industry in 1-2 years)
So taking this back to the Xai deal. Under above assumptions, Anthropic paying $5b / year. Let's say they turn that into $15b / year in rev (60-70% gross margin)
Win win!!
We’ve agreed to a partnership with @SpaceX that will substantially increase our compute capacity.
This, along with our other recent compute deals, means that we’ve been able to increase our usage limits for Claude Code and the Claude API.
Sinclair Ta retweeted
My AI investment thesis is that every AI application startup is likely to be crushed by rapid expansion of the foundational model providers.
App functionality will be added to the foundational models' offerings, because the big players aren't slow incumbents (it is wrong to apply the analogy of "fast startup, slow incumbent" here), they are just big. Far more so than with any other prior new technology, there is a massive and fast-moving wave that obsoletes every new app almost as fast as it can be invented. There is almost no time to build a company and scale it.
There are two ways AI application startup founders can make money:
- Make a flash-in-the-pan app that generates a ton of cash and bank the cash (my estimate is that you have about 12-18 months cashflow generation)
- Make a good enough app that you get acquired by one of the big players for sufficient equity
The situation is highly unstable - we don't know if it's going to crash or go to the moon but both scenarios make it very unlikely that any AI application startup will independently become a generational supercompany (baseline odds are low to begin with).
The best odds are finding an application niche in a highly specialized field with extremely unique and specific data barriers, ideally ones relating to real atoms (hardware or world-related) data and not software/finance.
Sinclair Ta retweeted
Claude Code and Codex do not replace Copilot and Cursor.
I've already heard multiple people make this argument, and I think it comes from the vibe-coding community because of the way they use these tools.
First, Claude Code and Codex are agentic coding tools. They are good at following instructions and generating a ton of code at once.
Second, you have Copilot, Cursor Tab, and similar AI assistants. They help with interactive development, where a human writes the code, and the tool autocompletes and suggests what to type next.
A way to think about this:
• Mode 1: AI writes the code, and the human copilots.
• Mode 2: The human writes the code, and AI copilots.
These two are very different. One doesn't replace the other.
Professional developers use both.
The IDE is still king.
Sinclair Ta retweeted
There’s so much content flying at us these days, it’s hard to separate the “this sounds smart!” from the “this is genuinely correct, helpful, and timeless.”
Below are seven essays that have had the most impact on my product career—that I find myself quoting from, sharing with people, and coming back to most often, even though most are decades old.
I’d love your help building out this list. What’s missing? Share a link in the comments. Bonus points for sharing stuff people may not have heard of.
(P.S. I’m not including books—yet. This is the beginning of an essential and timeless reading library specifically for product leaders.)
Sinclair Ta retweeted
One of our most requested resources within our venture assistance is help with pitching and storytelling -- here's the workshop on "Nailing your Fundraise" i gave our CEOs at our summit and full deck linked on our site
khoslaventures.com/nail-your…
Sinclair Ta retweeted
An attempt to explain (current) ChatGPT versions.
I still run into many, many people who don't know that:
- o3 is the obvious best thing for important/hard things. It is a reasoning model that is much stronger than 4o and if you are using ChatGPT professionally and not using o3 you're ngmi.
- 4o is different from o4. Yes I know lol. 4o is a good "daily driver" for many easy-medium questions. o4 is only available as mini for now, and is not as good as o3, and I'm not super sure why it's out right now.
Example basic "router" in my own personal use:
- Any simple query (e.g. "what foods are high in fiber"?) => 4o (about ~40% of my use)
- Any hard/important enough query where I am willing to wait a bit (e.g. "help me understand this tax thing...") => o3 (about ~40% of my use)
- I am vibe coding (e.g. "change this code so that...") => 4.1 (about ~10% of my use)
- I want to deeply understand one topic - I want GPT to go off for 10 minutes, look at many, many links and summarize a topic for me. (e.g. "help me understand the rise and fall of Luminar"). => Deep Research (about ~10% of my use). Note that Deep Research is not a model version to be picked from the model picker (!!!), it is a toggle inside the Tools. Under the hood it is based on o3, but I believe is not fully equivalent of just asking o3 the same query, but I am not sure.
All of this is only within the ChatGPT universe of models. In practice my use is more complicated because I like to bounce between all of ChatGPT, Claude, Gemini, Grok and Perplexity depending on the task and out of research interest.
Sinclair Ta retweeted
Windsurf CEO explains why traditional startup moats are mostly silly.
Varun said even with 50-100 of the best MIT engineers, that's still just "hundreds of engineering years" in your product. Someone else can build something similar in the same space.
He used Nvidia as an example. People think CUDA is their real moat, but that's not accurate.
Large companies like Google, OpenAI, and Anthropic spend tens of billions on chips. If CUDA didn't exist, they'd find a way to write assembly code and make GPUs work.
People use CUDA because Nvidia made it really capable at low floating-point math, great interconnect, and fast computers. Not because they're locked in.
Every year Nvidia has pressure - if they don't make hardware, interconnect, and memory bandwidth much faster, their profit margins shrink and AMD will compete.
For startups, the only moat is speed. Learning where the dead bodies are. Understanding what doesn't work and building compounding advantages through that knowledge.
Speed lets you learn from the market faster, which means you're first to the next idea too.
Sinclair Ta retweeted
We're now beginning the alpha-test phase of our new V7 image Model. It's our smartest, most beautiful, most coherent model yet. Give it a shot and expect updates every week or two for the next two months.
