Josh Klein

Investing in and writing about AI.
Formerly: @bcg, @ripple

[Josh Klein @klezus]

I just dropped part 1 of my deep dive on the frontier model labs. Take a look if you want a head start on how to think about this layer of the AI stack as Anthropic and OpenAI gear up for their IPOs https://t.co/n2fSvd249L

[Josh Klein @klezus]

@NYCMayor This is a win for New Yorkers and post ideologues alike. Proof that you can care about quality of life and affordability AND want good value for your tax dollars!

[Midnight Capital @Midnight_Captl]

“Yeahh I dumped my $NVDA to go buy the next bottleneck in the supply chain. Ticker symbol is $0028.0006 it trades on the Sri Lankan stock exchange. They make some kind of glue or something man i don’t even know haha, but it’s a big bottleneck i heard”

[Josh Klein @klezus]

@firstadopter Cc: @pruuuie plz fix

[Josh Klein @klezus]

It's going to be interesting to see how companies test candidates' ability to use AI efficiently and effectively. I feel like somebody has to have a startup working on this…

[Josh Klein @klezus]

@a16z Cc: @MayorofSeattle

[Josh Klein @klezus]

@AlexRoseJo @AndrewYNg What is unfair about recognizing outstanding with an A and median with a B?

You might as well let combo of curriculum + performance track record mean something or else just get rid of grades all together

[Aaron Levie @levie]

What’s happened is that we went from AI chat tools that were relatively cheap and had small context windows, to AI agents that have giant context windows, the ability to keep track of longer running work, and models that cost an order of magnitude more on inference because they’re that much better.

This has compounded far faster than most realized (unless you were paying close attention at the middle or end of last year, which many here were), and the dollars flowing in now are much more real.

What follows is a continued march of AI capability that will continue to be used by anyone with a frontier use-case (like coding, sciences, finance, consulting) and then a peeling off of tasks to lower cost models that are capable enough for the job. Whereas we thought the cost of AI might converge on a single low price per token before, it’s clear the stratification is only widening based on the task you need performed.

This will be yet another component that has to be figured out for broad AI diffusion. Enterprises will need to put in programs, new finance teams, and technology solutions to manage this all. The labs and platforms that can ensure customers can price optimize for the task at hand will be in the best position.

[Josh Klein @klezus]

If you asked 10 people what an "AI agent" is, I bet you'd get almost as many unique answers

[Josh Klein @klezus]

@learncurvesai @AndrewYNg Maybe for technical roles and for the best resourced employers but so many positions, interviews start through who you know/a point of connection to someone at the company and end with whether or not the candidate relates well to the interviewer

[Josh Klein @klezus]

@a16z The barrier to build continues to fall!

[Josh Klein @klezus]

@nickclement This is a real cost of meta layoffs… between the level of distraction and the morale issues caused by such a widely watched layoff

[Josh Klein @klezus]

@GergelyOrosz This is completely wrong. Meta needs to free up cash to fun investments. Also they don't need all these employees. I wouldn't be surprised if this screen tracking initiative expose a lot of employees who have not been working even close to a 9-to-5

[Josh Klein @klezus]

@redtachyon It's called employment at will. I remember when hedge funds were going around parks in the middle of the day in Austin asking people which tech companies they worked for!

[Josh Klein @klezus]

@Incenzee @redtachyon There are lots of prestigious places that are explicitly up or out: everything from the Marines to consulting firms and investment banks

[Josh Klein @klezus]

@Midnight_Captl Do you know if these companies are actively working on competitive memory solutions?

[Josh Klein @klezus]

@dee_bosa @ReflectionAI_ The assumption is not that nobody can catch them but that OpenAI and Anthropic will figure out a way to create competitive moats. With LLMs commoditized everywhere but the frontier, vertical integration, application layer businesses, and deep tech innovation become the playbook

[Josh Klein @klezus]

Half of $NVDA rev comes from hyperscalers who are actively in-housing chip design in order to reduce dependence on NVDA and capture that ~70% gross margin themselves. Credible alternatives becoming available the largest leading-edge chip buyers will worsen NVDA’s leverage as a seller. This is the bear case but it’s what’s priced in

[Josh Klein @klezus]

$NVDA's earning just now. Exceptional quarter and good signal for broader AI industry.

Here are my initial top takeaways heading into the call:

1. Demand is very strong, evidenced by a comfortable beat on top line estimates ($81.6B vs. $79B consensus) and great outlook for next quarter

2. Progress diversifying its revenue base from hyperscalers - this is a master maneuver and a strong decision to segment out.ACIE (AI Clouds, Industrial & Enterprise) from hyperscalers so the market can track progress as the concentration risk eases. Look for commentary here on the earning call

3. Record guidance assumes zero Hopper shipments to China. With that market still locked and that baked into guidance, any movement on that front is pure upside

4. They're printing cash ($50.3B operating CF) and returning capital to shareholders via a dividend hike ($0.01 -> $0.25 per share) and an $80B buyback.