danny adkins

@TheRealAdamG @courtne +1 these people are forces of nature

I can assure you that the talent density on the GTM team at OpenAI is unlikely anything you or I've seen in our careers. I mean that humbly. It's the best of the best. The GTM org is made of the top sellers, top engineers, top architects, top CSMs, etc, at their former companies. They are because they were so good and selling (and servicing). I'm may be biased (because I work there), but I am not wrong. Also, yes, AI is an awesome thing to sell for many reasons. It's a super fun job.

openai and anthropic probably have terrible sales reps they're talented, but they've never actually had to sell anything. ben horowitz said it best in a recent conversation: "right now with openai and anthropic, everybody wants to buy ai. they're already predisposed to buy." that's order-taking, not selling. let's zoom in on this distinction. 1) the order-taker problem cloudflare's CEO admitted in 2023 their product was so good that "many of our sales team succeeded largely by just taking orders." deals were like "fish jumping right in the boat." then the economy shifted and they fired 100 salespeople who'd contributed just 4% of new business. when your product sells itself, mediocre reps look like rockstars. they crush quota, win the president's club, and get promoted into leadership. nobody knows they can't actually sell until the fish stop jumping in the boat. 2) why hard sells matter ben won't shut up about ptc, a 90s cad/cam company. the product "wasn't that great." "the windshield wiper didn't work." but that forced discipline. you had to map accounts systematically, lay traps for competitors, and build airtight technical cases. his favorite hire was ryan gabrisco at databricks, who came from a company selling secure ftp as a public company. think about how good you have to be to make quota selling that. when ben hires sales leaders, he looks for people from companies where the product was hard to sell because that's the only way to test if someone can actually sell. 3) what happens when markets turn every hot market eventually cools. i'll give you a few examples. salesforce in 2001. facebook ads in 2012. aws in 2015. the order-takers got exposed every time. modern AI sales reps don't know how to qualify prospects who aren't already sold or how to systematically lock out competitors or how to build pipeline when inbound dries up. ben's story about hiring at Okta: two candidates, one super enthusiastic, the other said "let me talk to your customers first." ben told the ceo: "you want the guy qualifying YOU. that's what good salespeople do." 4) openai scaled their sales team from 10 to 500 people in under two years. anthropic is scaling fast too. but how would anyone know if they're good? you can't test sales ability when customers are lined up begging to buy. when real competition arrives, the kind where enterprises have three viable options and care about pricing, support, and vendor risk, AI companies will discover which GTM leaders can actually sell and which ones were just processing waitlists. 5) how to hire right if you're building a GTM team right now, think like a value investor. resumes don't matter. look for human capital that the market has significantly underpriced. someone who's had to sell a product that didn't sell itself, someone who's built discipline through necessity, not abundance (no order-takers). find the person who sold enterprise software at a company nobody's heard of. find the person who had to fight for every deal because the competitor was already embedded in the account. the person who figured out how to systematically lock out competition even when they were the underdog. those skills matter. for AI companies, the question is whether they can close deals when the market shifts. because when inbound dries up (it always does), you'll discover who can actually sell.

@felixrieseberg It'll be tough for Anthropic to keep this lead though. The winning model will be a really fast one, and good at multi-agent communication (for scaling both context and instances), and one with the most compute infra. The other labs have structural advantages here.

@felixrieseberg I do think the implications for devices are pretty massive. If you can have an agent that uses all your apps for you remotely, there may not be a reason for an iPhone anymore. And devices can come in any form, any interface.

wow. maybe one of the best products ever launched, and the start of the great hardware unbundling soon, we'll have armies of computer users w persistent storage. we'll have organizations of long-running coworkers. we'll be able to talk to them while we sit by a campfire

Love codex subagents. Huge kudos to @polynoamial @thsottiaux and team. If you thought the world was running out of compute before...

just figure out how to define your problem and throw the max amount of useful test time compute at it

harness engineering is the most useful skill of the year

@jxmnop @karpathy Not disdain, and not sure why you’re comparing to a median employee, when the counterfactual is Andrej working at OpenAI

One counterbalance will be that some advances from automated research will be rapidly diffused - if Lab A discovers a new algorithm, it probably will leak to Lab B rather quickly. But this doesn’t fully counteract these dynamics, since many advances will be codesigned for each lab’s infra, model particularities, and individual strategies, and thus won’t be easily transferable.

While compute advantages thus far have come out in the wash (having 2x the compute hasn’t proved much more important, as Dario observes in Dwarkesh episode), a sharper curve likely means more monopolistic market dynamics.

Automated AI research will lead to new scaling laws. When you have 10x the compute, and 10x the number of automated researchers/engineers, I’d bet returns will steeper than pretraining and RL, given the parallelizability of AI research and compounding.

ARC AGI will eventually turn into a captcha company

@fchollet Can you name the core types of problems for which you think there’s a clear gap, and which you expect to be the last to get solved? Physical causal reasoning & long horizon are the two important types of benchmarks I could see taking a bit of time. But they don’t seem far.

Reaching AGI won't be beating a benchmark. It will be the end of the human-AI gap. Benchmarks are simply a way to estimate the current gap, which is why we need to continually release new benchmarks (focused on the remaining gap). Benchmarking is a process, not a fixed point. We can say we have AGI when it's no longer possible to come up with a test that evidences the gap. When it's no longer possible to point to something that regular humans can do and AI can't. Today, it's still easy. I expect it will become nearly impossible by 2030.

@bilaltwovec @IsomorphicLabs Incredible

very belated update: i joined @isomorphiclabs right after graduating early last year—its been really incredible getting to work on pushing the frontier of scaling ai for science for very very difficult problems (solve all disease) in the real world!

@polynoamial @VictorTaelin @METR_Evals Already becoming a challenge (per @testingham)

@VictorTaelin Yes. I think by the end of the year the main challenge for @METR_Evals will be measuring horizons that long.

When GPT-5 was released, some folks claimed AI progress was hitting a wall, whereas others said progress would continue. GPT-5.2 was released 2 months ago. GPT-5.3-Codex was released 2 days ago and is twice as token efficient for coding. It's clear who turned out to be correct.

Today will go down as some kind of turning point. Somewhat arbitrarily, but it is OK if journalists and historians have to present things in that manner.

@LM_Braswell @scale_AI calvin is the goat

Across basically all scaling early-stage startups I know (even now in the age of AI codegen) - there is always an unsung hero infra/security engineer who is a key part of fixing every severe outage. Early days @scale_AI it was Calvin Huang. Who are other good examples of this?

I'm proud to have had @JeffDean as my lead at Google for the last 10 years. He is one of the few people of his stature in our industry always willing to stand up and say the obvious. We can't let this administration scare us away from acknowledging the reality we are living in. It's time to stand up and use our voices to end this.