Dan Darnell @dkdarnell - Twitter Profile

Dan Darnell

@dkdarnell

5 days ago

What an amazing program. I am so glad we can do this again this Summer.

DatologyAI @datologyai

5 days ago

1/ 🌞 Our Summer of Data Seminar brought together some of the sharpest minds in data curation last year. We are bringing it back in 2026! Let's recap the great talks from 2025!

datologyai's tweet photo. 1/ 🌞 Our Summer of Data Seminar brought together some of the sharpest minds in data curation last year. We are bringing it back in 2026! Let's recap the great talks from 2025! https://t.co/wddwTQMu8C

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Ari Morcos

@arimorcos

7 days ago

Most companies that say they want to own their model are going to fail at it. I like the conversation about "owning vs renting" intelligence. It's the right frame, and it's about to become the defining decision for most companies, because AI will be core for pretty much everyone, even if they don't realize it yet. For those where the model is the business today, these questions hit close to home. Renting was the right call for the last three years. Call an API, ship, don't think about infrastructure. But the ground is shifting. The frontier is quietly closing up. Meta moved its newest flagship work to closed models under its Superintelligence Lab, and the strongest Chinese models, like Qwen's top tier, are now API-only. Open weights aren't going away, but counting on the best ones being there for you isn't a safe bet anymore. And the closed labs are compute-constrained enough that access itself is becoming something you reserve years in advance: OpenAI is already selling multi-year "Guaranteed Capacity" contracts. So serious companies are deciding to own their models rather than rent them. Here's where almost everyone gets it wrong. They treat it as a compute problem, or a talent problem: get the GPUs, hire the team, and you can build a great model. They line up the compute, put a date on the calendar for the model, and then hit the real blocker. Data. Their proprietary data isn't ready for training. There isn't enough data to train on in the areas they really care about. That's the part almost nobody budgets for: data quality in a shape that can train the model your business needs. Getting the data right also flips the economics that we have come to expect from the past three years. A small, domain-specific model built on the right data can go toe-to-toe with the best the frontier labs can build, and you keep your data, own your roadmap, control your costs, and build a moat that's actually yours. The future worth betting on isn't three labs renting the same model to everyone. It's thousands of companies building their own domain-specific models, each better at its job than any general model could be. The frontier used to look like the shining house on the hill. Lately, it looks more like a landlord, happy to keep you renting as long as you never price out what owning could really look like.

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DatologyAI @datologyai

8 days ago

A spicy take from @arimorcos on @jacobeffron's Unsupervised Learning: frontier APIs may not always be there. The teams that can build their own models won't be exposed when that happens.

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Ari Morcos

@arimorcos

11 days ago

These are always a blast! My spicy prediction: frontier model APIs may go away. Not as a intentional business decision, but purely because labs will prioritize first-party products over the third-party API under severe compute constraints. If you can't count on frontier or open models being there, the move is owning the ability to build your own.

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