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Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using...

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Aish

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240,743 Aufrufe • vor 1 Jahr •via X (Twitter)

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8 Kommentare

Profilbild von Raphael Schaad
Raphael Schaadvor 1 Jahr

@garrytan “proprietary data no longer provides companies with a competitive advantage in the AI era [IF that data is generally available and collectable]” (and I think that’s a pretty big IF to warrant such a blanket statement)

Profilbild von ᐸGerardSans/ᐳ🚀🇬🇧
ᐸGerardSans/ᐳ🚀🇬🇧vor 1 Jahr

These claims are not true at all. Better performance in benchmarks which do not imply better performance on other tasks besides the standardised tests used in post-training. This narrative from OpenAI has been challenged by recent research papers:

Profilbild von Dolapo Obat
Dolapo Obatvor 1 Jahr

Embodied labor is no longer a barrier. That’s both terrifying and liberating.

Profilbild von Aish
Aishvor 1 Jahr

yeah it is!

Profilbild von Jacob Eckel
Jacob Eckelvor 1 Jahr

Always assume that motivated reasoning is BS.

Profilbild von Shawn Chauhan
Shawn Chauhanvor 1 Jahr

AI shifts competitive edge from data to innovation

Profilbild von Milo
Milovor 1 Jahr

This does not sound believable.

Profilbild von deleuze
deleuzevor 1 Jahr

This just says data collection is better enabled by AI. Data moat is non-public info collected via the company ops, their ecosystem, and products. You need to then recreate a company’s infra, product, ecosystem etc to even collect that data Incomplete narrative at best

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Dave Lee

187,984 Aufrufe • vor 1 Jahr

🎉I’m excited to announce our $18M Series A, with participation from Venrex, The General Partnership ("TheGP") , Virtue , Caffeinated Capital , Array Ventures, GreatPoint Ventures , LiveRamp, Haystack, Also Capital, Life Extension Ventures , Circle & Co, and WS Investments. The first generation of AI was built on public, human-curated data. The next will be built on real-world data. The datasets carrying real human behavior are what now separate one AI system from another. AI companies want them. The companies that hold them want to put them to work. The barrier has always been risk. We solved that risk in healthcare, one of the most regulated data environments there is. Today we bring it to the rest of the economy.
1:57

🎉I’m excited to announce our $18M Series A, with participation from Venrex, The General Partnership ("TheGP") , Virtue , Caffeinated Capital , Array Ventures, GreatPoint Ventures , LiveRamp, Haystack, Also Capital, Life Extension Ventures , Circle & Co, and WS Investments. The first generation of AI was built on public, human-curated data. The next will be built on real-world data. The datasets carrying real human behavior are what now separate one AI system from another. AI companies want them. The companies that hold them want to put them to work. The barrier has always been risk. We solved that risk in healthcare, one of the most regulated data environments there is. Today we bring it to the rest of the economy.

Shubh Sinha

108,648 Aufrufe • vor 3 Monaten

Big pharma just handed the AI industry one of the most important reality checks of 2026 (Save this). david friedberg revealed that Anthropic approached major life sciences companies with a pitch, share your proprietary data, sign an NDA and we will give you early access to a specialized life sciences model and nearly every company they spoke with said no. Here is what these pharma companies understood that many enterprises still have not. A large pharmaceutical company may have spent decades and tens of billions of dollars generating proprietary datasets, clinical trial results, genomic sequences, drug interaction data, compound libraries. That data is the business and the competitive moat that separates them from every other player in the industry lives in those datasets. Handing it to an AI lab in exchange for early access to a model is essentially handing your most valuable asset to a company whose entire business model depends on combining your data with everyone else's and then selling the output back to you and to your competitors. Palantir CEO Alex Karp made this exact point that enterprise leaders are paying for AI tokens that generate no tangible business value while simultaneously surrendering their most sensitive operational data to external providers. He called this transferring a company's alpha, the unique advantage that secures the business directly to a third-party lab. Microsoft CEO Satya Nadella echoed the same concern independently, warning that entire sectors might find their accumulated knowledge commoditized if they do not build their own data and model ownership layers. The structural problem is not unique to pharma but it applies to every enterprise sector. Every time an employee runs a query through a third-party frontier model, proprietary workflows, customer data, and strategic processes pass through infrastructure the enterprise does not control. The data already shows the market moving, Open-source captured 67% of all AI tokens processed in the first half of 2026, up from a fraction of that just twelve months earlier. The performance gap between proprietary frontier models and open-source alternatives has nearly closed, DeepSeek costs approximately 1/36th of GPT-5 for comparable workloads. What pharma figured out and what enterprises across every sector are starting to realize is that the model is not the moat but the data is. And once you hand your data to a model company, you have permanently surrendered the asset that took you decades and billions of dollars to build.
3:21

Big pharma just handed the AI industry one of the most important reality checks of 2026 (Save this). david friedberg revealed that Anthropic approached major life sciences companies with a pitch, share your proprietary data, sign an NDA and we will give you early access to a specialized life sciences model and nearly every company they spoke with said no. Here is what these pharma companies understood that many enterprises still have not. A large pharmaceutical company may have spent decades and tens of billions of dollars generating proprietary datasets, clinical trial results, genomic sequences, drug interaction data, compound libraries. That data is the business and the competitive moat that separates them from every other player in the industry lives in those datasets. Handing it to an AI lab in exchange for early access to a model is essentially handing your most valuable asset to a company whose entire business model depends on combining your data with everyone else's and then selling the output back to you and to your competitors. Palantir CEO Alex Karp made this exact point that enterprise leaders are paying for AI tokens that generate no tangible business value while simultaneously surrendering their most sensitive operational data to external providers. He called this transferring a company's alpha, the unique advantage that secures the business directly to a third-party lab. Microsoft CEO Satya Nadella echoed the same concern independently, warning that entire sectors might find their accumulated knowledge commoditized if they do not build their own data and model ownership layers. The structural problem is not unique to pharma but it applies to every enterprise sector. Every time an employee runs a query through a third-party frontier model, proprietary workflows, customer data, and strategic processes pass through infrastructure the enterprise does not control. The data already shows the market moving, Open-source captured 67% of all AI tokens processed in the first half of 2026, up from a fraction of that just twelve months earlier. The performance gap between proprietary frontier models and open-source alternatives has nearly closed, DeepSeek costs approximately 1/36th of GPT-5 for comparable workloads. What pharma figured out and what enterprises across every sector are starting to realize is that the model is not the moat but the data is. And once you hand your data to a model company, you have permanently surrendered the asset that took you decades and billions of dollars to build.

Milk Road AI

16,317 Aufrufe • vor 3 Monaten

Is the future of AI $NEAR … ? OpenAI is racing toward a $1T IPO, quickly becoming a centralized beast of epic proportions. But what happens to your data in that world?? As chronear explains, “We can't let centralized actors determine how they store or how they use user data. It should be the users who actually control the custody or use of their data." NEAR AI is building a truly open AI, open source, where users control their data and store their data locally. What AI do you use daily? And would you switch if a better decentralized option existed?
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Is the future of AI $NEAR … ? OpenAI is racing toward a $1T IPO, quickly becoming a centralized beast of epic proportions. But what happens to your data in that world?? As chronear explains, “We can't let centralized actors determine how they store or how they use user data. It should be the users who actually control the custody or use of their data." NEAR AI is building a truly open AI, open source, where users control their data and store their data locally. What AI do you use daily? And would you switch if a better decentralized option existed?

Bri Teresi

25,101 Aufrufe • vor 6 Monaten