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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... their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.show more

Aish

4,366 subscribers

240,704 views • 1 year ago •via X (Twitter)

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8 Comments

Raphael Schaad1 year ago

@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)

ᐸGerardSans/ᐳ🚀🇬🇧1 year ago

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:

Dolapo Obat1 year ago

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

Aish1 year ago

yeah it is!

Jacob Eckel1 year ago

Always assume that motivated reasoning is BS.

Shawn Chauhan1 year ago

AI shifts competitive edge from data to innovation

Milo1 year ago

This does not sound believable.

deleuze1 year ago

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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Bri Teresi

25,101 views • 4 months ago

60% of companies expect to be transformed by AI within two years. But here's the reality: most are still bolting AI onto legacy workflows instead of reimagining how work gets done. That's not transformation—that's just expensive automation. Our latest research reveals what separates AI leaders from laggards. The companies winning with AI aren't just adopting new tools—they're redesigning how their business creates value from the ground up. The challenge: Most organizations are nowhere near putting AI at the center of their strategy. They're missing the exponential gains that come from true AI-first thinking. The solution: A complete playbook for IT leaders and CIOs ready to move at the speed of AI. 🎯 5 Core Principles of AI-First Companies: 1️⃣ AI as a capability expander — Enable work that wasn't possible before, not just faster versions of old tasks 2️⃣ Human-AI partnership — Augment human creativity and decision-making rather than replacing it 3️⃣ AI-native design — Build systems that seamlessly integrate thousands of AI agents working behind the scenes 4️⃣ Trust & governance foundation — Remember: "AI agents can't keep a secret"—never rely on AI to maintain core security models. 5️⃣ Data as strategic fuel — Transform unstructured content into your competitive advantage The companies implementing these principles today are building insurmountable competitive advantages. They're not just working faster—they're working in entirely new ways. Ready to lead your industry's AI transformation? 📖 Get the complete framework Want to explore the benefits? Get the full details here:

60% of companies expect to be transformed by AI within two years. But here's the reality: most are still bolting AI onto legacy workflows instead of reimagining how work gets done. That's not transformation—that's just expensive automation. Our latest research reveals what separates AI leaders from laggards. The companies winning with AI aren't just adopting new tools—they're redesigning how their business creates value from the ground up. The challenge: Most organizations are nowhere near putting AI at the center of their strategy. They're missing the exponential gains that come from true AI-first thinking. The solution: A complete playbook for IT leaders and CIOs ready to move at the speed of AI. 🎯 5 Core Principles of AI-First Companies: 1️⃣ AI as a capability expander — Enable work that wasn't possible before, not just faster versions of old tasks 2️⃣ Human-AI partnership — Augment human creativity and decision-making rather than replacing it 3️⃣ AI-native design — Build systems that seamlessly integrate thousands of AI agents working behind the scenes 4️⃣ Trust & governance foundation — Remember: "AI agents can't keep a secret"—never rely on AI to maintain core security models. 5️⃣ Data as strategic fuel — Transform unstructured content into your competitive advantage The companies implementing these principles today are building insurmountable competitive advantages. They're not just working faster—they're working in entirely new ways. Ready to lead your industry's AI transformation? 📖 Get the complete framework Want to explore the benefits? Get the full details here:

Box

128,566 views • 1 year ago

Training robot foundation models faces two key hurdles: how to get enough data to train an effective model, and how to make sure that new skills can be acquired quickly. The team at Rhoda AI believes that the answer is training Direct Video Action models from web data. Web data is plentiful, to the point where Rhoda can train their base model on hundreds of years of video data. And then, with the addition of robot data, they can quickly adapt it to new tasks with as little as 20 hours of in-domain data, performing complex, multi-step manipulation tasks with their purpose-built video foundation model. Tongzhou Mu 🤖🦾🦿 Eric Chan and Changan Chen joined us to talk more about their approach. Watch Episode #79 of RoboPapers, with Michael Cho - Rbt/Acc, Chris Paxton, and Jiafei Duan, to learn more!

Training robot foundation models faces two key hurdles: how to get enough data to train an effective model, and how to make sure that new skills can be acquired quickly. The team at Rhoda AI believes that the answer is training Direct Video Action models from web data. Web data is plentiful, to the point where Rhoda can train their base model on hundreds of years of video data. And then, with the addition of robot data, they can quickly adapt it to new tasks with as little as 20 hours of in-domain data, performing complex, multi-step manipulation tasks with their purpose-built video foundation model. Tongzhou Mu 🤖🦾🦿 Eric Chan and Changan Chen joined us to talk more about their approach. Watch Episode #79 of RoboPapers, with Michael Cho - Rbt/Acc, Chris Paxton, and Jiafei Duan, to learn more!

RoboPapers

24,475 views • 3 months ago

.Josh Wolfe: Anybody Using DeepSeek App Is 'Absolute Fool' "Anybody using the DeepSeek app is an absolute fool. If you're using DeepSeek on companies like Together Compute, one of Lux's companies, which can get rid of the CCP censorship, then it's probably okay. But remember, the open-source movement is something we deeply believe in. Most great technologists, entrepreneurs, and venture capitalists are on the side of open source. The closed-source models that have consumed tens of billions of dollars are the ones that are really going to be at risk. When you look at Hugging Face, a major repository, or Together Compute, Runway ML, and a lot of Lux's companies, they have been pioneers in open source. Now, why am I not worried about open source, even with the DeepSeek model? As long as you don't have the CCP censorship on it, the models with their open weights allow people to run on their proprietary data. This means companies like pharma or defense companies that have their own siloed, proprietary data—think about Bloomberg with their proprietary longitudinal data, or Meta with their data—are the ones who will have the edge. Even as open source takes hold, these companies will still dominate. I’m not worried about open source being the problem. I’m more concerned about people overfunding closed models with no proprietary source. A lot of capital is going to be burned there, and we’re already seeing that with people worried about OpenAI in some aspects."

.Josh Wolfe: Anybody Using DeepSeek App Is 'Absolute Fool' "Anybody using the DeepSeek app is an absolute fool. If you're using DeepSeek on companies like Together Compute, one of Lux's companies, which can get rid of the CCP censorship, then it's probably okay. But remember, the open-source movement is something we deeply believe in. Most great technologists, entrepreneurs, and venture capitalists are on the side of open source. The closed-source models that have consumed tens of billions of dollars are the ones that are really going to be at risk. When you look at Hugging Face, a major repository, or Together Compute, Runway ML, and a lot of Lux's companies, they have been pioneers in open source. Now, why am I not worried about open source, even with the DeepSeek model? As long as you don't have the CCP censorship on it, the models with their open weights allow people to run on their proprietary data. This means companies like pharma or defense companies that have their own siloed, proprietary data—think about Bloomberg with their proprietary longitudinal data, or Meta with their data—are the ones who will have the edge. Even as open source takes hold, these companies will still dominate. I’m not worried about open source being the problem. I’m more concerned about people overfunding closed models with no proprietary source. A lot of capital is going to be burned there, and we’re already seeing that with people worried about OpenAI in some aspects."

Josh Caplan

39,985 views • 1 year ago