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“Until you actually see things operationally, it’s going to be hard to build DoorDash from scratch.” Our CEO Yash Patil sat down with Andy Fang on why cheaper software doesn’t erase years of operating advantage. DoorDash’s moat is its proprietary data, edge cases, and hard-won knowledge, and increasingly, the... show more

Applied Compute

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12,083 views • 21 days ago •via X (Twitter)

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“50% of DoorDash’s agentic restaurant orders are going to places users have never ordered from before.” Andy Fang tells our CEO Yash Patil what happens when agents become the discovery layer. If models increasingly decide what gets surfaced and bought, companies have a strong reason to train and own that intelligence.

“50% of DoorDash’s agentic restaurant orders are going to places users have never ordered from before.” Andy Fang tells our CEO Yash Patil what happens when agents become the discovery layer. If models increasingly decide what gets surfaced and bought, companies have a strong reason to train and own that intelligence.

Applied Compute

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.Andy Fang Boris Cherny on why engineering leaders need to experience the full process of building and shipping again. You have to build with AI to understand where engineering is going and avoid being out of step with your teams. That bias for action is a big part of our culture at DoorDash

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Larry Ellison $ORCL highlighted something critical: models like ChatGPT, Gemini, Grok, and Llama are all trained on largely the same public internet data. When everyone trains on the same information, models inevitably converge. That’s why AI is moving toward commoditization. The real moat isn’t the model itself. It’s the proprietary data behind it. Companies that can train on exclusive datasets gain an advantage competitors can’t replicate. Having data that no one else has will allow you to dominate your market.

Larry Ellison $ORCL highlighted something critical: models like ChatGPT, Gemini, Grok, and Llama are all trained on largely the same public internet data. When everyone trains on the same information, models inevitably converge. That’s why AI is moving toward commoditization. The real moat isn’t the model itself. It’s the proprietary data behind it. Companies that can train on exclusive datasets gain an advantage competitors can’t replicate. Having data that no one else has will allow you to dominate your market.

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Easy money is a cure all, until it’s not. It’s hard to know when you have to put on the brakes and when things might pop. Today, the stock market is at its peak, gold is through the roof, and the wealth gap continues to increase. And in terms of what comes next, it looks like we’re going to see a significant easing of monetary policy along with less regulation. I recently sat down with CNBC's Andrew Ross Sorkin to discuss all of this and more. You can find the full video at the link in the comments.

Easy money is a cure all, until it’s not. It’s hard to know when you have to put on the brakes and when things might pop. Today, the stock market is at its peak, gold is through the roof, and the wealth gap continues to increase. And in terms of what comes next, it looks like we’re going to see a significant easing of monetary policy along with less regulation. I recently sat down with CNBC's Andrew Ross Sorkin to discuss all of this and more. You can find the full video at the link in the comments.

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🇺🇸Nick Shirley explains why cracking down on fraud is so difficult. "The thing with fraud is they want to make it so big and so complex that it’s hard to actually crack down on. And so when you have that much money going out to these locations, going out to these hospices, going out to these home health providers, it makes it very hard to crack down on.” Nick shirley , Shawn Ryan

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Mario Nawfal

19,835 views • 5 months ago

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.

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.

Aish

240,743 views • 1 year ago

AI doesn’t scale on mega-models — it scales on distilled skills at the edge. From Davos, Qualcomm's Nakul Duggal breaks down the real physics of edge AI: smaller, purpose-built models, mixed precision, and ruthless focus on power + cost efficiency. The biggest constraint is that if AI runs in robotics, ADAS, or edge automation, it has to be efficient. Watch the full conversation to see why edge AI outcomes depend on efficiency by design:

AI doesn’t scale on mega-models — it scales on distilled skills at the edge. From Davos, Qualcomm's Nakul Duggal breaks down the real physics of edge AI: smaller, purpose-built models, mixed precision, and ruthless focus on power + cost efficiency. The biggest constraint is that if AI runs in robotics, ADAS, or edge automation, it has to be efficient. Watch the full conversation to see why edge AI outcomes depend on efficiency by design:

Six Five Media

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.Ilya Sutskever on why OpenAI gave up on robotics: "Progress comes from the combination of compute and data. There was no path to data from robotics. I'd say that now it is possible to create a path forward, but one needs to really commit. You could imagine a gradual path of improvement where you build more robots, they do more things, you collect more data and so on. But you need to really love robots and need to be really willing to solve all the physical and logistical problems of dealing with them. It's not the same as software at all."

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Exclusive Interview with the CEO of KRAIN AI by Crypto Data Space Jared Stauffer sat down with Rabbitcoinhole to talk about what KRAIN is building, why it matters, and how you can be a part of it. Watch the full interview here:

Exclusive Interview with the CEO of KRAIN AI by Crypto Data Space Jared Stauffer sat down with Rabbitcoinhole to talk about what KRAIN is building, why it matters, and how you can be a part of it. Watch the full interview here:

KRAIN AI

31,963 views • 1 year ago

Gabbit Robotics co-founder Nicole Mattero on why the next AI winners won't fight over your screen, they'll fight to get closer to you: She kicks things off with what she admits is a hot take: "My hot take is that hardware is the only moat going forward because anyone's going to be able to spin up a model and make it really good and really cheap." Her logic is simple. Once a strong model is cheap and easy for anyone to build, the model alone can't set you apart. So what actually protects you? "Where does that leave you? Basically companies that have tactile modes such as hardware are the only ones that actually survive." Hardware, though, is only one piece of her argument. The second piece is data: "The models that win in the long run are the ones where private data is their [moat]." From there, Mattero ties the two together. Hardware gives a company one moat. The ability to gather unique personal data gives it another. Put both in place and the edge starts to multiply: "When you have a company that has hardware as a [moat], but then B is able to collect really unique personal data, that also becomes a [moat]." And that, she says, is the reason so many foundation model companies are reaching beyond the screen and into the physical world: "That's why I think a lot of these foundation models are like, let's get something in the home or on the person. It gives them an edge on both fronts."

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Big Brain AI

19,342 views • 3 months ago

Venture studios are quietly becoming one of the most effective models for company creation, but most people still don't understand what happens inside one. For this podcast episode I sat down with Andy Bilinsky, Partner at Interplay leading our Studio, to break down exactly how we build companies from the ground up. During our conversation, we covered: - What a venture studio actually is and how it differs from traditional VC - Why we research industries deeply before ever writing a business plan - The role of operating partners as true co-founders - Why second- and third-time founders are choosing to build with a studio instead of going solo - How Interplay went from 3 new companies in Q4 2025 to targeting 10+ in 2026 Big thanks to Andy for pulling back the curtain on how we build. Grateful to have him here at Interplay.

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Mark Peter Davis

54,633 views • 4 months ago

“don’t train your own model” is common ai advice. it's wrong. your token bill's the proof. today, we’re excited to launch castform into open preview. castform is the easiest way for you to train your own model, on your own data. open-weights models are performant and much cheaper. when trained on your task & proprietary data, they beat closed models. the thing standing between you and that was weeks of plumbing & years of ml expertise. with castform, model training is as simple as prompt engineering. castform bring your agent traces or raw corpora. castform turns it into training data, picks the right algorithmic recipes, manages gpus, and gives you an ide to watch and chat with your model as it learns. see what you can build with castform👇

“don’t train your own model” is common ai advice. it's wrong. your token bill's the proof. today, we’re excited to launch castform into open preview. castform is the easiest way for you to train your own model, on your own data. open-weights models are performant and much cheaper. when trained on your task & proprietary data, they beat closed models. the thing standing between you and that was weeks of plumbing & years of ml expertise. with castform, model training is as simple as prompt engineering. castform bring your agent traces or raw corpora. castform turns it into training data, picks the right algorithmic recipes, manages gpus, and gives you an ide to watch and chat with your model as it learns. see what you can build with castform👇

girish

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Larry Ellison basically explained why OpenAI is positioned to win the AI race. Today’s models all trained on the same thing: public data. The internet. Books. Papers. General knowledge. Impressive, but capped. Ellison’s point: peak value only comes when AI is trained on private data. That’s where OpenAI is moving: • partnerships with universities to work with unpublished research • experts teaching models real job tasks, not just theory • deep access to how science, medicine, law, and finance actually work Public data makes AI smart. Private data makes it decisive. The next AI leap won’t come from reading more of the internet. It’ll come from being invited behind the curtain. That’s the real moat.

Larry Ellison basically explained why OpenAI is positioned to win the AI race. Today’s models all trained on the same thing: public data. The internet. Books. Papers. General knowledge. Impressive, but capped. Ellison’s point: peak value only comes when AI is trained on private data. That’s where OpenAI is moving: • partnerships with universities to work with unpublished research • experts teaching models real job tasks, not just theory • deep access to how science, medicine, law, and finance actually work Public data makes AI smart. Private data makes it decisive. The next AI leap won’t come from reading more of the internet. It’ll come from being invited behind the curtain. That’s the real moat.

VraserX e/acc

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Paul Kedrosky on the money flooding into data centers: "You have this incredible flow of money purchasing things that's increasingly divorced from what's going on inside the data centers." "From the standpoint of most of the lenders I talk to, there could be hide and go seek competitions going on inside the data centers, and they wouldn't give a f***." → Listen: → Watch:

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Meb Faber

68,592 views • 20 days ago

We're going to build a city that everyone can afford — and with about 40 days until I become the next mayor of the city, we're hard at work on the transition. (And if that's something you want to be a part of, visit our website!)

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Zohran Kwame Mamdani

194,203 views • 10 months ago

"Are people going to build their own software, or buy software from vendors? Unequivocally, they're going to buy more software from vendors." - Okta CEO Todd McKinnon "Companies have a very hard time building something that goes from SAP to content management to Salesforce in an end-to-end process." "Maybe there’ll be some more customization and more vibe coding to build that. But the core vendors, core data sources, and the core business logic and processes, that’s going to get bigger and bigger. I’m 100% convinced of that."

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TBPN

12,518 views • 7 months ago

We think #GenAI will transform virtually every customer experience. That’s why we’re doubling down on all three layers of what’s called the GenAI “stack.” What do we mean by stack? Simple. Three layers, bottom to top. The “bottom layer” means the infrastructure. Think chips, servers, networking – the software, hardware, and computing power in our data centers used to train, build, and deploy AI models and applications. The “middle layer” is the GenAI sandbox – all the tools, Large Language Models (LLMs), and Foundation Models (FMs) we’ve built into Amazon Bedrock. This lets customers pick the #AI models that best fit their needs to easily and securely customize GenAI applications specific to their business. The “top layer” means the popular agents and assistants that people actually interact with – now including Amazon Q. These are built from the models in the middle layer – in Amazon Bedrock. While other apps might help you write a quick email, or edit an image, Amazon Q’s meant for taking care of business. It’s been engineered to be the most capable GenAI-powered assistant for speeding up software dev and leveraging companies’ internal data. Are you as bullish on the future of GenAI as we are? Listen to our CEO Andy Jassy talk about our investment across the stack.

We think #GenAI will transform virtually every customer experience. That’s why we’re doubling down on all three layers of what’s called the GenAI “stack.” What do we mean by stack? Simple. Three layers, bottom to top. The “bottom layer” means the infrastructure. Think chips, servers, networking – the software, hardware, and computing power in our data centers used to train, build, and deploy AI models and applications. The “middle layer” is the GenAI sandbox – all the tools, Large Language Models (LLMs), and Foundation Models (FMs) we’ve built into Amazon Bedrock. This lets customers pick the #AI models that best fit their needs to easily and securely customize GenAI applications specific to their business. The “top layer” means the popular agents and assistants that people actually interact with – now including Amazon Q. These are built from the models in the middle layer – in Amazon Bedrock. While other apps might help you write a quick email, or edit an image, Amazon Q’s meant for taking care of business. It’s been engineered to be the most capable GenAI-powered assistant for speeding up software dev and leveraging companies’ internal data. Are you as bullish on the future of GenAI as we are? Listen to our CEO Andy Jassy talk about our investment across the stack.

Amazon

47,421 views • 2 years ago

Our Llama-3.1-Nemotron-70B-Instruct model is a leading model on the 🏆 Arena Hard benchmark (85) from Arena. Arena Hard uses a data pipeline to build high-quality benchmarks from live data in Chatbot Arena, and is known for its predictive ability of Chatbot Arena Elo score as well as separability between helpful and less helpful models. Use our customized model Llama-3.1-Nemotron-70B to improve the helpfulness of LLM generated responses in your applications. 📥 Try on our API catalog: 📥 On GitHub: 📥 Or on Hugging Face:

Our Llama-3.1-Nemotron-70B-Instruct model is a leading model on the 🏆 Arena Hard benchmark (85) from Arena. Arena Hard uses a data pipeline to build high-quality benchmarks from live data in Chatbot Arena, and is known for its predictive ability of Chatbot Arena Elo score as well as separability between helpful and less helpful models. Use our customized model Llama-3.1-Nemotron-70B to improve the helpfulness of LLM generated responses in your applications. 📥 Try on our API catalog: 📥 On GitHub: 📥 Or on Hugging Face:

NVIDIA AI Developer

140,800 views • 1 year ago

1/6 Asheesh Birla | CEO at Evernorth sat down with Frank Chaparro on the GSR podcast to break down what it actually takes to move financial markets on-chain. Start here: why XRP? It moves tokenized value efficiently and has an exchange built into the ledger. But the real moat is the years of licensing and bank connections Ripple laid down. That connective tissue to the real world can't be shortcut. 🧵👇 This content is for informational purposes only and does not constitute investment advice. Digital assets involve risk, including potential loss of principal. Learn more about Evernorth:

1/6 Asheesh Birla | CEO at Evernorth sat down with Frank Chaparro on the GSR podcast to break down what it actually takes to move financial markets on-chain. Start here: why XRP? It moves tokenized value efficiently and has an exchange built into the ledger. But the real moat is the years of licensing and bank connections Ripple laid down. That connective tissue to the real world can't be shortcut. 🧵👇 This content is for informational purposes only and does not constitute investment advice. Digital assets involve risk, including potential loss of principal. Learn more about Evernorth:

evernorthxrp

52,610 views • 3 months ago

GLM 4.7 is one of the top open-source models on LM Arena—and it's going toe-to-toe with Claude Opus 4.5 and Gemini Pro. We sat down with Anastasios Nikolas Angelopoulos, co-founder and CEO of Arena.ai, to break down 8,000+ developer votes: → Within 30 points of Gemini Pro in math & coding → Frontier-level multi-turn & instruction following → The open-source model devs are actually switching to The best part? You can run it at 1,500+ tokens/sec on Cerebras—for free.

GLM 4.7 is one of the top open-source models on LM Arena—and it's going toe-to-toe with Claude Opus 4.5 and Gemini Pro. We sat down with Anastasios Nikolas Angelopoulos, co-founder and CEO of Arena.ai, to break down 8,000+ developer votes: → Within 30 points of Gemini Pro in math & coding → Frontier-level multi-turn & instruction following → The open-source model devs are actually switching to The best part? You can run it at 1,500+ tokens/sec on Cerebras—for free.

Cerebras

28,515 views • 7 months ago