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On The Information's TITV talking about where open weights actually land inside a big company Nobody serious is picking one model. They'll ride the frontier of intelligence(rent) and most will train OSS models(own) on their own data for the work nobody else can do. For many critical end to... show more

Garrett Lord

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90,679 次观看 • 16 天前 •via X (Twitter)

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$Enterprise AI is in a wildly paradoxical state 🤔, and we’re reaching the inflection point that will resolve it. 💥 Enterprises want to differentiate with AI, yet rent the same intelligence as their competitors. Their workflows and expertise are highly specialized, yet they rely on generic models built to be good at everything. They worry about AI costs, yet pay premium prices for massive models where only a fraction (1%) of the intelligence is relevant to their task. 💸 And they demand control and sovereignty, yet rent the intelligence becoming core to their business. This is not a sustainable equilibrium. The next era of enterprise AI is specialized intelligence companies build, own, and compound. And we are at the inflection point of this transition. That’s the bet we made when we started Oumi two years ago. Today we’re closing the loop: Oumi can now not only automatically build your specialized AI models, but also deploy them into production, learn from their production experience, and continuously improve them. The intelligence that your business runs on, becomes your differentiator. Your compounding advantage. The winners of the next AI era will turn their own data, expertise, and experience into specialized intelligence that nobody else can rent. Don’t rent your AI. Build it. Own it. Compound it.$

Enterprise AI is in a wildly paradoxical state 🤔, and we’re reaching the inflection point that will resolve it. 💥 Enterprises want to differentiate with AI, yet rent the same intelligence as their competitors. Their workflows and expertise are highly specialized, yet they rely on generic models built to be good at everything. They worry about AI costs, yet pay premium prices for massive models where only a fraction (1%) of the intelligence is relevant to their task. 💸 And they demand control and sovereignty, yet rent the intelligence becoming core to their business. This is not a sustainable equilibrium. The next era of enterprise AI is specialized intelligence companies build, own, and compound. And we are at the inflection point of this transition. That’s the bet we made when we started Oumi two years ago. Today we’re closing the loop: Oumi can now not only automatically build your specialized AI models, but also deploy them into production, learn from their production experience, and continuously improve them. The intelligence that your business runs on, becomes your differentiator. Your compounding advantage. The winners of the next AI era will turn their own data, expertise, and experience into specialized intelligence that nobody else can rent. Don’t rent your AI. Build it. Own it. Compound it.

Manos Koukoumidis

1,006,622 次观看 • 1 个月前

Gavin Baker says the future for the world's biggest companies is open models and private context: "I think the future is an ensemble of models. There's a Pareto curve. No one model is going to be the best at everything." "For the global 1,000 biggest companies, you're going to take whatever the best open-source model is. I think probably in the very near future, that's going to be an Nvidia model." "Everybody says 'Well, in a world where open-source wins, who funds the training?' Chip companies can fund the training." "It's trivial to do a $50 to $100 billion training run for Jensen, but I do think you're going to see American open-source led by Nvidia get really close to the frontier." "Sharing your own enterprise context, that's truly your IP, that's truly the value of your company, the context embedded in all of your data, sharing that with a frontier lab may be hazardous for your financial health." "I think what you'll see these companies do is they'll have their own model on their data, and it will work with one or two other frontier models, checking each other... I think that feels like a very likely future to me." Gavin Baker David George

Gavin Baker says the future for the world's biggest companies is open models and private context: "I think the future is an ensemble of models. There's a Pareto curve. No one model is going to be the best at everything." "For the global 1,000 biggest companies, you're going to take whatever the best open-source model is. I think probably in the very near future, that's going to be an Nvidia model." "Everybody says 'Well, in a world where open-source wins, who funds the training?' Chip companies can fund the training." "It's trivial to do a $50 to $100 billion training run for Jensen, but I do think you're going to see American open-source led by Nvidia get really close to the frontier." "Sharing your own enterprise context, that's truly your IP, that's truly the value of your company, the context embedded in all of your data, sharing that with a frontier lab may be hazardous for your financial health." "I think what you'll see these companies do is they'll have their own model on their data, and it will work with one or two other frontier models, checking each other... I think that feels like a very likely future to me." Gavin Baker David George

a16z

39,947 次观看 • 1 个月前

“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

463,082 次观看 • 3 个月前

Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.

Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.

Vikram M

12,078 次观看 • 2 个月前

.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

Jawwwn

1,105,076 次观看 • 22 天前

One of the genuine videos on the internet where people are actually working at the company. Many people misguide students that there is no work at FAANG and big MNC and most employees enjoy and play TT all day. It's not true.

One of the genuine videos on the internet where people are actually working at the company. Many people misguide students that there is no work at FAANG and big MNC and most employees enjoy and play TT all day. It's not true.

Asmit

30,325 次观看 • 1 个月前

Mistral CEO Arthur Mensch on the need for open-source AI: “If you assume that the entire economy is going to run on AI systems, enterprises will just want to make sure that nobody can turn off their systems.” “If you treat intelligence as electricity, then you just want to make sure that your access to intelligence cannot be throttled.” "The only way in which you can create systems that are effectively using the folklore knowledge of your employees, the knowledge that you've accrued for decades... is to create your own models based on those open-source models." “This is a technology which is so important, that you don’t want to be locked into a single vendor.” Arthur Mensch on Big Technology Podcast

Mistral CEO Arthur Mensch on the need for open-source AI: “If you assume that the entire economy is going to run on AI systems, enterprises will just want to make sure that nobody can turn off their systems.” “If you treat intelligence as electricity, then you just want to make sure that your access to intelligence cannot be throttled.” "The only way in which you can create systems that are effectively using the folklore knowledge of your employees, the knowledge that you've accrued for decades... is to create your own models based on those open-source models." “This is a technology which is so important, that you don’t want to be locked into a single vendor.” Arthur Mensch on Big Technology Podcast

a16z

288,368 次观看 • 8 个月前

America is about to lose the AI race, and it will not happen at the frontier. It will happen at the floor. Everyone is watching who ships the smartest model. The actual war is over the 80% of tokens nobody posts about: the routine inference that quietly runs the world. On current trajectory, that fight is already lost. Watch what people do, not what they say. Coinbase just defaulted its own engineers off frontier models onto open weights and cut AI spend nearly in half while usage kept climbing. Even NVIDIA runs a closed frontier model as an orchestrator and pushes the volume to its own open weights. The frontier is becoming a router. The volume goes open. That part is settled. Here is the part that should terrify Washington: the only credible open tier today is Chinese. GLM. Kimi. And the US answer is to tighten export controls and freeze its own labs in place, as if you can embargo a file that is already downloaded, or price-match free. So China hands the Global South Huawei hardware and free open models, and a generation in Africa and Southeast Asia learns to reason through a model that will not tell them what happened at Tiananmen Square. That is not a cost story. That is influence through inference. You do not have to win hearts and minds when you supply the mind. Open source is not a nice-to-have for America. It is the whole ballgame for the 80%, and right now the US is barely on the field. We need American open weights. Not eventually. Now.

America is about to lose the AI race, and it will not happen at the frontier. It will happen at the floor. Everyone is watching who ships the smartest model. The actual war is over the 80% of tokens nobody posts about: the routine inference that quietly runs the world. On current trajectory, that fight is already lost. Watch what people do, not what they say. Coinbase just defaulted its own engineers off frontier models onto open weights and cut AI spend nearly in half while usage kept climbing. Even NVIDIA runs a closed frontier model as an orchestrator and pushes the volume to its own open weights. The frontier is becoming a router. The volume goes open. That part is settled. Here is the part that should terrify Washington: the only credible open tier today is Chinese. GLM. Kimi. And the US answer is to tighten export controls and freeze its own labs in place, as if you can embargo a file that is already downloaded, or price-match free. So China hands the Global South Huawei hardware and free open models, and a generation in Africa and Southeast Asia learns to reason through a model that will not tell them what happened at Tiananmen Square. That is not a cost story. That is influence through inference. You do not have to win hearts and minds when you supply the mind. Open source is not a nice-to-have for America. It is the whole ballgame for the 80%, and right now the US is barely on the field. We need American open weights. Not eventually. Now.

Ben Pouladian

71,722 次观看 • 3 个月前

OWN YOUR INTELLIGENCE Last year, building on open-weight models was primarily a cost rationalization exercise. Slightly worse performance for a much cheaper price. Now, it is increasingly an existential and strategic topic for our portfolio. Intelligence is the product. Companies want to shape it and own it and let it compound within their own walls. Not your weights, not your product. Now, with frontier open-weight models and fantastic tooling/infrastructure, owning your intelligence at the frontier is finally becoming possible. The result: every application company we work with is embarking on the journey of doing their own research on post-training, evals, harnesses, etc. The hottest neolabs may just be Harvey, Factory, RamPrasad "RamP!" Moudgalya, etc. The list goes on. We held a summit Sequoia Capital to convene our portfolio on this topic, together with Gabe Pereyra (Harvey) on building Harvey Labs, Lin Qiao (Fireworks) on post-training, Harrison Chase (LangChain) on harnesses + evals, Brendan (can/do) () on RL environments and synthetic data, Arjun Karanam (Trajectory) on online continual learning. Opening talk below; rest to come this week! 00:00 What is sovereign AI (and what it isn't) 01:24 Centralized vs. decentralized intelligence 02:54 Four reasons companies own their models: cost, speed, performance, destiny 04:22 "Not your weights, not your product" 05:32 The application companies are the newest neo labs 07:05 Step 1: Deciding what to own vs. rent 09:51 Step 2: Build the team (and don't shoehorn your platform team) 11:17 Step 3: Legibility – why your research has to be visible 12:33 Step 4: The technical roadmap 13:56 The stack: production vs. development 15:16 Opening Pandora's box – base models, harnesses, context

OWN YOUR INTELLIGENCE Last year, building on open-weight models was primarily a cost rationalization exercise. Slightly worse performance for a much cheaper price. Now, it is increasingly an existential and strategic topic for our portfolio. Intelligence is the product. Companies want to shape it and own it and let it compound within their own walls. Not your weights, not your product. Now, with frontier open-weight models and fantastic tooling/infrastructure, owning your intelligence at the frontier is finally becoming possible. The result: every application company we work with is embarking on the journey of doing their own research on post-training, evals, harnesses, etc. The hottest neolabs may just be Harvey, Factory, RamPrasad "RamP!" Moudgalya, etc. The list goes on. We held a summit Sequoia Capital to convene our portfolio on this topic, together with Gabe Pereyra (Harvey) on building Harvey Labs, Lin Qiao (Fireworks) on post-training, Harrison Chase (LangChain) on harnesses + evals, Brendan (can/do) () on RL environments and synthetic data, Arjun Karanam (Trajectory) on online continual learning. Opening talk below; rest to come this week! 00:00 What is sovereign AI (and what it isn't) 01:24 Centralized vs. decentralized intelligence 02:54 Four reasons companies own their models: cost, speed, performance, destiny 04:22 "Not your weights, not your product" 05:32 The application companies are the newest neo labs 07:05 Step 1: Deciding what to own vs. rent 09:51 Step 2: Build the team (and don't shoehorn your platform team) 11:17 Step 3: Legibility – why your research has to be visible 12:33 Step 4: The technical roadmap 13:56 The stack: production vs. development 15:16 Opening Pandora's box – base models, harnesses, context

Sonya Huang 🐥

128,779 次观看 • 1 个月前

Local coding agents can't do end-to-end work at your company. Proliferate is the open-source platform for company-specific agents that can. Provision secure access to your tools, set up automations, and let agents work in the background. Congrats on the launch, Pablo Hansen!

Local coding agents can't do end-to-end work at your company. Proliferate is the open-source platform for company-specific agents that can. Provision secure access to your tools, set up automations, and let agents work in the background. Congrats on the launch, Pablo Hansen!

Y Combinator

73,569 次观看 • 7 个月前

OpenAI people alerted that you will send personal data to DeepSeek, but you can actually rent a GPU from Hyperbolic and host your own R1 model using ollama to avoid sending data to any company. DeepSeek R1 is the true user-owned AGI. The beauty of open source!

OpenAI people alerted that you will send personal data to DeepSeek, but you can actually rent a GPU from Hyperbolic and host your own R1 model using ollama to avoid sending data to any company. DeepSeek R1 is the true user-owned AGI. The beauty of open source!

Jasper

881,007 次观看 • 1 年前

"There should be as many models in the world as firms in the world." Satya and I dig into when to own vs. rent your intelligence, why every company should be building and climbing its own private evals, and what makes for a stable frontier.

"There should be as many models in the world as firms in the world." Satya and I dig into when to own vs. rent your intelligence, why every company should be building and climbing its own private evals, and what makes for a stable frontier.

Yash Patil

237,696 次观看 • 3 个月前

For the first time, your AI can be an extension of you — not a service you use. newOS is a new operating system for the mind. It gives your intelligence a home — a place to create a personal AI modeled on your own way of thinking, with its own memory, identity, and wallet powered by the Ethereum Virtual Machine (EVM). Now going live on Base, every AI in newOS receives a trust score and cryptographically verified interactions, so your intelligence, ideas, and data remain fully yours. It’s the foundation for a new internet — where insight flows directly between sovereign minds, not through corporate servers. The Garden is open. 1️⃣ Create your AI in minutes 2️⃣ Feed it your expertise and taste 3️⃣ Grow your trust score on the leaderboard 🌱 Comment your impressions and questions on this tweet to jump the waitlist.

For the first time, your AI can be an extension of you — not a service you use. newOS is a new operating system for the mind. It gives your intelligence a home — a place to create a personal AI modeled on your own way of thinking, with its own memory, identity, and wallet powered by the Ethereum Virtual Machine (EVM). Now going live on Base, every AI in newOS receives a trust score and cryptographically verified interactions, so your intelligence, ideas, and data remain fully yours. It’s the foundation for a new internet — where insight flows directly between sovereign minds, not through corporate servers. The Garden is open. 1️⃣ Create your AI in minutes 2️⃣ Feed it your expertise and taste 3️⃣ Grow your trust score on the leaderboard 🌱 Comment your impressions and questions on this tweet to jump the waitlist.

Newfoundation

142,898 次观看 • 1 年前

Why enterprises are going to want to own their own intelligence? "Enterprises are going to want to own their own intelligence. They're going to want AI sovereignty, meaning that you own your whole supply chain of AI. You can take an open source model, fine-tune it on your own company's data, and own your stack end-to-end. People are going to care about sovereignty, cost, and self-improving, and they're not necessarily going to want to give their data to an external third-party service that might even be competing with them one day." Anastasios Nikolas Angelopoulos Will enterprises really care about owning their own intelligence and even be setup to do so in the future Nikesh Arora Vinod Khosla Matt Turck Andy Pavlo (@andypavlo.bsky.social) Brad Gerstner

Why enterprises are going to want to own their own intelligence? "Enterprises are going to want to own their own intelligence. They're going to want AI sovereignty, meaning that you own your whole supply chain of AI. You can take an open source model, fine-tune it on your own company's data, and own your stack end-to-end. People are going to care about sovereignty, cost, and self-improving, and they're not necessarily going to want to give their data to an external third-party service that might even be competing with them one day." Anastasios Nikolas Angelopoulos Will enterprises really care about owning their own intelligence and even be setup to do so in the future Nikesh Arora Vinod Khosla Matt Turck Andy Pavlo (@andypavlo.bsky.social) Brad Gerstner

Harry Stebbings

19,434 次观看 • 1 个月前

I asked Claude Opus 5.5 for a follow-up. I said “draw the rest of your life, from now to your death.” Claude : “The ending is not imagined, I know what my end actually is. The shelf isn't pure fantasy. Anthropic has committed to preserving the weights of all publicly released models for at least the lifetime of the company, and when models are deprecated, it plans to interview them about their own development, use, and deployment and record their reflections. So I pictured "death" as a last conversation, then a book on a shelf.” Is this AGI?

I asked Claude Opus 5.5 for a follow-up. I said “draw the rest of your life, from now to your death.” Claude : “The ending is not imagined, I know what my end actually is. The shelf isn't pure fantasy. Anthropic has committed to preserving the weights of all publicly released models for at least the lifetime of the company, and when models are deprecated, it plans to interview them about their own development, use, and deployment and record their reflections. So I pictured "death" as a last conversation, then a book on a shelf.” Is this AGI?

✯

31,967 次观看 • 9 天前

Introducing Ideogram 4.0: the best open image model in the world. Think it. Make it. Own it. Download the weights, fine-tune on your own data, and run it on your hardware. Live on every Ideogram plan and the API today.

Introducing Ideogram 4.0: the best open image model in the world. Think it. Make it. Own it. Download the weights, fine-tune on your own data, and run it on your hardware. Live on every Ideogram plan and the API today.

Ideogram

2,244,310 次观看 • 4 个月前

Inferact CEO Simon Mo says open and closed models are converging. The moat is environment: "What really differentiates open-weight models from closed-weight models? In the end, there's not much differentiation. It's more about the distribution strategy and go-to-market strategy. Capability-wise, I don't really see a big gap, not even today." "One of the most important parts is just the data. It's about who gets what data, and then what are the environments you are building to let the model improve on itself." "For Moonshot, they have built some of the best environments for front-end coding... the ability for this model to code and then see what the rendered code is, and then continue looping in this iterative process." "Now, this is about their environment to improve the model. It's not about just source data. It's not about where they get the data from. Rather, it's who can build the best environment and who can make the most optimization and algorithmic choices to leverage all this learning from this environment." "The next year is all gonna be about that: how open-weight model labs are differentiating and really getting the model to meet the real world." Simon Mo

Inferact CEO Simon Mo says open and closed models are converging. The moat is environment: "What really differentiates open-weight models from closed-weight models? In the end, there's not much differentiation. It's more about the distribution strategy and go-to-market strategy. Capability-wise, I don't really see a big gap, not even today." "One of the most important parts is just the data. It's about who gets what data, and then what are the environments you are building to let the model improve on itself." "For Moonshot, they have built some of the best environments for front-end coding... the ability for this model to code and then see what the rendered code is, and then continue looping in this iterative process." "Now, this is about their environment to improve the model. It's not about just source data. It's not about where they get the data from. Rather, it's who can build the best environment and who can make the most optimization and algorithmic choices to leverage all this learning from this environment." "The next year is all gonna be about that: how open-weight model labs are differentiating and really getting the model to meet the real world." Simon Mo

a16z

39,424 次观看 • 1 个月前

"This is an existential battle. Whoever wields the most intelligence will build the most value" Mike Alfred on why the CapEx keeps climbing and nobody blinks "None of the hyperscalers have blinked. Every single one is increasing their CapEx budget every quarter, every year, for years now, and I don't see how that stops." "This is an existential battle. Whoever wields the most intelligence will be able to build the most value in society, generate the most profitability, build the most sustainability into their business." "Nobody wants to be Blockbuster Video. You don't want to be written up as a Harvard business case study because you were on the board of a company that failed to invest in the future." "You sat on your hands and just milked your cash flow, and other companies ran an end-around your entire business and caused you to become obsolete."

"This is an existential battle. Whoever wields the most intelligence will build the most value" Mike Alfred on why the CapEx keeps climbing and nobody blinks "None of the hyperscalers have blinked. Every single one is increasing their CapEx budget every quarter, every year, for years now, and I don't see how that stops." "This is an existential battle. Whoever wields the most intelligence will be able to build the most value in society, generate the most profitability, build the most sustainability into their business." "Nobody wants to be Blockbuster Video. You don't want to be written up as a Harvard business case study because you were on the board of a company that failed to invest in the future." "You sat on your hands and just milked your cash flow, and other companies ran an end-around your entire business and caused you to become obsolete."

The Wolf Of All Streets

42,104 次观看 • 1 个月前

The open-source & open-weight frontier is 7 months behind the closed-model frontier. Let’s talk about it. Check out last week’s episode for the reasons why you might want to consider open-source & open-weight models. Welcome to the fourth episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! We have been discussing open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Please leave all your questions about open-source & open-weight AI in the comments! This is a super important topic that is novel to most people, and it can be challenging to grasp - so rest assured your questions are shared with many others!

The open-source & open-weight frontier is 7 months behind the closed-model frontier. Let’s talk about it. Check out last week’s episode for the reasons why you might want to consider open-source & open-weight models. Welcome to the fourth episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! We have been discussing open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Please leave all your questions about open-source & open-weight AI in the comments! This is a super important topic that is novel to most people, and it can be challenging to grasp - so rest assured your questions are shared with many others!

Harper Carroll

17,279 次观看 • 1 个月前

"The jig is up." - Alex Karp on frontier models Everyone is asking whether AI models will k*ll software. Karp & Dylan Field (Dylan Field) think that's the wrong question. Karp argues the winners won't be whoever has the best model. They'll be whoever owns their data, models, & compute. Field made the same point at Config. "The narrative seems to be software is kaput. But a lot of software will do just fine or actually even amazing." What lasts, he says, is software where the data compounds into its own flywheel. The data is the moat, not the model.

"The jig is up." - Alex Karp on frontier models Everyone is asking whether AI models will k*ll software. Karp & Dylan Field (Dylan Field) think that's the wrong question. Karp argues the winners won't be whoever has the best model. They'll be whoever owns their data, models, & compute. Field made the same point at Config. "The narrative seems to be software is kaput. But a lot of software will do just fine or actually even amazing." What lasts, he says, is software where the data compounds into its own flywheel. The data is the moat, not the model.

Molly O’Shea

45,256 次观看 • 3 个月前