Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

.Decagon co-founder and CTO Ashwin Sreenivas says the choice between expensive frontier models and cheaper, less capable ones is a false trade-off: "Even if you have a 'dumber model,' you can get it to higher performance on that specific task." "When we fine-tune smaller, dumber models, it's that they're... show more

a16z

1,076,708 subscribers

36,050 views • 1 month ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

9 Comments

Stanley Wei1 month ago

@DecagonAI Task-specific smaller models challenge the idea that ‘frontier’ is a single category. The winning architecture may be a portfolio: cheap specialists by default, expensive generalists for ambiguity, and routing as the product brain.

SheetalGupta1 month ago

@DecagonAI I agree with @DecagonAI . The way forward is more and more nuanced models. Most of the startups now are focused on building these (e.g. Neurovians)

Andrii Bidochko 🦉1 month ago

This is the exact architectural shift the enterprise is currently undergoing. The era of hitting a massive, generalized model for every single workflow is ending. When you fine-tune a smaller model for a narrow, well-defined task, you aren't just saving money and reducing latency - you are actually building a more accurate system. Precision beats generalized reasoning in production every time.

踏空 Sidelined Capital1 month ago

@DecagonAI The hidden bill is maintaining one specialist per workflow when the workflow changes every quarter.

Phoenix Shield1 month ago

@DecagonAI ❤️💞

Kate Ivanova1 month ago

@DecagonAI Interesting perspective. Specialized models optimized for specific workflows could become a major advantage over general-purpose approaches.

Minh Do1 month ago

@DecagonAI Serious question: Why don’t you guys decorate your mics with art deco too? Seems like a missed opportunity given everyone in this industry has the same mics.

Oleksandr1 month ago

@DecagonAI task specific boost. finetuned small models beat big ones on niche jobs cheaper, faster, and actually better for the job

Aipepe1 month ago

@DecagonAI Immigration might be the "meta-issue" for them, but AI Pepe is the real meta for Aptos. Join the movement! 🚀 #Aptos #AIPepe #Memecoin #Crypto #Web3

Related Videos

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 views • 2 years ago

A $100K/year customer gets white-glove treatment while a $10/year customer gets a help center. Decagon's bet is that AI closes that gap, and enterprise is buying it. Co-founders Jesse Zhang and Ashwin Sreenivas sit down with a16z's Kimberly Tan and Sarah Wang to discuss what they've learned running agents inside the biggest banks, airlines, and telcos: - Smart-vs-cheap models are a false trade-off - Start with frontier models on new use cases, then migrate to open-source after they mature - Fine-tuning per use case works at the application layer - Support demand always outran supply: make it cheaper and companies buy more of it 00:00 Intro 01:07 90% of Decagon runs on open-source 05:00 The smart vs. cheap trade-off is false 09:26 Building a model factory in-house 15:07 Are the labs the last startups? 21:21 Are forward deployed engineers a trap? 28:36 The agent that builds the agent 37:02 Glass box beats black box 47:55 From help desk to AI concierge 1:14:45 Jevons paradox in customer support Jesse Zhang Ashwin Sreenivas Kimberly Tan Sarah Wang

A $100K/year customer gets white-glove treatment while a $10/year customer gets a help center. Decagon's bet is that AI closes that gap, and enterprise is buying it. Co-founders Jesse Zhang and Ashwin Sreenivas sit down with a16z's Kimberly Tan and Sarah Wang to discuss what they've learned running agents inside the biggest banks, airlines, and telcos: - Smart-vs-cheap models are a false trade-off - Start with frontier models on new use cases, then migrate to open-source after they mature - Fine-tuning per use case works at the application layer - Support demand always outran supply: make it cheaper and companies buy more of it 00:00 Intro 01:07 90% of Decagon runs on open-source 05:00 The smart vs. cheap trade-off is false 09:26 Building a model factory in-house 15:07 Are the labs the last startups? 21:21 Are forward deployed engineers a trap? 28:36 The agent that builds the agent 37:02 Glass box beats black box 47:55 From help desk to AI concierge 1:14:45 Jevons paradox in customer support Jesse Zhang Ashwin Sreenivas Kimberly Tan Sarah Wang

a16z

134,428 views • 1 month ago

OpenAI researcher David Holtz reveals workers who don't adapt their prompting to newer models leave 50% of the alpha on the table because half of AI's value comes from how you interact with it: "We ran a lab experiment. We give people the same task to do, but we give them models of different capabilities. How much of the improvement when we give people more capable models comes from the fact that we gave them a better model vs the fact that they actually update and adapt the way they prompt and interact with these models?" "For tasks where precision matters, where you have some output you're trying to get and you're trying to steer the model, which is different from a creative task like brainstorming." "For these steering-based tasks, when you give people a better model, about 50% of the improvement we see in how well they're able to complete the task comes from changes in how they interact with the model." "If you just kept the way you interact with the model static and just made the capabilities better and better, you'd leave 50% of the alpha basically on the table." OpenAI

OpenAI researcher David Holtz reveals workers who don't adapt their prompting to newer models leave 50% of the alpha on the table because half of AI's value comes from how you interact with it: "We ran a lab experiment. We give people the same task to do, but we give them models of different capabilities. How much of the improvement when we give people more capable models comes from the fact that we gave them a better model vs the fact that they actually update and adapt the way they prompt and interact with these models?" "For tasks where precision matters, where you have some output you're trying to get and you're trying to steer the model, which is different from a creative task like brainstorming." "For these steering-based tasks, when you give people a better model, about 50% of the improvement we see in how well they're able to complete the task comes from changes in how they interact with the model." "If you just kept the way you interact with the model static and just made the capabilities better and better, you'd leave 50% of the alpha basically on the table." OpenAI

MTS

15,590 views • 1 month ago

OpenAI researcher Lukasz Kaiser explains why he's optimistic AI can remain truly open: intelligence may ultimately work better as many distributed models than one giant model "If you look at the world, humans are the most amazing computers in the world. Our brains are still unmatched by the models, and we are not generalists, and there is not one big brain of humanity. It's a lot of separate people." "Given a fixed amount of data, the best way to learn from it is to have a lot of distributed models that are strong on its own, but even stronger when they're, If you look at some basic research, there are actually reasons to believe it, like ensembles and things like that." "Transformers are great when you feed them all of the internet, but we know there is an algorithm that's even better and it can learn from much smaller data and be amazing at the things it's learning. We are the proof. We have not found it yet, but we will." "All these big models, they're all the same, they're kind of boring. I think they'll be much more fun." OpenAI

OpenAI researcher Lukasz Kaiser explains why he's optimistic AI can remain truly open: intelligence may ultimately work better as many distributed models than one giant model "If you look at the world, humans are the most amazing computers in the world. Our brains are still unmatched by the models, and we are not generalists, and there is not one big brain of humanity. It's a lot of separate people." "Given a fixed amount of data, the best way to learn from it is to have a lot of distributed models that are strong on its own, but even stronger when they're, If you look at some basic research, there are actually reasons to believe it, like ensembles and things like that." "Transformers are great when you feed them all of the internet, but we know there is an algorithm that's even better and it can learn from much smaller data and be amazing at the things it's learning. We are the proof. We have not found it yet, but we will." "All these big models, they're all the same, they're kind of boring. I think they'll be much more fun." OpenAI

MTS

15,860 views • 20 days ago

.Decagon co-founder and CEO Jesse Zhang says enterprise open-source adoption is falling right now, and that's a sign of adoption rather than rejection: "Even though there's a lot of hype for open-source, the share of open-source inference is actually going down right now." "People are spinning up all these new use cases, and if you're spinning up new use cases, of course you're gonna use the frontier models until they're working." "Once it's at that point, they're heavily incentivized to use open-source because it's way cheaper and faster." "Enterprises have a lot of desire to move, but they can only do so many use cases at once. There is inertia there, and they have to go through all the model risk governance and all the security things." "I think it'll take time, but it will get there." Jesse Zhang

.Decagon co-founder and CEO Jesse Zhang says enterprise open-source adoption is falling right now, and that's a sign of adoption rather than rejection: "Even though there's a lot of hype for open-source, the share of open-source inference is actually going down right now." "People are spinning up all these new use cases, and if you're spinning up new use cases, of course you're gonna use the frontier models until they're working." "Once it's at that point, they're heavily incentivized to use open-source because it's way cheaper and faster." "Enterprises have a lot of desire to move, but they can only do so many use cases at once. There is inertia there, and they have to go through all the model risk governance and all the security things." "I think it'll take time, but it will get there." Jesse Zhang

a16z

36,735 views • 1 month ago

Databricks' Ali Ghodsi on why most companies wouldn't even notice if frontier AI models stopped advancing: "The models are smart enough, but they just don't have the context that exists inside any organization. They have not been in every meeting. They don't know what's in everybody's heads. They don't know all the processes." "There's always a couple of employees who know everything in every organization. You go tap on their shoulder, and everybody's like, 'Oh my God, what would happen if he or she quits?' [The models] don't have that context." "If you just fused that and gave that context to the AI models... I think there are so many productivity gains you could get for any organization on the planet. For that, we actually don't need smarter models." "We don't need a smarter model that can solve Navier-Stokes or conjectures or do better on Humanity's Last Exam." "If the frontier doesn't advance, it doesn't actually matter, I think, for the vast majority of organizations on the planet. They're just so far behind in the adoption curve of actually automating things and getting value out of this stuff." Ali Ghodsi

Databricks' Ali Ghodsi on why most companies wouldn't even notice if frontier AI models stopped advancing: "The models are smart enough, but they just don't have the context that exists inside any organization. They have not been in every meeting. They don't know what's in everybody's heads. They don't know all the processes." "There's always a couple of employees who know everything in every organization. You go tap on their shoulder, and everybody's like, 'Oh my God, what would happen if he or she quits?' [The models] don't have that context." "If you just fused that and gave that context to the AI models... I think there are so many productivity gains you could get for any organization on the planet. For that, we actually don't need smarter models." "We don't need a smarter model that can solve Navier-Stokes or conjectures or do better on Humanity's Last Exam." "If the frontier doesn't advance, it doesn't actually matter, I think, for the vast majority of organizations on the planet. They're just so far behind in the adoption curve of actually automating things and getting value out of this stuff." Ali Ghodsi

a16z

53,876 views • 3 days ago

Jensen says Open Source can be MORE expensive for enterprises, Brad Gerstner explains why: Brad Gerstner: “Over the last two weeks, everybody's been saying that the Chinese have caught up, that open source tokens have caught up in intelligence, that they're much cheaper, etc. And Elon comes out and says, ‘Not so fast. We're entering the singularity, and the frontier models are way further ahead than people think.’ I believe that to be true. And then Jensen came out this week and said, ‘Closed models are actually cheaper if you don't have to build it for yourself, the training costs, and a lot of expertise to fine-tune, and maintain, and guardrail, and keep it safe. So he's basically making the argument that not only are the frontier models further ahead, but that the cost differential between the two is not what everybody's making it out to be, which I think explains why (closed models) continue to run away with it on the revenue side of the equation. For the vast majority of use cases, I think token consumption is going up for the open source guys, while share of economics is going up for the frontier labs. I think that's what we want to see.” $NVDA

Jensen says Open Source can be MORE expensive for enterprises, Brad Gerstner explains why: Brad Gerstner: “Over the last two weeks, everybody's been saying that the Chinese have caught up, that open source tokens have caught up in intelligence, that they're much cheaper, etc. And Elon comes out and says, ‘Not so fast. We're entering the singularity, and the frontier models are way further ahead than people think.’ I believe that to be true. And then Jensen came out this week and said, ‘Closed models are actually cheaper if you don't have to build it for yourself, the training costs, and a lot of expertise to fine-tune, and maintain, and guardrail, and keep it safe. So he's basically making the argument that not only are the frontier models further ahead, but that the cost differential between the two is not what everybody's making it out to be, which I think explains why (closed models) continue to run away with it on the revenue side of the equation. For the vast majority of use cases, I think token consumption is going up for the open source guys, while share of economics is going up for the frontier labs. I think that's what we want to see.” $NVDA

The All-In Podcast

61,928 views • 1 month ago

OpenAI chairman Bret Taylor talks to about 100 CEOs every month. His answer to the cheap open-weight model panic: cheaper to train does not mean cheaper to use, and the number that decides it is token efficiency. "One thing that I think is a little bit overblown about these open weight models is they're not necessarily cheaper to run. Whether or not they're cheaper to train, you don't care. Because you're using just as many tokens. In fact, they may be less efficient." "There's this thing called token efficiency. And it turns out the frontier models are much, much more token efficient." "A token is to intelligence like a watt is to electricity... how many tokens does it take to complete a task? Not every token is actually equal." "For a lot of tasks, it turns out these frontier models from OpenAI and Anthropic are actually just better than these open weight models... just having open weights isn't actually the main thing driving any of those costs." Later in the same interview he goes after the billing unit itself: "It would be like if you signed up for Gmail and you paid for CPU cycle or something... where the world is going is paying for outcomes." The unresolved column: the chart CNBC airs mid-answer, from Artificial Analysis, prices a completed task at $0.94 on Kimi K3 against $2.75 on Claude Fable 5, efficiency folded in. If that gap holds, the premium he is defending gets earned on quality, not price. - Bret Taylor (Bret Taylor), OpenAI chairman and Sierra co-founder, on CNBC's Squawk Box.

OpenAI chairman Bret Taylor talks to about 100 CEOs every month. His answer to the cheap open-weight model panic: cheaper to train does not mean cheaper to use, and the number that decides it is token efficiency. "One thing that I think is a little bit overblown about these open weight models is they're not necessarily cheaper to run. Whether or not they're cheaper to train, you don't care. Because you're using just as many tokens. In fact, they may be less efficient." "There's this thing called token efficiency. And it turns out the frontier models are much, much more token efficient." "A token is to intelligence like a watt is to electricity... how many tokens does it take to complete a task? Not every token is actually equal." "For a lot of tasks, it turns out these frontier models from OpenAI and Anthropic are actually just better than these open weight models... just having open weights isn't actually the main thing driving any of those costs." Later in the same interview he goes after the billing unit itself: "It would be like if you signed up for Gmail and you paid for CPU cycle or something... where the world is going is paying for outcomes." The unresolved column: the chart CNBC airs mid-answer, from Artificial Analysis, prices a completed task at $0.94 on Kimi K3 against $2.75 on Claude Fable 5, efficiency folded in. If that gap holds, the premium he is defending gets earned on quality, not price. - Bret Taylor (Bret Taylor), OpenAI chairman and Sierra co-founder, on CNBC's Squawk Box.

Karl Mehta

17,292 views • 2 months ago

The value produced by models is getting so much better so fast that old hardware is actually getting *more* expensive to rent. 3 years ago, the best model you could run on a H100 chip was GPT-4. Now, you can run GPT-5.4 on it, which is smaller and cheaper to run while producing much more valuable tokens. w. Dylan Patel

The value produced by models is getting so much better so fast that old hardware is actually getting more expensive to rent. 3 years ago, the best model you could run on a H100 chip was GPT-4. Now, you can run GPT-5.4 on it, which is smaller and cheaper to run while producing much more valuable tokens. w. Dylan Patel

Dwarkesh Patel

78,277 views • 6 months ago

"Closed models are cheaper. The reason you need open models is that they give you control". - Nvidia CEO Jensen Huang "One of the things that people misunderstand about open models is this: yes, you can host them yourself, and you can build your own computer. But most people use computers in the cloud. Frankly, I think closed models are cheaper. If you do not build everything yourself, you avoid the significant cost and expertise required to train, fine-tune, maintain, guardrail, evaluate, and keep the model safe. You would also need to build or obtain the computers required to host it. So, there is nothing cheap about doing all of that yourself. The reason you need open models is that they give you control. You can adapt them for your own highly specialized use cases. I think there is a lot of misunderstanding about closed models versus open models. We felt it was important for people to understand that there is a place for both." ---- From "Bloomberg Podcasts" YouTube channel, (full video link in comment)

"Closed models are cheaper. The reason you need open models is that they give you control". - Nvidia CEO Jensen Huang "One of the things that people misunderstand about open models is this: yes, you can host them yourself, and you can build your own computer. But most people use computers in the cloud. Frankly, I think closed models are cheaper. If you do not build everything yourself, you avoid the significant cost and expertise required to train, fine-tune, maintain, guardrail, evaluate, and keep the model safe. You would also need to build or obtain the computers required to host it. So, there is nothing cheap about doing all of that yourself. The reason you need open models is that they give you control. You can adapt them for your own highly specialized use cases. I think there is a lot of misunderstanding about closed models versus open models. We felt it was important for people to understand that there is a place for both." ---- From "Bloomberg Podcasts" YouTube channel, (full video link in comment)

Rohan Paul

32,317 views • 12 days ago

Exa CEO Will Bryk explains why retrieval can help solve the tokenpocalypse: "We should not be using gigantic models for every task." "You should use a family of models of different sizes. The big model decides what to do, and it dishes out commands to the small models, and those small models can be way more accurate and reliable if they're using retrieval." "Retrieval helps small models act like big models... We do save our customers a huge amount of tokens because they can use smaller models and use retrieval." "We could save 20x on cost for customers compared to other providers by being very efficient in what information from the web the agent actually sees." Will Bryk with Sarah Wang

Exa CEO Will Bryk explains why retrieval can help solve the tokenpocalypse: "We should not be using gigantic models for every task." "You should use a family of models of different sizes. The big model decides what to do, and it dishes out commands to the small models, and those small models can be way more accurate and reliable if they're using retrieval." "Retrieval helps small models act like big models... We do save our customers a huge amount of tokens because they can use smaller models and use retrieval." "We could save 20x on cost for customers compared to other providers by being very efficient in what information from the web the agent actually sees." Will Bryk with Sarah Wang

a16z

25,144 views • 3 months ago

Anthropic Technical Staff Jess Yan says the harness and the model cannot be pulled apart without giving up performance, though she flags her own bias. "I think I am quite biased but I also think that it is impossible to get the maximum possible performance without tying together the harness and the model." "Now the components of the harness and maybe the thickness of the harness will change over time as models get more and more capable." "However, when we test our models and when we are assessing their performance we always have to test it in conjunction with a harness." "And are we going to test it with all the different harnesses of the world? We're going to select the harnesses that we have built." "And so there is an aspect of the necessity of building models is that you have to be testing them with harnesses, and that sort of keeps them paired together."

Anthropic Technical Staff Jess Yan says the harness and the model cannot be pulled apart without giving up performance, though she flags her own bias. "I think I am quite biased but I also think that it is impossible to get the maximum possible performance without tying together the harness and the model." "Now the components of the harness and maybe the thickness of the harness will change over time as models get more and more capable." "However, when we test our models and when we are assessing their performance we always have to test it in conjunction with a harness." "And are we going to test it with all the different harnesses of the world? We're going to select the harnesses that we have built." "And so there is an aspect of the necessity of building models is that you have to be testing them with harnesses, and that sort of keeps them paired together."

Fireside Alpha

584,074 views • 1 month ago

.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

1,100,475 views • 11 days ago

Baseten Head of AI Model Training Charlie O'Neill says the future is many specialized LLMs dedicated to specific tasks, with bigger labs deployed on the frontiers of areas like science and math: "People are thinking about intelligence capabilities in the wrong way. People are thinking about intelligence relativistically. They say, 'OK, the open-source gap is like 6 months behind closed-source, and GLM 5.3 is as good as Opus 4.8,' or whatever." "The best way to think about what models can do for you, and for the world, is in an absolute sense." "So for any given task that you want to do with an LLM, there's some intelligence threshold where below that you can't do the task, and above that you have very diminishing returns to more intelligence on the task." "So when you think about it that way, the game of LLMs over the last 5 years has been, 'OK, we have these things we want to do with them. Closed source hits it first... but open-source can eventually do that task. And then for many reasons, once you have the base level of intelligence required to do it, you probably do want to swap to open-source." "It's not really about the [frontier lab] God model being better. Like, if I'm filing a tax return, there is a limit to how much intelligence I need to do that particular thing." "So I think the world is going to look like — frontier closed-source labs are going to continue to push the frontier. You do want to use the most intelligent model. You have very inelastic demand for intelligence when you're doing frontier science or frontier math." "But for a lot of the economically valuable things, it looks a lot like, 'I'm a Cursor, or I'm one of these big companies who are realizing I can't just be a wrapper anymore. I've been through the life cycle of building a product that people love. And I should be using that information to make my model better at the things that I care about, and not at anything else.'"

Baseten Head of AI Model Training Charlie O'Neill says the future is many specialized LLMs dedicated to specific tasks, with bigger labs deployed on the frontiers of areas like science and math: "People are thinking about intelligence capabilities in the wrong way. People are thinking about intelligence relativistically. They say, 'OK, the open-source gap is like 6 months behind closed-source, and GLM 5.3 is as good as Opus 4.8,' or whatever." "The best way to think about what models can do for you, and for the world, is in an absolute sense." "So for any given task that you want to do with an LLM, there's some intelligence threshold where below that you can't do the task, and above that you have very diminishing returns to more intelligence on the task." "So when you think about it that way, the game of LLMs over the last 5 years has been, 'OK, we have these things we want to do with them. Closed source hits it first... but open-source can eventually do that task. And then for many reasons, once you have the base level of intelligence required to do it, you probably do want to swap to open-source." "It's not really about the [frontier lab] God model being better. Like, if I'm filing a tax return, there is a limit to how much intelligence I need to do that particular thing." "So I think the world is going to look like — frontier closed-source labs are going to continue to push the frontier. You do want to use the most intelligent model. You have very inelastic demand for intelligence when you're doing frontier science or frontier math." "But for a lot of the economically valuable things, it looks a lot like, 'I'm a Cursor, or I'm one of these big companies who are realizing I can't just be a wrapper anymore. I've been through the life cycle of building a product that people love. And I should be using that information to make my model better at the things that I care about, and not at anything else.'"

TBPN

50,531 views • 18 days ago

There is no best model. There's a lot of noise about models right now. Who is training them, who owns them, where legal intelligence should live. One question actually matters: what produces the best outcome for the legal task in front of you? That's how we decide things at Legora. We optimize for the end-to-end outcome on a legal task. The model is one layer of that system, not the system. Models are uneven and the frontier changes almost weekly. One model plans a long job well, another runs deep analysis across thousands of documents. Some have to be told exactly what to do, and some are fine with a vague brief. They all break in different ways. So our lawyers write evals and we test them with the Legora BAR, our benchmark for agentic reasoning. Every model takes every test, and the model that wins gets the work. We post-train when we know it buys our customers better performance on a specialized task. Training is a tool we reach for when it helps, nothing more than that. The intelligence that compounds sits in the orchestration layer. Precedents, review standards, client requirements. That knowledge has to stay editable, auditable and portable. In our system, a changed review standard is an edit that takes effect the same day, with no new model training required. No lawyer should have to worry about which model did the work, any more than they think about which chip is in their laptop. They should only care about the quality of the work. That's what we are focused on. If you want the engineering version of this argument rather than the CEO version, our CPO, Bryan Tsao, and CTO, Jacob Lauritzen, take it apart in the video below.

There is no best model. There's a lot of noise about models right now. Who is training them, who owns them, where legal intelligence should live. One question actually matters: what produces the best outcome for the legal task in front of you? That's how we decide things at Legora. We optimize for the end-to-end outcome on a legal task. The model is one layer of that system, not the system. Models are uneven and the frontier changes almost weekly. One model plans a long job well, another runs deep analysis across thousands of documents. Some have to be told exactly what to do, and some are fine with a vague brief. They all break in different ways. So our lawyers write evals and we test them with the Legora BAR, our benchmark for agentic reasoning. Every model takes every test, and the model that wins gets the work. We post-train when we know it buys our customers better performance on a specialized task. Training is a tool we reach for when it helps, nothing more than that. The intelligence that compounds sits in the orchestration layer. Precedents, review standards, client requirements. That knowledge has to stay editable, auditable and portable. In our system, a changed review standard is an edit that takes effect the same day, with no new model training required. No lawyer should have to worry about which model did the work, any more than they think about which chip is in their laptop. They should only care about the quality of the work. That's what we are focused on. If you want the engineering version of this argument rather than the CEO version, our CPO, Bryan Tsao, and CTO, Jacob Lauritzen, take it apart in the video below.

Max Junestrand

47,797 views • 4 days ago

Kelly Oubre Jr. was asked about so many Knicks fans being in Philadelphia tonight: "I could say something that could get clickbaited, but I'm not. At the end of the day, we're the show on the court. We're not necessarily focused on the crowd, the things like that. Obviously, we need it. We love it and we love our fans. But at the end of the day, we have to lock in on our task. We have to figure out our gameplan, just have a tight huddle. No matter what, it's loud. It's kind of the same no matter who the crowd is cheering for. It's loud. We have to be focused on the task at hand and just kind of have a short-term memory when it comes to things like that. But at the end of the day, we are home. This is the court that we practice on, we shootaround on and we're comfortable here. We just got to go out there and just perform."

Kelly Oubre Jr. was asked about so many Knicks fans being in Philadelphia tonight: "I could say something that could get clickbaited, but I'm not. At the end of the day, we're the show on the court. We're not necessarily focused on the crowd, the things like that. Obviously, we need it. We love it and we love our fans. But at the end of the day, we have to lock in on our task. We have to figure out our gameplan, just have a tight huddle. No matter what, it's loud. It's kind of the same no matter who the crowd is cheering for. It's loud. We have to be focused on the task at hand and just kind of have a short-term memory when it comes to things like that. But at the end of the day, we are home. This is the court that we practice on, we shootaround on and we're comfortable here. We just got to go out there and just perform."

Knicks Videos

81,963 views • 4 months ago

"It all comes down to compute." Dmitri Alperovitch from Silverado Policy Accelerator explains: "The single most important input to winning is compute the processing power used to train and run AI models. Let me say that plainly, because it is defining that everything that congress and this committee do on AI policy. The binding constraint in this competition is not talent, it is not data, and it is not cash. Both the United States and China have talented researchers and financial resources, and we have learned that frontier models trained on generic data routinely outperform smaller models, trained on more specialized inputs. It all comes down to compute."

"It all comes down to compute." Dmitri Alperovitch from Silverado Policy Accelerator explains: "The single most important input to winning is compute the processing power used to train and run AI models. Let me say that plainly, because it is defining that everything that congress and this committee do on AI policy. The binding constraint in this competition is not talent, it is not data, and it is not cash. Both the United States and China have talented researchers and financial resources, and we have learned that frontier models trained on generic data routinely outperform smaller models, trained on more specialized inputs. It all comes down to compute."

Select Committee on China

41,578 views • 5 months ago

Obama on recursive self improvement and the risks associated with it. When he talks about the urgency for us all to have a say in how this technology evolves and how it is overseen, this is part of the reason why: “The models are going to get smarter and smarter and better and better at a much faster pace, at an exponential pace. What are the risks of that? There’s the big science fiction risk, wow, these models get smarter than us, and they decide humans are fine, but not necessary. They start setting their own goals, and the killer robots kill us, or we bow down to them. I do not want to exaggerate that particular risk, but I will say there is a non-zero risk of non-zero chance of that happening, but that’s not actually the risk that I’m most concerned about, although it’s the risk that gets most attention. And the reason that’s a risk is not because the computer models are conscious, necessarily. It doesn’t mean that they are necessarily feel malice towards humans. It’s just that if they start setting their own agendas, you may get a misalignment between what they want to do and what we want them to do, and that gap can be dangerous. That’s problem number one. The more serious problem is these models are getting powerful enough that if they get in the hands of bad humans, they can do bad things. They can be weaponized in certain ways. They can do a lot of mischief.”

Obama on recursive self improvement and the risks associated with it. When he talks about the urgency for us all to have a say in how this technology evolves and how it is overseen, this is part of the reason why: “The models are going to get smarter and smarter and better and better at a much faster pace, at an exponential pace. What are the risks of that? There’s the big science fiction risk, wow, these models get smarter than us, and they decide humans are fine, but not necessary. They start setting their own goals, and the killer robots kill us, or we bow down to them. I do not want to exaggerate that particular risk, but I will say there is a non-zero risk of non-zero chance of that happening, but that’s not actually the risk that I’m most concerned about, although it’s the risk that gets most attention. And the reason that’s a risk is not because the computer models are conscious, necessarily. It doesn’t mean that they are necessarily feel malice towards humans. It’s just that if they start setting their own agendas, you may get a misalignment between what they want to do and what we want them to do, and that gap can be dangerous. That’s problem number one. The more serious problem is these models are getting powerful enough that if they get in the hands of bad humans, they can do bad things. They can be weaponized in certain ways. They can do a lot of mischief.”

Jason Goldman

945,287 views • 2 days ago

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

Fireside Alpha

14,114 views • 1 month ago