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Open-source models are already taking the easier tasks. The bull case for frontier labs rests on one question. "A lot of software engineering, white-collar work in general, does not need Fable 5.1 or Astra 6 level intelligence." "A lot of businesses, especially lower gross margin businesses, are very rationally... making the decision to start offloading some of the easier tasks to these increasingly capable open-source models." "So whether the frontier labs are a durable business model really comes down to your belief on whether the TAM of new things you can do with increasingly smarter frontier intelligence will outgrow the portion of tasks being offloaded to these cheaper models." "If you were to magically spawn 100 million superintelligent PhDs who are experts in every field and don't need to eat or sleep, do you think the economy would be able to absorb that relatively quickly and still get high ROI?"show more

SemiAnalysis

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35,742 Aufrufe • vor 2 Tagen •via X (Twitter)

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18 Kommentare

Profilbild von SemiAnalysis

SemiAnalysisvor 2 Tagen

Get the Full Podcast on Spotify:

Profilbild von Anderson

Andersonvor 2 Tagen

Frontier labs selling genius while most work needs a cheap model

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Deepvor 2 Tagen

which tasks do you count as easy for the open models?

Profilbild von Stevan Boljevic

Stevan Boljevicvor 2 Tagen

When you strip out API margin differentials (source - your numbers) the frontier labs have lower compute cost per task for easier tasks. With additional compute supply they could capture the market for these tasks too with lower margins on Luna/Sonnet/Terra class models There’s plenty of upside being lowest cost producer of commodity factor of production - look at the Gulf

Profilbild von Macro Bombastic

Macro Bombasticvor 2 Tagen

tbh the easy task money was never the frontier labs game anyway

Profilbild von ALEX SERRA

ALEX SERRAvor 2 Tagen

Besides cybersecurity,what other AI niches could open models attract in the future ?

Profilbild von l-tzhar-bijaz

l-tzhar-bijazvor 2 Tagen

thank you very much. how about frontier labs are going to dominate every single point of the pareto cap / price curve?

Profilbild von Joshua Week

Joshua Weekvor 2 Tagen

And a lot of compute doesn’t need a power hungry GPU. Please follow these examples

Profilbild von Alex Crișan

Alex Crișanvor 2 Tagen

Well said; but electricity will skyrocket

Profilbild von Eon Vale

Eon Valevor 2 Tagen

what counts as an easy task keeps moving up every six months, so frontier labs are basically selling a head start that keeps getting shorter

Profilbild von Thomas DiFazio

Thomas DiFaziovor 2 Tagen

Two-thirds of large businesses now pay for AI per Ramp data but the next leg of adoption may be depth: learning the difference of when to use a top-tier model vs. a less intensive one.

Profilbild von RowdyGoose

RowdyGoosevor 2 Tagen

Semianalysis has 0 credibility any more

Profilbild von StockTake

StockTakevor 2 Tagen

Feels like lazy analysis. Open source taking over simpler tasks doesn’t necessarily weaken the frontier labs. It could massively expand the market while pushing frontier models to problems that weren’t economically feasible to solve before. The bigger question is whether the labs can maintain pricing power as intelligence gets cheaper. That’s what will determine who captures the value, not how many hypothetical PhDs the economy can absorb.

Profilbild von Nacho

Nachovor 2 Tagen

Once an app lets users pick, routine work drifts to the cheaper model, and the frontier labs have to earn their price on the hardest tasks

Profilbild von BullBear.News

BullBear.Newsvor 2 Tagen

the transition point is when local inference cost drops below the coordination overhead of external APIs

Profilbild von Sterling

Sterlingvor 2 Tagen

Unless you're a Big Tech SWE, the cost effectiveness of open source tokens is really really hard to ignore.

Profilbild von Nessuno

Nessunovor 2 Tagen

See Wallis/North 1986, or our book forthcoming @PalgraveEcon for an unqualified, No.

Profilbild von Tim

Timvor 2 Tagen

Competition at any level (state/country, corporation, individual) ensures that there is a market for ever higher levels of intelligence. You want to be smarter than your competitor.

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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 Aufrufe • vor 1 Monat

AI only has pre-schooler level capabilities for visual tasks. "The frontier models—the best of them—still reason around the age of a preschooler, and any elementary school kid in that benchmark was able to beat all the frontier models on these visual tasks." "These are tasks that are not just object recognition. It's about spatial reasoning... tasks like navigating through the maze…” — Andrew M. Dai, CEO of Elorian

AI only has pre-schooler level capabilities for visual tasks. "The frontier models—the best of them—still reason around the age of a preschooler, and any elementary school kid in that benchmark was able to beat all the frontier models on these visual tasks." "These are tasks that are not just object recognition. It's about spatial reasoning... tasks like navigating through the maze…” — Andrew M. Dai, CEO of Elorian

The Information

26,229 Aufrufe • vor 3 Monaten

$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 Aufrufe • vor 2 Monaten

Palantir CTO Shyam Sankar says Chinese open source models pose a "threat to American Prosperity": "These Chinese open source models are really the result of distillation attacks." "Most of this is just stolen American IP from frontier labs, and I think the frontier labs should do more to protect that IP, and that's in their own economic interest." "The gravest threat to us is not homicide, it's suicide." "It's an inability to turn these tokens into economic value and generate American prosperity for it." "You see that with the proposed moratorium in New York on data centers. Turning our back on AI would be as consequential a mistake as turning our back on the atom in the '70s." "It's just the beginning, but we're starting to see a burgeoning US open model ecosystem forming." "NVIDIA's Nemotron models are very good." "I think you're going to see in the next month a slate of announcements of other American companies, non-frontier labs, neo labs, who are putting out open weight models that companies are going to be able to cheaply fine-tune and capture their own alpha in weights they control." Via Bloomberg

Palantir CTO Shyam Sankar says Chinese open source models pose a "threat to American Prosperity": "These Chinese open source models are really the result of distillation attacks." "Most of this is just stolen American IP from frontier labs, and I think the frontier labs should do more to protect that IP, and that's in their own economic interest." "The gravest threat to us is not homicide, it's suicide." "It's an inability to turn these tokens into economic value and generate American prosperity for it." "You see that with the proposed moratorium in New York on data centers. Turning our back on AI would be as consequential a mistake as turning our back on the atom in the '70s." "It's just the beginning, but we're starting to see a burgeoning US open model ecosystem forming." "NVIDIA's Nemotron models are very good." "I think you're going to see in the next month a slate of announcements of other American companies, non-frontier labs, neo labs, who are putting out open weight models that companies are going to be able to cheaply fine-tune and capture their own alpha in weights they control." Via Bloomberg

Jawwwn

181,054 Aufrufe • vor 2 Monaten

Interesting that in three of my recent conversations with Krishna at Anthropic, Dylan Patel, and Gavin Baker, each said that frontier tokens are capturing the majority of the economic value. Gavin: "An overwhelming amount of the economic returns to AI at the model layer have been at the frontier. That's surprising to me, and I think it's been surprising to a lot of people. This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor." Krishna: "We think the returns to frontier intelligence are extremely high. Customers invest really heavily in more tokens with the newer models. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. The returns to frontier intelligence are not slowing down." Dylan: "No one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things."

Interesting that in three of my recent conversations with Krishna at Anthropic, Dylan Patel, and Gavin Baker, each said that frontier tokens are capturing the majority of the economic value. Gavin: "An overwhelming amount of the economic returns to AI at the model layer have been at the frontier. That's surprising to me, and I think it's been surprising to a lot of people. This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor." Krishna: "We think the returns to frontier intelligence are extremely high. Customers invest really heavily in more tokens with the newer models. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. The returns to frontier intelligence are not slowing down." Dylan: "No one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things."

Patrick OShaughnessy

121,392 Aufrufe • vor 4 Monaten

Gavin Baker Gavin Baker says open source is "awesome" for AI infrastructure names because it shifts the economic value from the frontier labs to the infrastructure, and it is already 80% plus of all tokens processed. "A misconception that a lot of people have is that open source models are somehow bad for AI. They're awesome for the AI infrastructure providers. They just shift economic value from the margins of the frontier labs to the infrastructure." "But I do think there's still a role for these frontier models. And it may be true, to date, frontier tokens are capturing 90% of the economic value, and open source tokens are probably 80% plus of tokens processed, and those ratios may be here to stay." _______ Follow Fireside Alpha for more highlights of the best business and technology conversations.

Gavin Baker Gavin Baker says open source is "awesome" for AI infrastructure names because it shifts the economic value from the frontier labs to the infrastructure, and it is already 80% plus of all tokens processed. "A misconception that a lot of people have is that open source models are somehow bad for AI. They're awesome for the AI infrastructure providers. They just shift economic value from the margins of the frontier labs to the infrastructure." "But I do think there's still a role for these frontier models. And it may be true, to date, frontier tokens are capturing 90% of the economic value, and open source tokens are probably 80% plus of tokens processed, and those ratios may be here to stay." _______ Follow Fireside Alpha for more highlights of the best business and technology conversations.

Fireside Alpha

162,832 Aufrufe • vor 2 Monaten

Open models are closing the intelligence gap, says ollama's Jeffrey Morgan. That opens up a new opportunity for founders: coordinating fast, cheap models to solve more complex problems. “We’re maybe less than three months behind between the frontier closed models and the open models. But the next problem to solve is extreme efficiency.” “They’re good enough for 80% of the tasks, they’re really fast and they’re ultra cheap.” “That class of models, in my mind, will be the first ones that come down to this idea of the unlimited tokens.” “Being able to coordinate these flash models together to do different tasks can also yield great results that a bigger model can.”

Open models are closing the intelligence gap, says ollama's Jeffrey Morgan. That opens up a new opportunity for founders: coordinating fast, cheap models to solve more complex problems. “We’re maybe less than three months behind between the frontier closed models and the open models. But the next problem to solve is extreme efficiency.” “They’re good enough for 80% of the tasks, they’re really fast and they’re ultra cheap.” “That class of models, in my mind, will be the first ones that come down to this idea of the unlimited tokens.” “Being able to coordinate these flash models together to do different tasks can also yield great results that a bigger model can.”

Y Combinator

58,126 Aufrufe • vor 1 Monat

Hacktron founder s1r1us on why frontier models are still far ahead of open models for real-world hacking: "The frontier models are insanely capable. There's a huge gap. You don't even want to use Kimi K3 when you have access to GPT 5.6." "We can't really use Opus 5 anymore. Every time there's a new model release, you kind of don't want to work with the previous model." "Open models, we don't really use them. It's incredibly hard to hand hold them to do the exploitation. They're not yet there." Hacktron AI Harsh Jaiswal

Hacktron founder s1r1us on why frontier models are still far ahead of open models for real-world hacking: "The frontier models are insanely capable. There's a huge gap. You don't even want to use Kimi K3 when you have access to GPT 5.6." "We can't really use Opus 5 anymore. Every time there's a new model release, you kind of don't want to work with the previous model." "Open models, we don't really use them. It's incredibly hard to hand hold them to do the exploitation. They're not yet there." Hacktron AI Harsh Jaiswal

MTS

15,156 Aufrufe • vor 21 Tagen

SITUATION EXPLAINED: What happens to open source when the US restricts access to frontier AI models? We asked Adrian Dittmann: "China is releasing these models [open source] to gain international dominance, people will work on these things and say, 'Hey, maybe I can contribute to this if it's open source.' This is the thing that actually gets them talent." "If you're not allowed into the next GPT release or you're not allowed to use Fable, then what are you gonna do? You're stuck with the existing stuff, or you will have to use an open source model that might have subtly better capabilities." "The United States will catch up to open source in some form as well. They seem to just be maximizing product creation." "The problem with open source models is you have to be kind of a nerd in order to use them properly. Consumer facing, the effects will be minimal because the average person doesn't care about open source models. They only care whether or not they can do a quick search with Gemini or ChatGPT."

SITUATION EXPLAINED: What happens to open source when the US restricts access to frontier AI models? We asked Adrian Dittmann: "China is releasing these models [open source] to gain international dominance, people will work on these things and say, 'Hey, maybe I can contribute to this if it's open source.' This is the thing that actually gets them talent." "If you're not allowed into the next GPT release or you're not allowed to use Fable, then what are you gonna do? You're stuck with the existing stuff, or you will have to use an open source model that might have subtly better capabilities." "The United States will catch up to open source in some form as well. They seem to just be maximizing product creation." "The problem with open source models is you have to be kind of a nerd in order to use them properly. Consumer facing, the effects will be minimal because the average person doesn't care about open source models. They only care whether or not they can do a quick search with Gemini or ChatGPT."

MTS

14,406 Aufrufe • vor 3 Monaten

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

dnap

141,614 Aufrufe • vor 3 Monaten

one of the most challenging tasks for frontier models is being able to extract thousands of values from extremely dense tables in documents. our new Extract v2.5 agents are able to get 93%-96%+ on long-list extraction, including cells that fall in between pages. in contrast, astra gets ~30% accuracy over this benchmark. in these cases, frontier VLMs can stop early or miss repeated records. they also struggle to attribute each value back to the source. we are able to attribute every single extracted value back to the source. check out the video below, the blog post, and llamaparse! blog: llamaparse:

one of the most challenging tasks for frontier models is being able to extract thousands of values from extremely dense tables in documents. our new Extract v2.5 agents are able to get 93%-96%+ on long-list extraction, including cells that fall in between pages. in contrast, astra gets ~30% accuracy over this benchmark. in these cases, frontier VLMs can stop early or miss repeated records. they also struggle to attribute each value back to the source. we are able to attribute every single extracted value back to the source. check out the video below, the blog post, and llamaparse! blog: llamaparse:

Jerry Liu

13,103 Aufrufe • vor 8 Tagen

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

62,394 Aufrufe • vor 2 Monaten

learned a lot from this conversation with Simon Mo and Matt Bornstein. biggest takeaways for me: -there are a lot of reasons why we should like open-weight models. a lot of these arguments stop at handwavy things like "what if the labs stop releasing frontier models to the public" or "it's lower cost." but simon's position as lead maintainer of vLLM and CEO of Inferact give him authority to talk about some of the other, more interesting and concrete reasons to pay attention to open-weight models, namely that they allow end-users to calibrate latency / other performance metrics with way more customizability than what any of the frontier closed-source labs offer (and without the fear that your job might be met with a refusal at some random point where you're deep in a 2 hour job) -re: the above point...for this reason, a lot of US companies (inferact included!) choose to use open-weight models over their closed-source alternatives. this also isn't limited to internal workloads / research - on a recent a16z podcast the team at Decagon spoke about how something like 90% of their customer service ai agents run on open-weight models that they've fine-tuned. -we should really appreciate how many companies/teams came out researchers fascinated by the wave of very small open-weight models that were being distilled from e.g. gpt-3.5 and earlier models in 2022/2023 (prior to the release of chatGPT!). these small models motivated the development of pagedattention, which then led to vlmm/inferact (at other layers of the stack with similar origin stories, you can look at teams like openrouter or ollama). in other words, we have open-weight models to thank for a bunch of the orchestration infra we now rely on. i think yet another, indirect, way we can point to open-source/weight infra pushing the frontier forward. anyway, a lot more in this convo, it was a lot of fun!

learned a lot from this conversation with Simon Mo and Matt Bornstein. biggest takeaways for me: -there are a lot of reasons why we should like open-weight models. a lot of these arguments stop at handwavy things like "what if the labs stop releasing frontier models to the public" or "it's lower cost." but simon's position as lead maintainer of vLLM and CEO of Inferact give him authority to talk about some of the other, more interesting and concrete reasons to pay attention to open-weight models, namely that they allow end-users to calibrate latency / other performance metrics with way more customizability than what any of the frontier closed-source labs offer (and without the fear that your job might be met with a refusal at some random point where you're deep in a 2 hour job) -re: the above point...for this reason, a lot of US companies (inferact included!) choose to use open-weight models over their closed-source alternatives. this also isn't limited to internal workloads / research - on a recent a16z podcast the team at Decagon spoke about how something like 90% of their customer service ai agents run on open-weight models that they've fine-tuned. -we should really appreciate how many companies/teams came out researchers fascinated by the wave of very small open-weight models that were being distilled from e.g. gpt-3.5 and earlier models in 2022/2023 (prior to the release of chatGPT!). these small models motivated the development of pagedattention, which then led to vlmm/inferact (at other layers of the stack with similar origin stories, you can look at teams like openrouter or ollama). in other words, we have open-weight models to thank for a bunch of the orchestration infra we now rely on. i think yet another, indirect, way we can point to open-source/weight infra pushing the frontier forward. anyway, a lot more in this convo, it was a lot of fun!

Elena

12,922 Aufrufe • vor 2 Monaten

Engram cofounder Jack Morris just raised $98M to build a new type of AI. He says models don't need to get smarter over time. Instead, they just need to know you better and better over time. Jack describes what he's building: "Our product is a new type of AI. We have a pretty different vision from a lot of the frontier labs, which are working on one model per lab, and trying to make that model smarter every month." "There's another way to think about it, which is that the model doesn't need to get smarter every month. It needs to know you better." "So we're working on a whole different stack, which is a way to train models that train themselves to know your world better and adjust to the things that you say." "So: new ways of training, new ways of running the models."

Engram cofounder Jack Morris just raised $98M to build a new type of AI. He says models don't need to get smarter over time. Instead, they just need to know you better and better over time. Jack describes what he's building: "Our product is a new type of AI. We have a pretty different vision from a lot of the frontier labs, which are working on one model per lab, and trying to make that model smarter every month." "There's another way to think about it, which is that the model doesn't need to get smarter every month. It needs to know you better." "So we're working on a whole different stack, which is a way to train models that train themselves to know your world better and adjust to the things that you say." "So: new ways of training, new ways of running the models."

TBPN

70,731 Aufrufe • vor 3 Monaten

Sarah tries to know the 250 or so people pushing the frontier of AI research. I asked her what's changed in how that group is thinking in the last 6 months: "I don't think every researcher doing frontier work at these labs feels like they're essential to the machine. With recursive self-improvement of AI research models that can improve the models themselves, we are a 1-2 years away from some sort of exponential intelligence. That belief is new within the last 12 months for a lot of researchers. There is some sense of the major labs are so compute-intensive and so large from a headcount perspective now that the sense of contribution, of "I can move the needle." If OpenAI has 200 people, there's not that many researchers. The question of how do we get there is really up to every single person. Now if the question is, well, I need $750 billion of compute spend, I think people feel less ownership of the outcome. I definitely think there's a large contingent of researchers who would feel that one of two things is now true. What I do doesn't matter anyway because the model is gonna do it. Or, the only thing that matters is compute scale. And both of those are somewhat disempowering."

Sarah tries to know the 250 or so people pushing the frontier of AI research. I asked her what's changed in how that group is thinking in the last 6 months: "I don't think every researcher doing frontier work at these labs feels like they're essential to the machine. With recursive self-improvement of AI research models that can improve the models themselves, we are a 1-2 years away from some sort of exponential intelligence. That belief is new within the last 12 months for a lot of researchers. There is some sense of the major labs are so compute-intensive and so large from a headcount perspective now that the sense of contribution, of "I can move the needle." If OpenAI has 200 people, there's not that many researchers. The question of how do we get there is really up to every single person. Now if the question is, well, I need $750 billion of compute spend, I think people feel less ownership of the outcome. I definitely think there's a large contingent of researchers who would feel that one of two things is now true. What I do doesn't matter anyway because the model is gonna do it. Or, the only thing that matters is compute scale. And both of those are somewhat disempowering."

Patrick OShaughnessy

88,615 Aufrufe • vor 1 Monat

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 Aufrufe • vor 2 Monaten

Neil on open source v. frontier models: "The labs pay an immense premium to be 3-6 months ahead of everything else. I think that's probably still worth it. But I don't know that the premium is going to last that long. If you look at enterprise deployments, they don't move at three to six month speed. A lot of enterprises are probably still on Opus 4.6 or Opus 4.7. I don't think there's any way to call a winner in this race, and certainly I don't even think this is a race that can be decided ever. It's a continual process. I don't think open source ever goes away. If there's a vacuum because one leader steps out, a new leader will step in. It just gets easier every day to train a frontier class model."

Neil on open source v. frontier models: "The labs pay an immense premium to be 3-6 months ahead of everything else. I think that's probably still worth it. But I don't know that the premium is going to last that long. If you look at enterprise deployments, they don't move at three to six month speed. A lot of enterprises are probably still on Opus 4.6 or Opus 4.7. I don't think there's any way to call a winner in this race, and certainly I don't even think this is a race that can be decided ever. It's a continual process. I don't think open source ever goes away. If there's a vacuum because one leader steps out, a new leader will step in. It just gets easier every day to train a frontier class model."

Patrick OShaughnessy

32,180 Aufrufe • vor 1 Monat

Gavin Baker says open source AI actually makes closed models more valuable "Open source makes frontier tokens more valuable. If you have a 250 IQ intellect that can orchestrate these open source 150 IQ intellects, that makes the 250 IQ intellect more valuable because it can farm out tasks." "If we're going to use the Manhattan Project analogy, everybody talks about Richard Feynman. But they had Oppenheimer and effectively 30 Nobel laureates. But they needed 2,000 physicists who were top 10,000 in the world." "Having more affordable intelligence actually makes the frontier even more valuable. A very plausible outcome is one in which frontier tokens are 65 to 85% of the economic value. Open source tokens are like 80% of the volume. And that's good for everyone. And everyone wins."

Gavin Baker says open source AI actually makes closed models more valuable "Open source makes frontier tokens more valuable. If you have a 250 IQ intellect that can orchestrate these open source 150 IQ intellects, that makes the 250 IQ intellect more valuable because it can farm out tasks." "If we're going to use the Manhattan Project analogy, everybody talks about Richard Feynman. But they had Oppenheimer and effectively 30 Nobel laureates. But they needed 2,000 physicists who were top 10,000 in the world." "Having more affordable intelligence actually makes the frontier even more valuable. A very plausible outcome is one in which frontier tokens are 65 to 85% of the economic value. Open source tokens are like 80% of the volume. And that's good for everyone. And everyone wins."

dnap

28,807 Aufrufe • vor 1 Monat

CNBC described China’s open-source AI as “pushing the U.S. to compete at the lower end of the value chain,” initially trying to save face for Silicon Valley. The comments section immediately tore that framing apart: "Chinese models are approaching frontier-level capability while remaining absurdly cheap." "This is not the 'lower end' of the value chain." It is forcing expensive American models to answer a much uglier question: Why are you charging tens or even hundreds of times more for the same work? The most revealing part was that people saw through the euphemism immediately: “Lower end” is just cope. American labs are not protecting the high end. They are protecting high profit margins. China’s open-source models are turning frontier capability into a commodity, crushing prices, and freeing developers from brand worship. Suddenly, the American myth of closed-source superiority, premium pricing, and inflated valuations looks less like technological leadership— and more like an ordinary handbag with a luxury label.

𝘊𝘰𝘳𝘳𝘪𝘯𝘦

10,695 Aufrufe • vor 2 Monaten

🚨David Ondrej exposes the truth about the frontier labs. “The progress isn’t slowing down for them, it’s slowing down for us the slaves” “If you think Mythos or Fable 5 is the best model Anthropic has you’re delusional” this is the first time ever that the best AI models are not publicly accessible Anthropic has fear mongered every single human and is now playing the victim with OpenAI after getting “banned” by the U.S government. THIS NEEDS TO WAKE YOU UP frontier models moving forward will become more restricted, less accessible, and more expensive and the only way to combat that is with open-source models. Investing into a DGX spark or 5090 Linux for localized intelligence and building your own AI lab is where the future is heading. every single person needs to start contributing to open-source together for the betterment of society.

🚨David Ondrej exposes the truth about the frontier labs. “The progress isn’t slowing down for them, it’s slowing down for us the slaves” “If you think Mythos or Fable 5 is the best model Anthropic has you’re delusional” this is the first time ever that the best AI models are not publicly accessible Anthropic has fear mongered every single human and is now playing the victim with OpenAI after getting “banned” by the U.S government. THIS NEEDS TO WAKE YOU UP frontier models moving forward will become more restricted, less accessible, and more expensive and the only way to combat that is with open-source models. Investing into a DGX spark or 5090 Linux for localized intelligence and building your own AI lab is where the future is heading. every single person needs to start contributing to open-source together for the betterment of society.

Vadim

83,366 Aufrufe • vor 3 Monaten