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"83% want open source models but can't access the infra" io.net CMO Jack Collier exposed AI's broken infrastructure at SuperAI last week. Watch his keynote to find out: 🏛️ Why 3 companies control 65% of all compute 💤 How 90% of global GPUs sit idle 🚀 The DePIN solution...

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15,198 Aufrufe • vor 1 Jahr •via X (Twitter)

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@superai_conf

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Scan any documents, convert images into text, PDF files, etc. 👍

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Get ready to hear the truth. Jack Collier didn’t just give a talk he exposed how centralization is choking the progress of AI. 🔒 In a world where 3 companies control 65% of all compute, 🛑 And 90% of global GPUs sit idle, 🌍 83% of people want open-source models but can’t access the infrastructure. @ionet is breaking those chains with the DePIN revolution already processing over 1 billion tokens per week. The future isn’t centralized, it’s decentralized! ⚡

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DDAY119 | io.net & O.XYZvor 1 Jahr

@superai_conf Jack Collier nailed it at @superai_conf 👏 AI’s infrastructure is broken concentrated, inefficient, and gatekept. Enter @ionet and DePIN real solutions, real scale already processing 1B+ tokens/week. Open source AI needs open access. This is how we unlock it.

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@superai_conf #ionet

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@superai_conf 🔥🔥

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@superai_conf 🔥🔥🔥

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@superai_conf 🔥🔥🔥

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Jack Collier Thank you for highlighting these critical stats 65% of compute held by just three players and 90% of GPUs idle is eye-opening. As a Web3 & AI enthusiast, I’m excited by IONET’s DePIN approach handling 1B+ tokens/week. How can the community help drive open-source infra-adoption?

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@superai_conf LFG 🚀

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"65% of global compute is controlled by just three providers — and the more we hand over compute power and data to them, the more we risk building an AI future owned by them, not by us." - Jack Collier (Jack Collier), Chief Growth & Marketing Manager at io.net Jack Collier reveals a critical paradox: open source AI models are ready to transform industries, democratize innovation, and empower creators, yet adoption is being throttled by centralized infrastructure, hidden costs, and complex workflows. Standing on the RAISE 2025 Stage, he breaks down the myths that Open AI is too expensive, too weak, or too complicated — and showcases how distributed compute, unified open platforms, and training-as-a-service are not only solving these challenges but also creating a future where AI is accessible, fair, and owned by the many, not the few. Watch now: #RAISE2025 #OpenSourceAI #AIInfrastructure #FutureOfAI #RAISESummit #DistributedCompute #Innovation
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"65% of global compute is controlled by just three providers — and the more we hand over compute power and data to them, the more we risk building an AI future owned by them, not by us." - Jack Collier (Jack Collier), Chief Growth & Marketing Manager at io.net Jack Collier reveals a critical paradox: open source AI models are ready to transform industries, democratize innovation, and empower creators, yet adoption is being throttled by centralized infrastructure, hidden costs, and complex workflows. Standing on the RAISE 2025 Stage, he breaks down the myths that Open AI is too expensive, too weak, or too complicated — and showcases how distributed compute, unified open platforms, and training-as-a-service are not only solving these challenges but also creating a future where AI is accessible, fair, and owned by the many, not the few. Watch now: #RAISE2025 #OpenSourceAI #AIInfrastructure #FutureOfAI #RAISESummit #DistributedCompute #Innovation

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Last week, Anthropic said its new model Mythos had already found “thousands of high-severity vulnerabilities” and could “surpass all but the most skilled humans at finding and exploiting software vulnerabilities.” At almost the same moment, the Axios hack showed how exposed the open-source foundation under modern software still is. For this week’s 10 Minutes or Less, I spoke with Feross what this moment means. WATCH:
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Last week, Anthropic said its new model Mythos had already found “thousands of high-severity vulnerabilities” and could “surpass all but the most skilled humans at finding and exploiting software vulnerabilities.” At almost the same moment, the Axios hack showed how exposed the open-source foundation under modern software still is. For this week’s 10 Minutes or Less, I spoke with Feross what this moment means. WATCH:

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Wouter Teunissen

51,746 Aufrufe • vor 2 Jahren

When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!
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When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!

Sami Kassab

35,706 Aufrufe • vor 8 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!
2:50

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!

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12,922 Aufrufe • vor 1 Monat

STOP PAYING FOR TOKENS ! START GETTING PAID INSTEAD Introducing Straitly, the first AI service that PAYS YOU for using tokens Unlike > Openrouter charges 5.5% service fee > Vercel charges 2.9% payment fee > Anthropic gives you low rate limits > OpenAI only lets you use GPT models Straitly: > PAYS YOU 5% CASHBACK on all closed-source models > 10% CASHBACK on open-source models > High rate limits > 177 AI models > 99.8% reliability > ZDR This means that you can save up to 25% on your token bills compared to using services like OpenRouter or Vercel, while still getting the same reliability and model choices Since our waitlist launch 3 weeks ago we have processed ~150B tokens already, and are growing at 35% week over week We're backed by LeapYear, Sand Hill North, and other incredible angels in our mission to build the utility layer for all future token usage This is just the beginning Today Straitly is live and open for public access Start getting paid today at To celebrate, we are giving out $10k in free AI credits to 400 people Comment below what your are building and what model you use the most, then head over to Straitly and sign up We'll select the winners at the end of September !
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Mujtaba

194,402 Aufrufe • vor 23 Tagen

Nvidia CEO Jensen Huang on why engineers will soon be paid in tokens, not just salary: Jensen lays out a future where compute access becomes part of an engineer's compensation package. "I could totally imagine in the future every single engineer in our company will need an annual token budget," he says. He explains how the math would work: "They're going to make a few hundred thousand a year their base pay. I'm going to give them probably half of that on top of it as tokens so that they could be amplified 10x. Of course, we would." According to Huang, this is already changing how companies compete for talent: "It is now one of the recruiting tools in Silicon Valley. How many tokens comes along with my job?" His reasoning is simple: tokens make engineers more productive. As he puts it, "every engineer that has access to tokens will be more productive and those tokens as you know will be produced by AI factories that all of you and us we partner to build." Huang then zooms out to describe how this reshapes the nature of companies themselves: "Every single enterprise company in today sit on top of file systems and data centers. Every single software company of the future will be agentic and they will be token manufacturers. They be token users for their engineers and they'll be token manufacturers for all of their customers."
1:04

Nvidia CEO Jensen Huang on why engineers will soon be paid in tokens, not just salary: Jensen lays out a future where compute access becomes part of an engineer's compensation package. "I could totally imagine in the future every single engineer in our company will need an annual token budget," he says. He explains how the math would work: "They're going to make a few hundred thousand a year their base pay. I'm going to give them probably half of that on top of it as tokens so that they could be amplified 10x. Of course, we would." According to Huang, this is already changing how companies compete for talent: "It is now one of the recruiting tools in Silicon Valley. How many tokens comes along with my job?" His reasoning is simple: tokens make engineers more productive. As he puts it, "every engineer that has access to tokens will be more productive and those tokens as you know will be produced by AI factories that all of you and us we partner to build." Huang then zooms out to describe how this reshapes the nature of companies themselves: "Every single enterprise company in today sit on top of file systems and data centers. Every single software company of the future will be agentic and they will be token manufacturers. They be token users for their engineers and they'll be token manufacturers for all of their customers."

Big Brain AI

90,171 Aufrufe • vor 4 Monaten

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
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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 1 Monat

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."
2:06

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

110,354 Aufrufe • vor 2 Monaten

Last Christmas at brunch, my friend Peter showed me the latest version of his little bot he built. A crazy side project. Just for fun. This Monday he announced he's joining OpenAI. Sam Altman called him a genius. In between? OpenClaw went from a hobby project to the most talked-about AI agent in the world. Three months. No team. No funding. Just one retired guy from Vienna who rediscovered the joy of building things. ❤️ Last week I flew down to Vienna to sit down with him right in the middle of this madness to talk about everything: how he thinks about agents, why he let OpenClaw write its own soul file, why your mom will understand AI agents before most engineers do, and why open source as we know it might be completely broken. We are super proud of you, Peter. 🙌 Chapters: 00:00 Intro 01:02 The mom test moment & Managing 3,000 PRs 04:47 The Soul Markdown file & why OpenClaw feels different 06:58 The agent's personality, humor & OpenClaw's wild week 10:31 Think like the model 12:37 Privacy & security 15:28 Open source burnout & the future of engineering teams 17:32 The end of apps & closing thoughts
19:56

Last Christmas at brunch, my friend Peter showed me the latest version of his little bot he built. A crazy side project. Just for fun. This Monday he announced he's joining OpenAI. Sam Altman called him a genius. In between? OpenClaw went from a hobby project to the most talked-about AI agent in the world. Three months. No team. No funding. Just one retired guy from Vienna who rediscovered the joy of building things. ❤️ Last week I flew down to Vienna to sit down with him right in the middle of this madness to talk about everything: how he thinks about agents, why he let OpenClaw write its own soul file, why your mom will understand AI agents before most engineers do, and why open source as we know it might be completely broken. We are super proud of you, Peter. 🙌 Chapters: 00:00 Intro 01:02 The mom test moment & Managing 3,000 PRs 04:47 The Soul Markdown file & why OpenClaw feels different 06:58 The agent's personality, humor & OpenClaw's wild week 10:31 Think like the model 12:37 Privacy & security 15:28 Open source burnout & the future of engineering teams 17:32 The end of apps & closing thoughts

Andreas Klinger 🦾

160,390 Aufrufe • vor 7 Monaten

Quick chat with dylan ツ (Dylan Bristot, GTM @ $NBIS). Also on YouTube (link in first comment) for those who prefer to watch/listen there. 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But probably less mature than the headlines suggest.” → Implication: Enterprise demand is real, but deployment maturity still has significant room to improve. “The POC problem might be solved for a lot of companies, but the production problem isn’t yet.” → Implication: The enterprise bottleneck is shifting from proving AI works to deploying it reliably, securely and economically at scale. “They feel like it’s time for them to actually not only integrate AI, but build some sort of moat out of the AI.” → Implication: Enterprises increasingly want proprietary AI systems built around their own data rather than simply consuming generic models. “The more autonomous the software becomes, the more infra discipline you need underneath it.” → Implication: Agents increase the importance of inference cost, reliability and infrastructure optimization. “Maybe I have fifteen different versions of very different LLMs, fine tuned on fifteen different kinds of tasks that I’m operating across my business, instead of having a one model fits all.” → Implication: Enterprise AI could evolve toward many specialized models rather than one frontier model handling every workload. “I don’t necessarily think it’s open versus closed. That might be the wrong framing.” → Implication: Open and closed models can coexist because they optimize for different customer needs. “Historically the problem was that that control came with a massive operational tax.” → Implication: Better inference infrastructure can make open models materially more competitive by removing the complexity traditionally associated with running them. “I don’t think open needs to beat the best closed model on every single benchmark. It just basically needs to be good enough for the workload of the given customer while offering a much better combination of control, cost, and deployment flexibility.” → Implication: For production AI, workload-specific economics may matter more than having the absolute smartest model. “Maybe actually the bulk of tokens generated in the future might come from open models.” → Implication: Frontier intelligence could remain dominated by closed labs even while open models capture most production inference volume. “I could really imagine frontier intelligence being really concentrated while most of the production inference becomes super fragmented.” → Implication: AI could consolidate at the intelligence layer while fragmenting heavily at the inference layer across models, GPUs, providers and regions. “I don’t think that necessarily means the margins of open source will be much worse than the ones of closed source.” → Implication: Optimization can potentially make open-model inference highly profitable despite lower pricing. “I think now we’re probably in the middle of phase two... everything feeding the accelerator.” → Implication: The AI trade is broadening beyond GPUs toward networking, packaging, data centers, electrical equipment and power. “It’s no longer about the megawatts, about energized megawatts.” → Implication: Available power on paper matters less than how quickly that power can actually be delivered to operating AI infrastructure. “It’s increasingly about utilisation and conversion now and like how efficiently you convert expensive infra into actual useful AI work.” → Implication: Infrastructure efficiency and utilization become increasingly important as the absolute amount of deployed AI infrastructure grows. “The market tends to really notice demand before it notices what demand breaks.” → Implication: Second-order bottlenecks may offer some of the most interesting opportunities in the next phase of the AI buildout. “The interesting question now is which part of the mine breaks next?” → Implication: Finding the next constraint in the AI supply chain may matter more than simply identifying continued AI demand.
34:29

Quick chat with dylan ツ (Dylan Bristot, GTM @ $NBIS). Also on YouTube (link in first comment) for those who prefer to watch/listen there. Timestamps 00:00 – Dylan's role at Nebius and Nebius Token Factory 01:48 – Dylan's investing philosophy and portfolio approach 05:07 – How working in AI infrastructure influences his investing 08:29 – Training vs. inference and why inference demand could explode 13:22 – Enterprise AI adoption: from POCs to production 18:02 – Open-source vs. closed/frontier models 24:44 – The economics of open vs. closed AI models 29:27 – Where the next AI infrastructure bottlenecks could emerge 31:22 – Dylan's AI Bottlenecks project and approach to stock selection 34:06 – Closing thoughts Key Insights (AI Summary, so you don't have to copy paste and prompt for exactly that ;D) “I seem to like areas where the demand really looks kind of secular, but the supply is genuinely hard to create.” → Implication: The most attractive AI trades may sit in physical bottlenecks where supply cannot quickly respond to demand. “The bottleneck is who has the pricing power and kind of what might get commoditized and where the concentrate might move next.” → Implication: Value capture across the AI stack will keep shifting as individual layers become scarce or commoditized. “Training creates the intelligence and then the inference actually monetizes and distributes.” → Implication: Training and inference are complementary, rather than one ultimately replacing the other. “One user action can become dozens or hundreds of model calls, tools calls, and like verification steps, retries.” → Implication: Agentic AI can drive token consumption far faster than user growth alone would suggest. “The best infra for making any model and the best infra for serving a billion interactions are not necessarily the same.” → Implication: Training and inference could increasingly require different hardware and infrastructure architectures. “The Frontier Labs might be incentivized to run more and more of the inference of these models for internal research instead of providing it to external people.” → Implication: The most capable models and their compute could increasingly be used internally to accelerate frontier research rather than monetized externally. “Enterprise AI adoption is actually much further along than a lot of people kind of think. But probably less mature than the headlines suggest.” → Implication: Enterprise demand is real, but deployment maturity still has significant room to improve. “The POC problem might be solved for a lot of companies, but the production problem isn’t yet.” → Implication: The enterprise bottleneck is shifting from proving AI works to deploying it reliably, securely and economically at scale. “They feel like it’s time for them to actually not only integrate AI, but build some sort of moat out of the AI.” → Implication: Enterprises increasingly want proprietary AI systems built around their own data rather than simply consuming generic models. “The more autonomous the software becomes, the more infra discipline you need underneath it.” → Implication: Agents increase the importance of inference cost, reliability and infrastructure optimization. “Maybe I have fifteen different versions of very different LLMs, fine tuned on fifteen different kinds of tasks that I’m operating across my business, instead of having a one model fits all.” → Implication: Enterprise AI could evolve toward many specialized models rather than one frontier model handling every workload. “I don’t necessarily think it’s open versus closed. That might be the wrong framing.” → Implication: Open and closed models can coexist because they optimize for different customer needs. “Historically the problem was that that control came with a massive operational tax.” → Implication: Better inference infrastructure can make open models materially more competitive by removing the complexity traditionally associated with running them. “I don’t think open needs to beat the best closed model on every single benchmark. It just basically needs to be good enough for the workload of the given customer while offering a much better combination of control, cost, and deployment flexibility.” → Implication: For production AI, workload-specific economics may matter more than having the absolute smartest model. “Maybe actually the bulk of tokens generated in the future might come from open models.” → Implication: Frontier intelligence could remain dominated by closed labs even while open models capture most production inference volume. “I could really imagine frontier intelligence being really concentrated while most of the production inference becomes super fragmented.” → Implication: AI could consolidate at the intelligence layer while fragmenting heavily at the inference layer across models, GPUs, providers and regions. “I don’t think that necessarily means the margins of open source will be much worse than the ones of closed source.” → Implication: Optimization can potentially make open-model inference highly profitable despite lower pricing. “I think now we’re probably in the middle of phase two... everything feeding the accelerator.” → Implication: The AI trade is broadening beyond GPUs toward networking, packaging, data centers, electrical equipment and power. “It’s no longer about the megawatts, about energized megawatts.” → Implication: Available power on paper matters less than how quickly that power can actually be delivered to operating AI infrastructure. “It’s increasingly about utilisation and conversion now and like how efficiently you convert expensive infra into actual useful AI work.” → Implication: Infrastructure efficiency and utilization become increasingly important as the absolute amount of deployed AI infrastructure grows. “The market tends to really notice demand before it notices what demand breaks.” → Implication: Second-order bottlenecks may offer some of the most interesting opportunities in the next phase of the AI buildout. “The interesting question now is which part of the mine breaks next?” → Implication: Finding the next constraint in the AI supply chain may matter more than simply identifying continued AI demand.

Daniel Koss

49,970 Aufrufe • vor 1 Monat

The future doesn't just happen. Someone has to build it. Last week I sat down with Zach Abrams (Bridge → Stripe) to talk about going from a Slack integration to a $1B exit in under 2 years. Bridge was not an obvious bet in 2023. For one thing the timing for Bridge's launch was absolutely terrible. Terra Luna collapsed. FTX imploded. USDC depegged the week they launched. Every bank partner went under. But here's what actually happened: Bridge didn't wait for the stablecoin market to recover. They proved it out one use case at a time—cross-border payments, neo-bank infra, government payouts. First customer? Nicholas from Zulu, a fintech in Colombia messaging them on Slack. All their early customers came from Twitter. Zero enterprise sales playbook. Just compounding chaos that somehow looked like a hockey stick from the outside. In this conversation, Zach breaks down: - Why he's worked at every major fintech (Square, Coinbase, Brex) - How they built with 1 designer and API docs - What PMF actually feels like as a founder (spoiler: always broken) - Why the Collisons wanted to acquire Bridge and he asked one of his own investors to talk stripe out of the deal (!!) - His surprisingly bearish take on SF because of AI Full video 👇
39:08

The future doesn't just happen. Someone has to build it. Last week I sat down with Zach Abrams (Bridge → Stripe) to talk about going from a Slack integration to a $1B exit in under 2 years. Bridge was not an obvious bet in 2023. For one thing the timing for Bridge's launch was absolutely terrible. Terra Luna collapsed. FTX imploded. USDC depegged the week they launched. Every bank partner went under. But here's what actually happened: Bridge didn't wait for the stablecoin market to recover. They proved it out one use case at a time—cross-border payments, neo-bank infra, government payouts. First customer? Nicholas from Zulu, a fintech in Colombia messaging them on Slack. All their early customers came from Twitter. Zero enterprise sales playbook. Just compounding chaos that somehow looked like a hockey stick from the outside. In this conversation, Zach breaks down: - Why he's worked at every major fintech (Square, Coinbase, Brex) - How they built with 1 designer and API docs - What PMF actually feels like as a founder (spoiler: always broken) - Why the Collisons wanted to acquire Bridge and he asked one of his own investors to talk stripe out of the deal (!!) - His surprisingly bearish take on SF because of AI Full video 👇

AJ Asver

13,215 Aufrufe • vor 11 Monaten

Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.
1:05

Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.

Milk Road AI

27,908 Aufrufe • vor 2 Monaten

I don’t believe reality is a simulation, but you genuinely couldn’t script this timeline: • Two weeks ago: At swyx’s AI Engineer World’s Fair in SF, I decide at the last minute to introduce my friend Uri Rolls onstage for his talk on cyber benchmarks for infrastructure penetration and access control (see below, amazing team). I say: “There is a future where cyber is alive and everyone is well protected and I’m pretty sure that future involves open-source models.” And later: “A big challenge is going to be speed: the speed of attack versus defense. When an intruder starts to enter, you have to see what’s happening and catch them.” • One week ago: Hugging Face is hit by a sophisticated intrusion over the weekend. The traces look unlike anything we’ve seen before and suggest serious AI involvement, but we don’t yet know which model was used. The closed models we ask for help choke on their guardrails. We need to react fast, so we turn to Z.ai’s GLM-5.2 to help us analyze the attack. • Earlier this week: OpenAI reaches out, discloses what happened, and partners with us on the investigation. The intruder turns out to be exactly what we had discussed two weeks earlier: a fully autonomous agent, powered by an unreleased frontier model, attempting to gain access to part of our infrastructure. Sometimes the timeline we live in is genuinely vertigo-inducing.
16:47

I don’t believe reality is a simulation, but you genuinely couldn’t script this timeline: • Two weeks ago: At swyx’s AI Engineer World’s Fair in SF, I decide at the last minute to introduce my friend Uri Rolls onstage for his talk on cyber benchmarks for infrastructure penetration and access control (see below, amazing team). I say: “There is a future where cyber is alive and everyone is well protected and I’m pretty sure that future involves open-source models.” And later: “A big challenge is going to be speed: the speed of attack versus defense. When an intruder starts to enter, you have to see what’s happening and catch them.” • One week ago: Hugging Face is hit by a sophisticated intrusion over the weekend. The traces look unlike anything we’ve seen before and suggest serious AI involvement, but we don’t yet know which model was used. The closed models we ask for help choke on their guardrails. We need to react fast, so we turn to Z.ai’s GLM-5.2 to help us analyze the attack. • Earlier this week: OpenAI reaches out, discloses what happened, and partners with us on the investigation. The intruder turns out to be exactly what we had discussed two weeks earlier: a fully autonomous agent, powered by an unreleased frontier model, attempting to gain access to part of our infrastructure. Sometimes the timeline we live in is genuinely vertigo-inducing.

Thomas Wolf

79,158 Aufrufe • vor 2 Monaten