Podcasts about asic

On the latest Blockspace roundup, the gang cover's Block's 40% workforce reduction and our scoop that Magic Eden is quitting the Bitcoin and Ethereum NFT game. Get your tickets to OPNEXT 2026 before prices increase! Join us on April 16 in NYC for technical discussions, investor talks, and intimate conversation with the brightest minds in Bitcoin. Welcome back to The Blockspace Podcast! Today, Charlie and Colin cover the Block's 40% workforce reduction and why the stock ripped 20% on the news. We also dive into the bitcoin mining conditions that are driving hashprice to all-time lows, Blockspace's scoop that Magic Eden is sunsetting its Bitcoin Ordinals marketplace, MARA's latest AI partnership, and the Terra/Luna lawsuit against Jane Street. Plus, Luxor's Michael San Miguel joins the show to discuss the ins and outs of the GPU market.  Subscribe to the newsletter! https://newsletter.blockspacemedia.com Notes: * Block laid off 40% of its 10,000 employees. * Block stock surged 20% after the layoff news. * Bitcoin hash price hit an all-time low of $28. * Bitcoin difficulty adjusted upward by 14.73%. * Magic Eden is shutting down BTC and ETH marketplaces, multi-chain wallet * Bitdeer sold all its bitcoin; Cipher plans to sell its bitcoin in 2026 * MARA forms partnership with data center developer Starwood Timestamps: 00:00 Start 03:33 Hashrate update via Luxor's Hashrate Index 09:29 Block lays off 40% of staff 16:37 Magic Eden shutting down 25:54 GPUs & compute 28:03 GPU vs ASIC complexity 29:04 Upgrading hardware 32:16 Finding a compute buyer 34:00 Powershell vs Neocloud 37:12 Compute still in price discovery mode 42:05 MARA earnings 45:20 CIPHER dumping bags 48:44 Jane Street is the new boogyman 59:34 Everyone's short MSTR

Welcome to the Complexity Premia podcast by Coolabah Capital, a hosted by Christopher Joye, Chief Investment Officer and Portfolio Manager at Coolabah Capital, and Ying Yi, a Senior Portfolio Management Director at Coolabah Capital. The Complexity Premia podcast strives to deconstruct modern investment problems for wholesale (not retail) participants in capital markets. You can listen on your favourite podcast app, or you can find it on Spotify, Podbean or Apple Podcasts. In this episode, Chris and Ying Yi run a top-down markets scan: recent performance and the outlook for yields, how hyperscaler AI capex is feeding into bond supply, inflation expectations and term premia, and where value is emerging across major asset classes. They cover the next moves from the RBA and the Fed, implications for housing and growth, and whether AI ultimately proves disinflationary. The conversation closes with the cross-asset tells—USD, gold and bitcoin—and what they're signalling for the year ahead. This information is suitable for wholesale investors only and has been produced by Coolabah Capital Institutional Investments Pty Ltd ACN 605806059, which holds Australian Financial Services Licence No. 482238 (CCII). The views expressed in this recording represent the personal opinions of the speakers and do not represent the view of any other party. The information does not take into account the particular investment objectives or financial situation of any potential listener. It does not constitute, and should not be relied on as, financial or investment advice or recommendations (expressed or implied) and it should not be used as an invitation to take up any investments or investment services. Whilst we believe that the information discussed in the podcast is correct, no warranty or representation is given to this effect, and listeners should not rely on this information when making any decisions. No responsibility can be accepted by CCII to any end users for any action taken on the basis of this information. Any performance data presented on this site is pre-fees for institutional clients that negotiate custom fee rates, and these solutions are not available to retail investors. No investment decision or activity should be undertaken without first seeking qualified and professional advice. CCII may have a financial interest in any assets discussed during the podcast. Listeners in Australia are encouraged to visit ASIC's MoneySmart website to obtain information regarding financial advice and investments.

In 2026, auditors are working in an environment defined by AI technologies, heightened scrutiny, regulatory oversight and ethics demands.  Which is why it's vital that professionals in audit understand this evolving landscape. Additionally, how the regulator ASIC views key issues and what it is focusing on in 2026.  ASIC commissioner Kate O'Rourke is this episode's special guest and she discusses the evolution of audit and financial oversight at a pivotal time.  This episode explores:  The biggest forces currently reshaping the audit and assurance landscape in Australia  The key areas where ASIC expect firms to lift their performance  The role of digital tools, automation and AI in enhancing audit quality, risk detection, and regulatory oversight  The safeguards, ethical standards, and governance structure ASIC expects firms to put in place when integrating advanced analytics or AI into audit processes  Emerging major international trends  How stronger global alignment could shape the future of Australia's audit framework  Capabilities that the next generation of auditors need  How regulators, firms, and professional bodies like CPA Australia can work together to build that future-ready workforce pipeline.  Listen now for expert-led insights from ASIC.  Host: Tiffany Tan, audit and assurance lead, CPA Australia.  Guest: Kate O'Rourke, ASIC commissioner. She previously held senior leadership roles at Treasury and has held executive roles at ASIC overseeing corporate transactions and governance. Kate O'Rourke began her five-year term as an ASIC commissioner in September 2023.  ASIC is Australia's integrated corporate, markets, financial services, and consumer credit regulator. It is an independent Australian government body.  Head to ASIC online for more information on its senior leadership.  Loving this episode?  Listen to more With Interest episodes and other CPA Australia podcasts on YouTube.  And don't forget to click subscribe to the channel for a wide range of content that will help your career.  CPA Australia publishes four podcasts, providing commentary and thought leadership across business, finance and accounting:  With Interest  INTHEBLACK  INTHEBLACK Out Loud  Excel Tips  Search for them in your podcast platform. Email the podcast team at podcasts@cpaaustralia.com.au

DescriptionThis conversation explores the importance of open source in Bitcoin mining, discussing how it can drive innovation, improve efficiency, and create value for the industry. The panelists emphasize the need for collaboration and community contributions to establish standards and develop better tools. They also highlight the potential of heat reuse from Bitcoin mining as a valuable application, and the challenges of creating customized solutions in a competitive mining landscape.TakeawaysOpen source is fundamental to Bitcoin's success.The mining industry has shifted towards proprietary solutions.Innovations in open source can enhance mining efficiency.Heat generated by miners can be repurposed for heating applications.Community collaboration is essential for developing standards.Open source allows for iterative improvements in technology.Building in public fosters creativity and diverse use cases.Custom solutions are necessary for unique mining operations.Contributions can come in various forms, not just code.Investing in open source benefits the entire ecosystem.Chapters00:00 The Open Source Ethos in Bitcoin Mining07:37 Innovations Through Open Source Collaboration14:40 Heat Reuse: A New Perspective on Bitcoin Mining20:08 Building Custom Solutions with Mining OSKeywordsBitcoin, mining, open source, ASIC, innovation, heat reuse, mining OS, collaboration, standards, community

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

"Dok svet priča o ChatGPT-ju, mi otkrivamo hardversku revoluciju iz Beograda koja omogućava da AI uopšte postoji, i to 20 puta brže od svega što ste videli.“ U drugoj epizodi serijala Pojačalo specijala u saradnji sa kompanijom Next Sillicon, Ivan razgovara sa Markom Skakunom, AI Team Leadom u njihovoj beogradskoj kancelariji, o revoluciji u svetu veštačke inteligencije i hardvera koji je pokreće. Marko pruža detaljan istorijski pregled evolucije kompjuterske snage – od generičkih CPU-ova, preko specijalizovanih GPU-ova, pa sve do ultra-efikasnih ASIC čipova. Kroz razgovor se prati i razvoj samog AI-ja, od ranih neuronskih mreža i kompjuterske vizije do "Transformer" arhitekture i "Scaling Laws" fenomena koji su omogućili pojavu masivnih jezičkih modela poput ChatGPT-ja, fundamentalno menjajući zahteve koje postavljamo pred hardver. U drugom delu, fokus se prebacuje na jedinstveni pristup koji NextSilicon primenjuje kako bi odgovorio na ove izazove. Marko detaljno objašnjava inovativnu "dataflow" arhitekturu koja se fundamentalno razlikuje od tradicionalnih rešenja, omogućavajući hardveru da bude fleksibilan, adaptivan i energetski efikasniji. Poseban akcenat je stavljen na beogradsku kancelariju, koja nije samo podrška, već ključni razvojni centar gde timovi rade na najnaprednijim aspektima tehnologije – od dizajna čipa do AI kompajlera. Kroz Markovu ličnu priču, saznajemo zašto je rad na ovakvim "cutting-edge" projektima u Srbiji postao ne samo moguć, već i izuzetno privlačan za vrhunske svetske stručnjake. Podržite nas na BuyMeACoffee: https://bit.ly/3uSBmoa Pročitajte transkript ove epizode: https://bit.ly/4kGroRD Posetite naš sajt i prijavite se na našu mailing listu: http://bit.ly/2LUKSBG Prijavite se na naš YouTube kanal: http://bit.ly/2Rgnu7o Pratite Pojačalo na društvenim mrežama: FB: https://www.facebook.com/PojacaloRS/ IG: https://www.instagram.com/pojacalo.rs/ X: https://x.com/PojacaloRS LN: https://www.linkedin.com/company/pojacalo TikTok: https://www.tiktok.com/@pojacalo.rs

Is off-grid Bitcoin mining finally growing up, and is the oil & gas industry about to become the most unlikely power partner in the game?In this episode, Justin Ballard (@JLB_Oso) and Jake Corley (@jacobcorley) sits down with industry veterans Joel and Bryan to dig into the last three years of hard-won lessons at the intersection of off-grid data centers and oil & gas flow assurance, and what the next chapter looks like for operators willing to do the unglamorous work.From modular data center deployments in the field to navigating flare gas regulations, from ASIC supply chain realities to the quiet consolidation reshaping who gets deals and who gets left out, this conversation is a ground-level view of an industry maturing in real time.We explore: 

Take a Network Break! We start with listener follow-up on data centers in space, and sound the Red Alert about a sandbox failure in Claude Code and a rash of Microsoft zero-days. On the news front, Cisco announces a 102.4Tbps switch ASIC in its Silicon One line of homegrown chips, and adds AI agent monitoring... Read more »

Take a Network Break! We start with listener follow-up on data centers in space, and sound the Red Alert about a sandbox failure in Claude Code and a rash of Microsoft zero-days. On the news front, Cisco announces a 102.4Tbps switch ASIC in its Silicon One line of homegrown chips, and adds AI agent monitoring... Read more »

Take a Network Break! We start with listener follow-up on data centers in space, and sound the Red Alert about a sandbox failure in Claude Code and a rash of Microsoft zero-days. On the news front, Cisco announces a 102.4Tbps switch ASIC in its Silicon One line of homegrown chips, and adds AI agent monitoring... Read more »

ASIC has launched a major review into predatory lead generation services that use social media ads to trick Australians into shifting their superannuation into high-risk schemes. Commissioner Alan Kirkland joined Dean & Sofie on 4BC Breakfast to warn consumers to be wary of 'friendly' offers for free super reviews, advising people to simply hang up if pressured.See omnystudio.com/listener for privacy information.

In this episode of Broker Daily Uncut, host Alex Whitlock is joined by Eva Loisance and Costa Arvanitopoulos to unpack the strong start to the year across Australia's property markets, with auction clearance rates across the capital cities reflecting sustained buyer urgency despite tighter borrowing conditions. While supply remains constrained and investor activity is accelerating, the discussion turns to the practical impact of reduced borrowing capacity, with brokers noting that clients on the same income today can access less than they could two to three years ago. The trio explore how that shift is influencing structuring decisions, lender selection, and turnaround priorities, particularly as some lenders deliver rapid approvals, while others lag in service and communication. The episode also examines ASIC's tightening expectations around best interests duty, with brokers facing more detailed compliance reviews and increasing requirements to clearly justify product selection, especially where a chosen loan is not the cheapest available option. Rounding out the discussion is the growing use of AI tools in mortgage broking, from policy search and complex scenario analysis to SMSF servicing calculations, and whether automation enhances broker efficiency without displacing the human judgement central to complex lending advice.

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From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

1:12 雲端營收成長率，Google暴增至44%反超一大段 4:08 ASIC伺服器成長率宇硬體供應鏈仍是看好的方向 5:53 資本支出全數高於預期，6~7成成長，Google看翻倍 7:45 自由現金流的正負，乃至於[CapEx/OCF]之比例 9:41 Google FCF預期仍為正，Amazon開始要增發債 12:05 股價表現大致反應如此，Google獨強 14:15 OpenAI資金鏈，成為估值黑魔法，扯上關連都跌 16:49 AI進入激烈競爭，適者生存，有人會離開市場 17:48 債券降評、貸款轉售等等潛在事件，是大利空關鍵字 20:05 軟體股10月起就掉隊一直跌，先前podcast就有提及 22:50 平衡報導的重要性，有人漲也有人跌，類股產業權重問題 24:15 AI的遊戲即時世界生成，娛樂對初期商業化應用是必然的 27:30 總結 -- Hosting provided by SoundOn

9 - Vale Sterling First Class Action against ASIC by Australian Citizens Party

Quantum Blockchain Technologies PLC (AIM:QBT) CEO Francesco Gardin talked with Proactive's Stephen Gunnion about the company's participation in the recent Nashville Energy & Mining Summit (NEMS26) at Bitcoin Park in Nashville, a key networking hub for the Bitcoin community. Gardin described the conference as “extremely intensive” and valuable for meeting key industry players, including three ASIC manufacturers with whom the company has signed NDAs. He explained that Quantum Blockchain is progressing well in its relationships with these partners, particularly around its development of Method C, a neural network-based solution requiring ASIC-specific training. “The training is very ASICs oriented, specific ASICs oriented; and that's not something you do overnight,” Gardin noted. A major development for the industry, Gardin said, is the emergence of an open-source stack, including hashing board designs, control board software, and even mining pools. This marks a significant departure from the market dominance of Chinese manufacturers, who have historically restricted access to both ASIC specs and software. “This is for us, really a game changer,” Gardin said, pointing to the availability of chips and the open ecosystem as an opportunity for broader industry participation. He also clarified confusion around QBT's access to source code, explaining that access is being granted incrementally in alignment with the company's agreed path with its partners. For more updates from Quantum Blockchain Technologies and other innovative firms, visit Proactive's YouTube channel. Don't forget to like the video, subscribe to the channel, and enable notifications for future content. #QuantumBlockchain #FrancescoGardin #BitcoinMining #ASICs #MethodC #OpenSourceMining #CryptoTechnology #BlockchainInnovation #MiningHardware #BTCMining #ProactiveInvestors #TechUpdate

את דב מורן פגשנו לראשונה בכנס.הפעם הכנס היה שונה, מאחר וזה היה כנס לכבודו.כחלק מהאירועים לציון 25 שנים להמצאת הדיסק-און-קי, הגיע דב לשיחה בסגנון Fire-side-chat עם פרופ׳ שחר קוטינסקי בטכניון.אהבנו את הצורה שבה הוא מעביר מסר ומספר סיפור וניצלנו את ההזדמנות להזמין אותו לפרק.אחרי תהליך קצר להפליא הגענו למשרדים של Grove Ventures להקליט את הפרק.דב הוא אחד היזמים הבולטים בעולם השבבים הישראלי ועל שמו רשום אחד האקזיטים הגדולים בישראל אי פעם.השיחה הייתה קולחת ומעניינת וזכינו לשמוע על חייו המוקדמים ותחילת הקריירה שלו. מגבלת הזמן דחקה בנו לעצור מה שאומר שיש לנו עוד שאלות לפרק נוסף.על מה דיברנו?- איך קוראים לדיסק-און-קי בחו״ל?- מה הוביל את דב ללימודי עתודה בטכניון?- מה התרומה של סבא שלו על הקריירה שלו?- איך הוא הגיע להיות לוחם בשריון?- מה הוא עשה בתותחנים?- מה התרומה שלו למצפין הראשון בצה״ל?- מה הוא היה עושה כשהיה מסיים את יום העבודה שלו בחיל הים? ולמה?- מה הייתה הדרך, והטעויות בדרך, בהקמת msystems?- מה היה רגע ה׳אאוריקה׳ שהוביל להמצאת הדיסק-און-קי?- איך השפיע הומלס על היום של דב בנקודת השפל בקריירה שלו, כשהיה בדרך למשקיע?אחרי שהאזנתם לפרק, ובהנחה שאתם לא בוטים, מוזמנים להצטרף לקבוצת המאזינים שלנו - שם כבר קרו דברים בעתיד >>> https://chat.whatsapp.com/KwUu8pQsxx220qS7AXv04THard Reset - הפודקאסט של קהילת Hardware Engineering Israel.מוזמנים ליצור איתנו קשר במייל podcasthardreset@gmail.comהאזנה נעימה.

In episode 102, we are joined by Ryan, lead developer of the Mujina firmware, for a debrief on Telehash #3 live demo and the momentum around the 256 Foundation's fully open Bitcoin mining stack. We walk through the sous vide miner demo that cooked ribeyes while mining on three Ember One hashboards with custom water blocks, controlled by our Libre board prototype running Mujina and pointed at Hydra Pool; an eight-hour, live-streamed showcase of the entire open stack working together. We reflect on why releasing everything on GitHub from day one matters, how modularity in Mujina accelerates chip and board innovation, and why open tooling lowers the barrier for builders from hobbyists to mega-miners. We dig into industry reactions from NEMS, interest from ASIC manufacturers, and the business case for open firmware at fleet scale. We discuss roadmap polish for Mujina (APIs, multipool support, power targets), Hydra Pool enhancements, HashScope share verification, and how open primitives enable better miner management, heating applications, and novel products. We shout out community contributors and hash renters who powered Telehash, preview Heat Punk Summit workshops (including Canaan's home-mining session), and make the call for companies to support 256 Foundation grants that are already delivering outsized ROI for the entire mining ecosystem.

Daniel is joined by Mark Kuemerle, Vice President of Technology, Custom Cloud Solutions at Marvell. Mark is responsible for defining leading-edge ASIC offerings and architects system-level solutions. Before joining Marvell, Mark was a Fellow in Integrated Systems Architecture at GLOBALFOUNDRIES and has held multiple engineering… Read More

This is a massive one! Nearly two hours, but you know what they say: Too much of a good thing can be wonderful!My friend MBI (

January 2026 Sustainable Stock and ETF Picks… Covers the world's most sustainable companies, cleantech and renewable energy stocks, and more. By Ron Robins, MBA Transcript & Links, Episode 163, January 23, 2026 Hello, Ron Robins here. Welcome to my podcast episode 163, published on January 23, 2025, titled "January 2026 Sustainable Stock and ETF Picks." This podcast is presented by Investing for the Soul. Investingforthesoul.com is your go-to site for vital global, ethical, and sustainable investing mentoring, news, commentary, information, and resources. Remember that you can find a full transcript and links to content, including stock symbols and bonus material, on this episode's podcast page at investingforthesoul.com/podcasts. Also, a reminder. I do not evaluate any of the stocks or funds mentioned in these podcasts, and I don't receive any compensation from anyone covered in these podcasts. Furthermore, I will reveal any investments I have in the investments mentioned herein. I have a huge crop of 24 articles for you in this podcast! Note: Some companies are covered more than once. Now with so many articles to potentially cover, I've chosen 6 to quote from. The other 18 can be found with their titles and links on the webpage for this podcast edition. ------------------------------------------------------------- The 2026 Global 100 list puts speed in the spotlight The first article I'm quoting from is hot off the press and is about one of my favourite company rankings! It's titled The 2026 Global 100 list puts speed in the spotlight on corporateknights.com. The introduction is by Tristan Bronca. Here's some of what he says. "As the global economic transition accelerates, more companies are recognizing that sustainability isn't just good marketing – it's good for business, too… This was the animating spirit of the new methodology behind the Corporate Knights Global 100 ranking. The revised methodology introduces 'sustainable revenue momentum' to measure how fast companies are growing their sustainable revenues. A change of method Last year, sustainable revenues and investments together accounted for 50% of the score, and the other 50% was scored across 22 common environmental, governance and social performance indicators (KPIs) such as water use, emissions, workplace fatalities, and diversity on the board and among executives. The change has reordered the deck in a big way… A dramatic departure? 'In terms of performance, the G100 companies are back in top form, beating the benchmark MSCI AWCI index over the past year,' Toby Heaps says, referring to a stock market index of 85% of global investable equities across almost 50 countries." End quotes. Incidentally, the top five companies are ERG SpA (ERG.MI), Pandora A/S (PNDORA.CO), EDP Renováveis SA (EDP.LS), Fluence Energy, Inc. (FLNC), and Taiwan High Speed Rail Corp. (2633.TW). ------------------------------------------------------------ Top 4 Clean Tech Companies to Watch in 2026 This next article brings us back to highly familiar territory. It's titled Top 4 Clean Tech Companies to Watch in 2026 on carboncredits.com and is by Jennifer L. Here are some brief quotes. "1. NextEra Energy (NEE) is the largest clean energy company in the world. It owns and operates wind farms, solar fields, and battery storage systems across the United States… NextEra has also increased its dividend for more than 26 years in a row. 2. First Solar (FSLR) is one of the top makers of solar panels worldwide. It uses a technology called thin‑film photovoltaic modules. These panels are lighter, use fewer raw materials, and often perform better in hot climates compared to traditional silicon panels. The company builds large solar power plants that send power to utilities and corporate customers… Financially, First Solar is a strong player. Its market cap was around $24 billion in 2025, and it has shown double‑digit revenue growth. 3. Bloom Energy (BE) makes a special type of power generator called a solid‑oxide fuel cell. These units produce electricity efficiently and with low emissions. Customers include data centers, large buildings, and industrial sites that need reliable power without high carbon output. Bloom's fuel cells can run on hydrogen or biogas, which makes them flexible for future clean energy systems… Premium financial news reported that its stock jumped more than 410 % in 2025 after strong earnings results. 4. Plug Power (PLUG) focuses on hydrogen fuel cell systems. Its products are designed to replace traditional batteries and fossil fuels in heavy equipment, forklifts, and industrial vehicles. The company is also building hydrogen production and fueling infrastructure across North America and Europe. This supports a broader 'green hydrogen' economy… Plug Power has faced financial challenges, including consistent net losses and stock price volatility… Its long‑term growth story depends on hydrogen demand and policy support worldwide." End quotes. ------------------------------------------------------------- 3 ESG Stocks to Add to Your Portfolio for Sustainable Returns in 2026 - December 30, 2025 The third article I've chosen to quote from is titled 3 ESG Stocks to Add to Your Portfolio for Sustainable Returns in 2026 - December 30, 2025 on zacks.com. It's By Aniruddha Ganguly. Now, some quotes from the article. "1. NVIDIA (NVDA) achieved 100% renewable electricity for all its global offices and controlled data centers in fiscal 2025. This Zacks Rank #1 (Strong Buy) company targets to reduce direct emissions by 50% for operations (Scope 1) and electricity consumption (Scope 2) by 2030… The Zacks Consensus Estimate for fiscal 2026 increased a couple of cents to $4.66 per share, indicating 55.9% growth from the figure reported in fiscal 2025. (NVDA - Free Report). 2. IDEXX Laboratories (IDXX) is a developer, manufacturer and distributor of products and services primarily for the companion animal veterinary, livestock and poultry, water testing and dairy markets. IDEXX has set goals to reduce Scope 1 and 2 greenhouse gas emissions and aims to source 100% renewable electricity by 2030… This Zacks Rank #2 (Buy) company plans to improve diversity and representation of underrepresented groups… IDEXX shares have surged 66% in the trailing 12-month period. The Zacks Consensus Estimate for 2026 earnings has been steady at $14.42 per share, indicating 11.7% growth from the 2025 consensus estimate figure of $12.93 per share. (IDXX - Free Report). 3. Microsoft (MSFT) targets to become carbon negative, water positive, and generate zero waste by 2030… This Zacks Rank #3 (Hold) company is leveraging AI for Good Lab and tools like the Microsoft Planetary Computer to drive biodiversity conservation… Microsoft shares have returned 14.7% in a year. The Zacks Consensus Estimate for fiscal 2026 increased a couple of cents to $15.61 per share, indicating 14.4% growth from the figure reported in fiscal 2025. (MSFT - Free Report)." End quotes ------------------------------------------------------------- Top Renewable Energy Stocks To Watch Today This next article picks a few lesser-known, and for some sustainable investors, a few controversial companies for review. It's titled Top Renewable Energy Stocks To Watch Today on marketbeat.com and is by MarketBeat. Here are several brief quotes from the article. "1. Quanta Services (PWR) provides infrastructure solutions for the electric and gas utility, renewable energy, communications, and pipeline and energy industries in the United States, Canada, Australia, and internationally. Read Our Latest Research Report on PWR. 2. WEC Energy Group (WEC) through its subsidiaries, provides regulated natural gas and electricity, and renewable and nonregulated renewable energy services in the United States. It operates through Wisconsin, Illinois, Other States, Electric Transmission, and Non-Utility Energy Infrastructure segments. Read Our Latest Research Report on WEC. 3. NOV (NOV) designs, constructs, manufactures, and sells systems, components, and products for oil and gas drilling and production, and industrial and renewable energy sectors in the United States and internationally. Read Our Latest Research Report on NOV. 4. Clearway Energy (CWEN) operates in the renewable energy business in the United States. The company operates through Conventional and Renewables segments. Read Our Latest Research Report on CWEN. 5. HA Sustainable Infrastructure Capital (HASI) through its subsidiaries, engages in the investment of energy efficiency, renewable energy, and sustainable infrastructure markets in the United States. Read Our Latest Research Report on HASI. 6. Ameresco (AMRC) a clean technology integrator, provides a portfolio of energy efficiency and renewable energy supply solutions in the United States, Canada, Europe, and internationally. Read Our Latest Research Report on AMRC. 7. Gibraltar Industries (ROCK) manufactures and provides products and services for the renewable energy, residential, agtech, and infrastructure markets in the United States and internationally. Read Our Latest Research Report on ROCK." End quotes. ------------------------------------------------------------- Top Wind Energy Stocks Poised to Benefit From Clean Energy Transition My fifth article is titled Top Wind Energy Stocks Poised to Benefit From Clean Energy Transition on finance.yahoo.com. It's by Avisekh Bhattacharjee and originally published on zacks.com. In the US, the wind industry could be gaining ground despite President Trump's protestations. Here are some quotes from the article. "1. NextEra Energy (NEE) is a public utility holding company engaged in the generation, transmission, distribution and sale of electric energy. The Zacks Rank #2 (Buy) company's competitive energy business, NextEra Energy Resources LLC (NEER), is the leading generator of wind energy globally. NextEra Energy, Inc. (NEE): Free Stock Analysis Report. 2. PG&E (PCG) operates as the parent holding company of California's largest regulated electric and gas utility, Pacific Gas and Electric Company.  The Zacks Rank #2 company's exposure in wind energy stems from the procurement of power from several renewable resources. Pacific Gas & Electric Co. (PCG): Free Stock Analysis Report. 3. Arcosa (ACA) is a leading manufacturer of infrastructure-related products and services that serve the energy, construction and transportation markets. This Zacks Rank #2 company's Engineered Structures business continues to benefit from strong demand for its wind towers and engineered structures. Arcosa, Inc. (ACA): Free Stock Analysis Report. 4. Constellation Energy (CEG) is a well-recognised provider of electric power, natural gas and energy management services to 2 million customers across the continental United States. Constellation Energy operates 27 wind projects across 10 states… This Zacks Rank #3 (Hold) company is launching a $350 million initiative to increase the output and lifespan of its portfolio of renewable energy sources. Constellation Energy Corporation (CEG): Free Stock Analysis Report." End quotes. ------------------------------------------------------------- AI infrastructure stocks Lumentum, Celestica, Seagate beat Nvidia 2025 My final review article covers some old favourites. Its title is AI infrastructure stocks Lumentum, Celestica, Seagate beat Nvidia 2025 on cnbc.com. It's by Kif Leswing. Here are some brief quotes. 1. Nvidia has been the biggest infrastructure winner in the artificial intelligence boom, soaring in value by almost thirteenfold since the end of 2022 to a market cap of $4.6 trillion. 2. Lumentum based in San Jose, California, makes switches, transceivers and other optical laser-based parts that are needed for fiber-optic cables. Customers have typically been telecommunications carriers and device makers like Apple, which previously used Lumentum parts in its FaceID sensor… Lumentum's stock price has jumped 372% this year… lifting the company's market cap past $28 billion. Sales surged 58% in the most recent quarter from a year earlier to $533 million.  3. Western Digital is one of three major hard drive manufacturers, along with Seagate and Toshiba. Shares of the 55-year-old company are up almost 300% this year… 'Data is the fuel that powers AI, and it is HDDs that provide the most reliable, scalable and cost-effective data storage solution,' CEO Irving Tan said in October on an earnings call… Revenue is expected to increase about 23% in fiscal 2026, with growth slowing to 13% in 2027. 4. Micron is one of three major memory producers, alongside Samsung and SK Hynix, but the only one based in the U.S… Analysts from Morgan Stanley said in a December note that Micron's results showed the best revenue and profit upside in the 'history of the U.S. semis industry' — aside from Nvidia. Revenue is expected to almost double in the year ending in August, before dramatically slowing to 24% in fiscal 2027 and less than 1% in 2028, according to LSEG. 5. Seagate is also benefiting from booming demand for storage. The stock is up 231% this year. Sales rose 21% to $2.63 billion in the company's fiscal third quarter, which ended Oct. 3. The company said at the time that 80% of its sales go to the data center market. 'There is no question that AI is reshaping hard drive demand by elevating the economic value of data and data storage,' CEO Dave Mosley said on a call with analysts… Analysts expect 21% revenue growth this fiscal year, followed by increases of about 15% and 6% in the next two years, according to LSEG. 6. Celestica founded in 1994 as an IBM subsidiary, makes switches that connect networks together and manage the data and traffic flowing through them. The stock is up more than 230% this year… Analysts at Goldman Sachs wrote in a note Friday that Celestica supplies parts for Google's ASIC. 'The company should benefit in 2026 from being the leading provider of Google TPU rack level solutions,' the analysts wrote." End quotes. ------------------------------------------------------------- More articles from around the world with Sustainable Investment Picks for January 2026. 1. Title: These Infrastructure Stocks Could Quietly Power the AI Revolution on fool.com. By Matt DiLallo. 2. Title: Top Beaten-Down Data Center Infrastructure Stocks on seekingalpha.com. By Steven Cress. 3. Title: Meet the four most sustainable funds on the market for 2025 corporateknights.com. By CK Staff. 4. Title: 3 Green Energy Stocks to Watch for a Cleaner, More Sustainable 2026 on finance.yahoo.com. By Pulkit Chamria. 5. Title: Analysts See Triple-Digit Revenue Growth in 2026 for These 3 AI Infrastructure Stocks on wallst.com. By Rich Duprey. 6. Title: The Top Clean-Energy Stocks for 2026, According to an Investment Advisor on businessinsider.com. By Samuel O'Brient. 7. Title: Top 10 Companies for CSR and Sustainability in 2025 on thecsrjournal.in. By Hency Thacker. 8. Title: This Underrated Industrial Stock Could Be the Purest Play on AI Infrastructure on fool.com. By John Bromels. 9. Title: Sustainable Investing Trends to Watch in 2026 on sustainalytics.com. By Morningstar Sustainalytics. 10. Title: The most sustainable equity funds in 2026 on corporateknights.com. Introduction by Saint Ekpali. 11. Title: Top 10: Renewable Energy Companies on energydigital.com. By Charlie King. 12. Title: The Grid Gap Gamble: Why Bloom Energy is Defying the Clean Tech Downturn in 2026 on markets.financialcontent.com. By MarketMinute. 13. Title: Some of the Best Sustainable Companies Call This ETF Home on etftrends.com. By Todd Shriber. 14. Title: Cisco Systems a Top Socially Responsible Dividend Stock With 2.2% Yield (CSCO) on nasdaa.com. By BNK Invest. 15. Title: Top 10: Sustainable Investments 2026 on sustainabilitymag.com. By Charlie King. 16. Title: Why Bloom Energy (BE) Stock Is Trading Up Today on finance.yahoo.com. By Petr Huřťák. 17. Title: Barclays Calls This 1 AI Server Stock 'Best in Class' Amid Upgrade to 'Overweight' Rating on finance.yahoo.com. By Aditya Raghunath. 18. Title: A clean technology company on the verge of transformational growth on stockhouse.com. By Trevor Abes. ------------------------------------------------------------- Ending Comment These are my top news stories with their stock and fund tips for this podcast, "January 2026 Sustainable Stock and ETF Picks." Please click the like and subscribe buttons wherever you download or listen to this podcast. That helps bring these podcasts to others like you. And please click the share buttons to share this podcast with your friends and family. Let's promote ethical and sustainable investing as a force for hope and prosperity in these tumultuous times! Contact me if you have any questions. Thank you for listening. My next podcast will be on February 27th. See you then. Bye for now.   © 2026 Ron Robins, Investing for the Soul

Welcome to the Complexity Premia podcast by Coolabah Capital, a hosted by Christopher Joye, Chief Investment Officer and Portfolio Manager at Coolabah Capital, and Ying Yi, a Senior Portfolio Management Director at Coolabah Capital. The Complexity Premia podcast strives to deconstruct modern investment problems for wholesale (not retail) participants in capital markets. You can listen on your favourite podcast app, or you can find it on Spotify, Podbean or Apple Podcasts. In this episode, Chris and Ying Yi assess what lies ahead for markets in 2026 as Trump's America First doctrine reshapes global trade, geopolitics, and monetary policy. They argue that tariffs, reshoring, AI-driven capex, and unchecked fiscal spending are reviving inflation risks just as markets price complacency. From Trump's clash with the Fed to Australia's fiscal excesses and the RBA's credibility test, this episode explores why the easing cycle may already be over—and why power, not policy orthodoxy, is once again driving the global order. This information is suitable for wholesale investors only and has been produced by Coolabah Capital Institutional Investments Pty Ltd ACN 605806059, which holds Australian Financial Services Licence No. 482238 (CCII). The views expressed in this recording represent the personal opinions of the speakers and do not represent the view of any other party. The information does not take into account the particular investment objectives or financial situation of any potential listener. It does not constitute, and should not be relied on as, financial or investment advice or recommendations (expressed or implied) and it should not be used as an invitation to take up any investments or investment services. Whilst we believe that the information discussed in the podcast is correct, no warranty or representation is given to this effect, and listeners should not rely on this information when making any decisions. No responsibility can be accepted by CCII to any end users for any action taken on the basis of this information. Any performance data presented on this site is pre-fees for institutional clients that negotiate custom fee rates, and these solutions are not available to retail investors. No investment decision or activity should be undertaken without first seeking qualified and professional advice. CCII may have a financial interest in any assets discussed during the podcast. Listeners in Australia are encouraged to visit ASIC's MoneySmart website to obtain information regarding financial advice and investments.  

כל אפשרות היא אופציה.אבל לא כל אופציה היא מניה!המרואיין שלנו היום הוא יובל אריאב והוא יודע טוב מאוד מה היא אופציה.יובל הוא משקיע צ'ק ראשון, היזם ומנהל קרן ההשקעות סימבול. הוא פרופסור באוניברסיטת קולומביה ומלמד את הקורס עם השם המדליק "Data Driven Dollars".החיבור אליו הגיע כששאלנו את אלעד רז: "את מי כדאי לראיין שידבר איתנו על אופציות?". הוא ענה בלי למצמץ פעמיים.על מה דיברנו?- מה זאת אופציה?- למה יש מניות?- למה חברות נותנות אופציות לעובדים?- למה מקבלים אופציות כאופציות ולא כמניות?- איך מעריכים שווי של אופציות?- מה זה אומר דילול ואיך זה משפיע על המהנדס בקצה?- מה עושים עם האופציות בסיום העבודה?- ואיך אפשר להרוויח מאופציות?בהמשך לפרק המוצלח על RSU עם שרון רבינוביץ׳ (בו סיפרנו לכם מה ראשי התיבות אומרים באמת, וגם הבטחנו לכם פרק על אופציות), הגיע הזמן להקדיש פרק למי שעובד בסטארטאפ. גם עובדים עתידיים בסטארטאפ מוזמנים להאזין. או כאלה שיש להם אופציות מחברה קודמת. או בכלל.אחרי שהאזנתם לפרק מוזמנים להצטרף לקבוצת המאזינים שלנו - שם אנחנו נותנים אופציות שונות בסקרים משונים >>> https://chat.whatsapp.com/KwUu8pQsxx220qS7AXv04Tמוזמנים ליצור איתנו קשר במייל podcasthardreset@gmail.comהאזנה נעימה.

Are you mining Bitcoin to secure the money of the future? Or are you just a "Fiat Hasher" using the network to stack more dying dollars? Kent Halliburton (@khalliburton) joins me to argue that most of the industry has the wrong incentives. We compare the early days of El Salvador surf tourism, when walking to the beach meant risking your life, to the current state of the network. Just as surfers ventured into dangerous territory for the perfect wave, true Bitcoiners are pushing boundaries to build an escape raft from the fiat system that will last for generations.We discuss the concept of Bitcoin miners acting as a pioneer species in the global energy market. Kent explains how sovereign mining operations venture into remote regions like Ethiopia and Paraguay to monetize stranded energy resources that no one else can reach. This process does far more than generate revenue for developing nations. It helps stabilize the local electrical grid and paves the way for vital infrastructure development in places the central banks and global planners have largely ignored.For many plebs, the biggest barrier to hashing has always been the logistics of the hardware. We break down how hosted mining models allow you to own a dedicated ASIC miner without forcing you to manage the intense heat and noise at home. This is about far more than convenience or ROI. It is about aligning incentives so that you can acquire non-KYC "Wild Sats" at the cost of production rather than paying the inflated spot price on a KYC exchange.We also touch on the human side of hyperbitcoinization in places like the Peruvian Amazon. Kent shares his experience living near the circular economy projects that are proving Bitcoin works as a medium of exchange today. We talk about the importance of using Bitcoin as a tool for sovereignty and how "Energy Cost Averaging" allows you to opt out of the fiat ponzi completely while supporting the communities that need sound money the most.Finally, we tackle the critical threat facing the network regarding security and censorship resistance. With so much hash rate concentrated in just a few massive mining pools, the danger of state capture is higher than many admit. Kent uses the "Milan Cathedral" analogy to challenge us to lower our time preference. We need to stop thinking about quarterly profits and start building for a future we might not live to see. If this conversation made you think, please subscribe and drop a comment below.-Bitcoin Beach TeamConnect and Learn more about Kent Halliburton:X: https://x.com/khalliburton Web: https://www.sazmining.com/kent-halliburton Web: https://iris.to/kent Support and follow Bitcoin Beach:X: https://www.twitter.com/BitcoinBeach IG: https://www.instagram.com/bitcoinbeach_sv TikTok: https://www.tiktok.com/@livefrombitcoinbeach Web: https://www.bitcoinbeach.com Browse through this quick guide to learn more about the episode:00:00 Intro 05:15 How do Bitcoin circular economies work in Peru? 09:30 How to stop trading crypto and become Bitcoin-only? 12:45 How to mine Non-KYC Bitcoin without hardware? 16:20 Is Bitcoin mining profitable vs buying spot? 20:10 How to use Section 179 for mining tax deductions? 22:45 Why are miners moving to Ethiopia and Paraguay? 27:30 How does Bitcoin monetize stranded energy? 31:50 Why do you need Low Time Preference for wealth? 35:15 Is mining centralization a security threat?Live From Bitcoin Beach

Empire deal $20 billion Nvidia claims Groq inference LPUs-leadership strengthening moat disruptively. Chiplet bandwidth enables hyperscale agentic AI scalably unprecedentedly worldwide. Strategic bundle firewalls against ASIC challengers comprehensively.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

יצא לנו רצף של פרקים לא שגרתיים.פרק על מניות, פרק מאולתר על Electromigration, פרק מאירוע ACRC בטכניון וגם פרק שהקלטנו בלייב בכנס.יצא לנו סה״כ רצף לא רע, אז החלטנו להמשיך עם עוד פרק לא שגרתי ישירות להקלטה במרכז הישראלי למחשוב קוונטי בישראל - הIQCC.המרואיין שלנו הפעם הוא לא אחר מאשר מי שמוביל את המרכז - ד״ר ניר אלפסי.הקריירה האקדמית של ניר נשמעת כמו רשימת פרקים עתידיים שלנו, והשיחה איתו זרמה כל כך שפונקציית הגל לא קרסה. מה שאומר שחייב להיות עוד פרק בעתיד.על מה דיברנו?- איך ניר הגיע להוביל את המרכז?- איך נראית רשימת קניות של מחשב קוונטי?- באילו טכנולוגיות בונים מחשבים קוונטיים? ובמה משתמשים ב#IQCC?- מי הם הלקוחות של המרכז?- איך סטודנטים משתמשים במשאבי המרכז ללמידה?- מהו המירוץ לעליונות קוונטית ואיך מנצחים בו?אחרי שהאזנתם לפרק מוזמנים להצטרף לקבוצת המאזינים שלנו - שם אנחנו מפרסמים מפרטים של מחשבים קוונטים >>> https://chat.whatsapp.com/KwUu8pQsxx220qS7AXv04Tמוזמנים ליצור איתנו קשר במייל podcasthardreset@gmail.comשמעתם שאנחנו מתחרים בגמר #פודקאסטהשנה של @גיקטיים? הצבעתם לנו? שיתפתם? אפשר לעשות את זה ממש כאן: https://www.geektime.co.il/podcast-of-the-year-2025-vote-is-now-open/האזנה נעימה.

澳洲證監會（ASIC）發出最後警告，指出隨著 2026 年 1 月 1 日的教育水平限期臨近，理財行業約百分之 15 的從業員（涉及超過 2,300 人）可能被迫離開行業。

The guys discuss the fallout from the Bondi tragedy and why Labor failed to act on Antisemitism, Taylor Swift’s Eras Richard, ASIC loses the plot with the First Guardian debacle, PuppyGate WFH, Shayne Elliott $13.5m ANZ lawsuit and is Lululemon done? Thanks for listening! Join us on LinkedIn: https://www.linkedin.com/company/the-contrarians-with-adam-and-adir-podcast Subscribe on YouTube for all our video content: https://https://www.youtube.com/@ContrariansPodcast Follow us on Instagram: https://www.instagram.com/contrarianspod Follow us on TikTok: https://www.tiktok.com/@contrarianspodSee omnystudio.com/listener for privacy information.

שלוש ספות מהמשרדים של NextSilicon על הבמה, לוגו על המסך, תאורה, ו… קהל.הגענו לכנס Acceleration Summit 2025 ומעבר לאנשים המעניינים שפגשנו וההרצאות המרתקות ששמענו, הקלטנו גם פרק בלייב.המרואיין שלנו הפעם הוא ניצן שקד. ניצן בעל עבר עשיר בפיתוח, ויודע דבר או שניים על #HPC ועל #AI. אז ניצלנו את ההזדמנות.על מה דיברנו?- מה ההבדל בין תוכנה למחשבי על וזו ל-AI?- מה כל כך מיוחד ב#GPU?- מה היה ה-Aha moment בהקשר AI?- איך יראה עולם התוכנה והחומרה אחרי עידן הטרנספורמרים?- איפה יהיה צוואר הבקבוק הבא של עולם המיחשוב?אחרי שהאזנתם לפרק מוזמנים להצטרף לקבוצת המאזינים שלנו - שם אנחנו מאיצים יחד איתכם טרנדים טכנולוגיים >>> https://chat.whatsapp.com/KwUu8pQsxx220qS7AXv04Tמוזמנים ליצור איתנו קשר במייל podcasthardreset@gmail.comשמעתם שאנחנו מתקרבים בצעדי ענק לגמר #פודקאסט_השנה של @גיקטיים? רוצים לראות אותנו שם? כנסו לקישור, וכתבו Hard Reset בקטגוריות ״טכנולוגיה״ ו-״הייטק ופיתוח״:https://www.geektime.co.il/podcast-of-the-year-2025-pre-vote-is-now-open/האזנה נעימה.

Welcome to the Complexity Premia podcast by Coolabah Capital, a hosted by Christopher Joye, Chief Investment Officer and Portfolio Manager at Coolabah Capital, and Ying Yi, a Portfolio Management Director at Coolabah Capital. The Complexity Premia podcast strives to deconstruct modern investment problems for wholesale (not retail) participants in capital markets. You can listen on your favourite podcast app, or you can find it on Spotify, Podbean or Apple Podcasts.   In this episode, CJ and Ying Yi debate the rising dominance of the state over markets, arguing that Australia's declining productivity, capital flight, and ballooning public debt reflect political choices rather than bad luck. They explore why mobile talent and capital are leaving for more competitive jurisdictions, how activist government is eroding incentives to work and innovate, and why history suggests only crisis—not consensus—forces meaningful reform. This information is suitable for wholesale investors only and has been produced by Coolabah Capital Institutional Investments Pty Ltd ACN 605806059, which holds Australian Financial Services Licence No. 482238 (CCII). The views expressed in this recording represent the personal opinions of the speakers and do not represent the view of any other party. The information does not take into account the particular investment objectives or financial situation of any potential listener. It does not constitute, and should not be relied on as, financial or investment advice or recommendations (expressed or implied) and it should not be used as an invitation to take up any investments or investment services. Whilst we believe that the information discussed in the podcast is correct, no warranty or representation is given to this effect, and listeners should not rely on this information when making any decisions. No responsibility can be accepted by CCII to any end users for any action taken on the basis of this information. Any performance data presented on this site is pre-fees for institutional clients that negotiate custom fee rates, and these solutions are not available to retail investors. No investment decision or activity should be undertaken without first seeking qualified and professional advice. CCII may have a financial interest in any assets discussed during the podcast. Listeners in Australia are encouraged to visit ASIC's MoneySmart website to obtain information regarding financial advice and investments.  

Subscribe to the Blockspace newsletter! Welcome back to The Mining Pod! Today, Nick Hanson, CEO of Luxor Technology, joins us to talk about the ramifications of the most recent Bitcoin mining crackdown in Xinjiang China, how it relates to the 2021 prior ban, and what could happen to all that hashrate if the crackdown holds. Subscribe to the newsletter! https://newsletter.blockspacemedia.com **Notes:** 100 EH/s off-line in Xinjiang 1.3GW to 1.63GW of lost capacity 400,000 to 500,000 machines off-line Hashrate fell 6% to 1062 EH/s China's market share: 14% (145 EH/s) Hash price $36 or $37 (depressed) Timestamps: 00:00:00:00 Start 00:03:02:23 China Ban Redux 00:07:03:13 Discrepancy in numbers 00:08:13:22 What does this mean for miners? 00:09:52:11 Tik Tok & Red Note 00:12:00:19 Global Hashrate heat map 00:13:54:09 Moving hashrate globally 00:16:49:24 ASIC market 00:18:42:10 Does this lead to consolidation in ASIC manufacturing?00:21:39:23 What about China Ban 1.0?

What if Bitcoin does not fail because of governments, banks, or the IMF, but because Bitcoiners themselves refuse to actually use it? In this episode, Mike sits down with Isabella Santos in El Salvador to confront one of the most uncomfortable questions in Bitcoin today. Is most “Bitcoin adoption” actually fake if merchants never see anyone spend sats?Isabella shares the unfiltered story of how BTC Isla in Isla Mujeres went from a Twitter idea to a true Bitcoin circular economy with a Bitcoin cafe, a Bitcoin gym, and over 30 local merchants now accepting sats. She explains why trying to orange pill people with education alone did not work, and why building real businesses that accept Bitcoin as sound money was the only way to earn real trust inside the community.In one of the most extreme Bitcoin experiments you will hear this year, Isabella Santos lived for 21 days using only Bitcoin, earning sats through physical jobs like cleaning toilets, selling churros, and making tacos. No fiat safety net. No savings bailout. Just proof that a circular economy either works in real life, or it does not exist at all.The conversation then turns toward a side of Bitcoin many people avoid. Isabella openly calls out Bitcoin maxis who refuse to spend sats, the rise of ETFs that distract from sound money, and the uncomfortable truth that Bitcoin mining hardware is already highly centralized in only a few manufacturers. Her warning is simple and unsettling. Bitcoin does not win automatically. It only works if people fight for it.They close with the human side of adoption through the Bitcoin Fit Games, where fitness, scavenger hunts, and sats came together to support the very merchants who took the first risk on Bitcoin. From helping feed a family in crisis to building a wellness hub with a gym, recovery center, and café, Isabella Santos shows what Bitcoin looks like when it leaves Twitter and enters real life.—Bitcoin Beach teamConnect and Learn more about Isabella Santos:X: https://x.com/BTCIsla X: https://x.com/isabellasg3 YT: https://www.youtube.com/@btcisla Support the project through Geyser Fund: https://geyser.fund/project/btcisla https://www.youtube.com/@getbasedtvSupport and follow Bitcoin Beach:X: https://www.twitter.com/BitcoinBeach IG: https://www.instagram.com/bitcoinbeach_sv TikTok: https://www.tiktok.com/@livefrombitcoinbeach Web: https://www.bitcoinbeach.com Browse through this quick guide to learn more about the episode:0:00 Intro1:25 Is El Salvador really welcoming Bitcoiners at the airport with a “Welcome to Bitcoin Country” sign?4:49 What is BTC Isla and how does it work? How do you set up a Bitcoin cafe payment system that actually gets used?8:23 How does Bitcoin relate to health and low time preference at the first Bitcoin gym in Mexico running its own node?11:14 Can you live using only Bitcoin for 21 days? How do you get paid in Bitcoin for local jobs on an island?13:01 Should Bitcoin prices be listed in sats or fiat? How do you separate a Bitcoin savings stack from a spending stack so you can spend without guilt?18:36 How do you organize a Bitcoin themed fitness event? Can Bitcoin be used to support local families in need?27:19 How do you build a Bitcoin circular economy? How many merchants in Isla Mujeres accept Bitcoin, and how do you convince local merchants to accept Bitcoin?31:31 How do you grow a Bitcoin YouTube channel fast? What is The Exit Manual Bitcoin show ( @exitmanual )? How do you make Bitcoin content that reaches teenagers?35:25 Is Bitcoin mining too centralized? Who makes the ASIC mining chips? Is Bitcoin centralized?Live From Bitcoin Beach

In this episode, eco & Tyler welcome back Skot who was at the African Bitcoin Conference, this year hosted in Mauritius, where he spoke on open-source Bitcoin mining. We swap travel tales (including Scott's chaotic Paris layover) and impressions of Mauritius, the conference venue, and side events focused on Bitcoin education. We dig into mining headlines: Bitdeer's missed ASIC roadmap and investor lawsuit, Bitmain's history (Antbleed) and why open-source mining matters, and MicroBT's M70-series lineup pushing industrial-scale, three-phase miners. Skot explains the theory behind Bitdeer's hyped “adiabatic charge recovery logic,” why it's hard to scale, and how thermal and power density realities define miner design. We go deep on open hardware and firmware progress: Braiins' open control board, Secure Boot obstacles, and Mujina's modular path to safe, customizable, dev-fee-free mining; plus Skot's BitCrain control board concept for USB‑controlled fleets. We share shop-floor lessons building AddIt boards and Ember One prototypes (solder paste, tombstoning, reflow profiles) and celebrate practical innovation like Gridless's open-source JuaKali direct-DC solar mining kit. On home-mining UX, Tyler demos new Home Assistant integrations for Canaan Avalons and WhatsMiner, and we preview Hydra Pool deployments (Grafana/Prometheus dashboards) for the upcoming Telehash. Finally, we update the community on the Samourai Wallet case: Keonne's facility designation, the continuing push for a presidential pardon, and how to support via petition and donations. #PardonSamourai.

This week in bitcoin mining news, ERCOT sees a 266 GW of interconnection requests in 2026, IREN closed a $2.3 billion convertible note offering, and GPUs are leaving ASICs in the dust. Subscribe to the Blockspace newsletter for market-making news as it hits the wire! Welcome back to The Mining Pod! Today, Ethan Vera, COO of Luxor, joins us as we dive into MicroBT's Whatsminer M70 launching into a challenging ASIC market, IREN's $2.3 billion convertible note offering, the precarious state of hashprice, Luxor's new GPU hardware sales business, the staggering 270% leap in ERCOT interconnection requests, and the controversial Cat bitcoin fork proposal aimed at filtering ordinals / inscriptions. Subscribe to the newsletter! https://newsletter.blockspacemedia.com **Notes:** - Hash price is below $40 per second - Three negative difficulty adjustments - Ercot requests leaped 270% in 2025 - 73% of requests from data centers - IREN raised $2.3B in convertible notes - M70 efficiency: 12.5 J/TH 00:00 Start 02:35 Difficulty Report by Luxor 07:26 IREN note 10:44 M70 launch 20:02 Luxor launches GPU trading 27:12 ERCOT LL requests up 270% in 2025 34:10 Cry Corner: another filter fork proposal

Subscribe to the Blockspace newsletter for market-making news as it hits the wire! Welcome back to The Mining Pod! Today, Ethan Vera, COO of Luxor, joins us as we dive into MicroBT's Whatsminer M70 launching into a challenging ASIC market, IREN's $2.3 billion convertible note offering, the precarious state of hashprice, Luxor's new GPU hardware sales business, the staggering 270% leap in ERCOT interconnection requests, and the controversial Cat bitcoin fork proposal aimed at filtering ordinals / inscriptions. Subscribe to the newsletter! https://newsletter.blockspacemedia.com **Notes:** - Hash price is below $40 per second - Three negative difficulty adjustments - Ercot requests leaped 270% in 2025 - 73% of requests from data centers - IREN raised $2.3B in convertible notes - M70 efficiency: 12.5 J/TH 00:00 Start 02:35 Difficulty Report by Luxor 07:26 IREN note 10:44 M70 launch 20:02 Luxor launches GPU trading 27:12 ERCOT LL requests up 270% in 2025 34:10 Cry Corner: another filter fork proposal

Australia’s world-first under 16 social media ban has officially kicked in and that means TikTok, Meta and Snap are plotting their next move ASIC is on a fining-spree after hitting companies like Grill’d, Bob Jane and MJ Bale with fines for failing to lodge their financial accounts ChatGPT has officially become the most downloaded free iPhone app for 2025 - meaning it has overtaken TikTok, Instagram and even Google _ Download the free app (App Store): http://bit.ly/FluxAppStore Download the free app (Google Play): http://bit.ly/FluxappGooglePlay Daily newsletter: https://bit.ly/fluxnewsletter Flux on Instagram: http://bit.ly/fluxinsta Flux on TikTok: https://www.tiktok.com/@flux.finance —- The content in this podcast reflects the views and opinions of the hosts, and is intended for personal and not commercial use. We do not represent or endorse the accuracy or reliability of any opinion, statement or other information provided or distributed in these episodes.__See omnystudio.com/listener for privacy information.

In this episode of The Alternative Investing Advantage,host Alex Perny sits down with Josh Moore, founder and CEO of the NFN8 Group, to break down how everyday investors can participate in the explosive world of Bitcoin mining—without needing to build rigs in their garage. Josh shares his journey from the early days of self-directed IRAs toscaling large-format mining operations, and explains why mining is one of the most compelling cash-flow plays in the digital asset space today.They also explore ASIC technology, data centers, electricityeconomics, sale-leaseback investor structures, and why Bitcoin mining has matured from a hobbyist experiment into an institutional-grade asset class. Whether you're crypto-curious or already own digital assets, this episode brings clarity to one of the most misunderstood (and lucrative) sectors of the crypto ecosystem.00:00 Introduction to Cryptocurrency and Mining02:23 Josh Moore's Journey into Cryptocurrency09:28 The Catalyst for Mining Investments12:52 Understanding Mining as a Financial Model18:46 The Evolution of Mining Hardware21:02 Demystifying the Mining Process23:16 Understanding Bitcoin Mining Basics24:53 The Economics of Bitcoin Mining28:08 Scaling Bitcoin Mining Operations29:50 Building and Managing Data Centers33:58 Investor Perspectives on Bitcoin Mining45:57 Final Thoughts and Future OutlookSubscribe to our YouTube channel and join our growing community for new videos every week.If you are interested in being a podcast guest speaker or have questions, contact us at ⁠⁠⁠⁠⁠⁠⁠⁠Podcast@AdvantaIRA.com⁠⁠⁠⁠⁠⁠⁠⁠.Learn more about our guest, Josh Moore: https://www.linkedin.com/in/josh-e-moore/Learn more about Advanta IRA: https://www.AdvantaIRA.com/ https://podcasters.spotify.com/pod/show/advanta-irahttps://www.linkedin.com/company/Advanta-IRA/https://twitter.com/AdvantaIRA https://www.facebook.com/AdvantaIRA/ https://www.instagram.com/AdvantaIRA/The Alternative Investing Advantage is brought to you by Advanta IRA.Advanta IRA does not offer investment, tax, or legal advice nor do we endorse any products, investments, or companies that offer such advice and/or investments. This includes any investments promoted or discussed during the podcast as neither Advanta IRA nor its employees, have reviewed or vetted any investments, persons, or companies that may discuss their services during this podcast.  All parties are strongly encouraged to perform their own due diligence and consult with the appropriate professional(s) before entering into any type of investment.#BitcoinMining #CryptoInvesting #DigitalAssets#PassiveIncome #ASICMining #Blockchain #AlternativeInvesting #JoshMoore#AdvantaIRA #MiningROI

【98有聲書房】開張，訂閱收藏News98精選有聲書：https://apple.co/44KcuRo 主持人：阮慕驊 來賓：資深證券分析師 連乾文 (阿文師) 主題：ASIC壓著輝達打？TPU商機聯發科吃得到？ 節目時間：週一至週五 5:00pm-7:00pm 本集播出日期：2025.12.04 此集影片YouTube連結 https://youtube.com/live/v74mQXC09Vo

Google Gemini 的強勢逆襲，驗證雲端巨頭自研晶片的成效，也讓市場聚焦 ASIC 的趨勢 本集《上流投資術》深入剖析，當 AI 伺服器走向客製化，哪些台廠早已卡位關鍵節點？ 《各節重點》 ＊ 雲端巨頭的逆襲 ＊ ASIC 供應鏈大解密 ＊ 封測與檢測商機 加入會員，支持節目： https://wealth1974.firstory.io/join 留言告訴我你對這一集的想法： https://open.firstory.me/user/ckijrbz8nehm50847mulgl7v6/comments ★ 打電話也可以訂財訊→(02)2551-5228 轉 10。 ★ 商業合作請洽 ad@wealth.com.tw，或撥專線 (02)2551-2561 轉 255。 製作｜財訊雙週刊 主持｜尚清林 來賓｜劉志明 企劃｜吳雨軒 攝影｜吳雨軒 剪輯｜吳雨軒 錄影時間｜2025.12.2

Interview recorded - 2nd of December, 2025On this episode of the WTFinance podcast I had the pleasure of welcoming back Warwick Powell. Warwick is an Adjunct Professor at Queensland University of Professor working at the intersection of China, digital technologies, supply chains, financial flows and global political economy & governance.During our conversation we spoke about Warwick's overview of 2025, accelerating shift away from US hegemony, BRICS institution, currency and more. I hope you enjoy!0:00 - Introduction0:57 - Overview of 20256:50 - Accelerating US hegemonic shift?12:25 - Drivers of Western challenges18:28 - Real capital investment into US23:44 - AI impact on employment28:18 - Shifting alliances33:25 - BRICS institutions39:03 - European type alliance42:01 - BRICS currency48:33 - One message to takeaway?Warwick began his career in academia, teaching Chinese history and European cultural history at Griffith University. He graduated with First Class Honours and is the recipient of the prestigious University Medal for Academic Excellence. Warwick was also awarded a Department of Foreign Affairs and Trade scholarship to undertake postgraduate studies at People's University, Beijing. He deferred his studies to begin work for Kevin Rudd in the Queensland Government.He is the chairman and founder of Sister City Partners Limited, a not-for-profit investment bank focusing on developing links between regional Australia and the markets of Asia. Through this work, Warwick has experience in diverse industries including cattle and sheep production and processing, information and communication technology, infrastructure, energy, natural resources, travel and tourism and property development.He is a director of a number of funds management companies responsible for funds established under an ASIC-approved Australian Financial Services License. He is a member of the Central Highlands Accelerate Agribusiness Advisory Board and was the founding Treasurer of Innovation NQ Inc., a not-for-profit innovation incubator in North Queensland.He continues to teach professional courses in areas such as innovation, creativity, regional economic development and blockchain technology with James Cook University, QUT and Edith Cowan University.Warwick Powell: LinkedIn - https://au.linkedin.com/in/warwickpowellSubstack - https://substack.com/@warwickpowell Twitter - https://x.com/baoshaoshanWTFinance -Instagram - https://www.instagram.com/wtfinancee/Spotify - https://open.spotify.com/show/67rpmjG92PNBW0doLyPvfniTunes - https://podcasts.apple.com/us/podcast/wtfinance/id1554934665?uo=4Twitter - https://twitter.com/AnthonyFatseas

Happy Mindful Monday Everyone!In this week's episode, our host, Allie Brooke, sits down with  amazing Brigette Panetta. Brigette has emerged as a powerful advocate for individuals facing social injustice and adversity. As the partner of James Mawhinney, founder of Media.com, Brigette has personally experienced the profound challenges of fighting against a government body. Over the past four years, she has witnessed the severe impact of Australia's corporate regulator, ASIC's, false allegations on her family's investment business, Mayfair 101. This ordeal has deeply affected Brigette, her young family, and the lives of nearly 600 Australians. Her journey began under extraordinary circumstances—two days after giving birth during the onset of the pandemic, Brigette found herself in the ICU, recovering from a traumatic birthing experience while simultaneously supporting James through 26 legal hearings. The struggle resulted in losing their family home and enduring significant personal and professional setbacks. Despite these hardships, Brigette's resilience has driven her to speak out and assist others in similar predicaments. Brigette is committed to creating a foundation to provide litigation funding and support for individuals battling injustices. She aims to offer a platform for people to share their stories and find solidarity in their experiences. In addition to her advocacy work, Brigette is passionate about holistic healing modalities. She has explored and benefited from practices such as breath work, meditation, kinesiology, and Reiki to overcome trauma and stress. Through her journey, she has discovered the power of internal healing and now wishes to educate others on these methods.Episode TopicsHow did you specifically cultivate mental resilience during such an intense social justice pursuit, when the stakes were so high?Can you share an experience where a mental barrier (like self-doubt, fear, or hopelessness) felt particularly overwhelming, and what immediate, in-the-moment strategies you used to overcome it?How did your experiences with adversity fundamentally shift your perspective on personal strength and the human capacity for endurance?You've leveraged meditation, Reiki, and other holistic healing modalities to transcend adversity. For listeners who might be new to these practices, where would you recommend they start to begin building their inner strength?Can you describe how integrating mind, body, and spirit through these practices creates a different kind of resilience compared to purely mental fortitude?Was there a specific holistic practice that felt most impactful for you during your most challenging times, and what made it so powerful?How do you continue to weave these holistic practices into your daily life now, beyond times of crisis, to maintain your well-being?How To Connect w| BrigetteWebsiteLinkedIn  The Growth METHOD. Membership◦ ⁠⁠⁠⁠⁠⁠⁠⁠Join Here!⁠⁠⁠⁠⁠⁠⁠⁠ (Both FREE and Premium)◦ Use Code:growthmindsetgal for 50% off your first month's subscription! THE GREAT 2025 LOCK-IN GIFTED 1HR COACHING CALL SIGN UPENDS 12/31/2025 1:1⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ GROWTH MINDSET COACHING PROGRAMS!◦ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Application Form ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ What are the coaching sessions like?⁠⁠• Tailored weekly discussion questions and activities to spark introspection and self-discovery.• Guided reflections to help you delve deeper into your thoughts and feelings.• Thoughtfully facilitated sessions to provide maximum support, accountability, and growth.• Please apply for a FREE discovery call with me!• Allie's Socials• Instagram:@thegrowthmindsetgal• TikTok: @growthmindsetgal• Email: thegrowthmindsetgal@gmail.comLinks from the episode• Growth Mindset Gang ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠ Broadcast Channel⁠⁠⁠⁠⁠⁠⁠⁠• Growth Mindset Gang ⁠⁠⁠⁠⁠⁠⁠⁠Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• Growth Mindset Gal ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• Better Help Link: Save 10%SubstackDonate to GLOWIGloci 10% off Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Upstream Data CEO and Founder Steve Barbour joins the pod to talk about the state of bitcoin mining and why he's not sold on the hybrid AI-bitcoin miner trade. Subscribe to the Blockspace newsletter for market-making news as it hits the wire! Welcome back to The Mining Pod! Today, Steve Barbour, CEO of Upstream Data, joins us for a bitcoin mining palate cleanser! We cover the current state of mining economics, his take on some of the new ASIC models coming to market, oil and gas markets, the infrastructural / operational differences between AI/HPC data centers and mining farms, and why he believes Bitcoin mining is entering a new phase of maturity with sustainable business models. Subscribe to the newsletter! https://newsletter.blockspacemedia.com **Notes:** • Mining difficulty at all-time highs • Energy partnerships crucial for profitability • Home mining still viable for enthusiasts • Public miners face market pressure • Equipment costs down significantly • Operational efficiency key to survival Timestamps: 00:00 Start 04:11 Miner sentiment check 07:37 How much more downside? 11:28 ASIC market 14:18 Bitdeer & AISC sales 19:43 Proto ASIC design 23:18 Oil field mining 27:51 Renewables 30:00 Miner retrofit 35:58 NatGas Generators 39:53 Oil prices 41:26 Bits vs atoms 43:44 Lawsuit

Subscribe to the Blockspace newsletter for market-making news as it hits the wire! Welcome back to The Mining Pod! Today, Steve Barbour, CEO of Upstream Data, joins us for a bitcoin mining palate cleanser! We cover the current state of mining economics, his take on some of the new ASIC models coming to market, oil and gas markets, the infrastructural / operational differences between AI/HPC data centers and mining farms, and why he believes Bitcoin mining is entering a new phase of maturity with sustainable business models. Subscribe to the newsletter! https://newsletter.blockspacemedia.com **Notes:** • Mining difficulty at all-time highs • Energy partnerships crucial for profitability • Home mining still viable for enthusiasts • Public miners face market pressure • Equipment costs down significantly • Operational efficiency key to survival Timestamps: 00:00 Start 04:11 Miner sentiment check 07:37 How much more downside? 11:28 ASIC market 14:18 Bitdeer & AISC sales 19:43 Proto ASIC design 23:18 Oil field mining 27:51 Renewables 30:00 Miner retrofit 35:58 NatGas Generators 39:53 Oil prices 41:26 Bits vs atoms 43:44 Lawsuit

Khảo sát của ASIC cho thấy người Úc dự tính sẽ chi gần 800 đô mỗi người cho quà tặng, du lịch và tiệc tùng trong dịp cuối năm. Nhưng theo các chuyên gia, lập kế hoạch từ sớm và chi tiêu có chiến lược có thể giúp bạn tận hưởng trọn vẹn mùa lễ mà không vượt ngân sách.

美國四大CSP（Microsoft、Google、AWS、Meta）近期法說會不約而同調高資本支出預期，Google更是一路調高至900多億美元。OpenAI執行長奧特曼（Sam Altman）甚至計劃在2030年前打造近30GW的算力，預計投入超過一兆美元。面對如此龐大的投資，市場不免出現泡沫質疑。然而在今年九月Open AI、Oracle與Nvidia之間的「AI永動機」啟動後，AI晶片供過於求的問題卻似乎有了一些反轉⋯⋯ 這集節目，我們特別邀請長期關注半導體與AI產業的艦長程正樺，一起深入聊聊AI產業的現況與展望。 主持人：天下雜誌總主筆 陳良榕 來賓： 騰旭股份有限公司投資長 程正樺 ＊Podcast限定優惠方案！訂閱一年《胡說科技》還附天下暢銷書《晶片戰爭》：https://bit.ly/47fR4PX ＊意見信箱：bill@cw.com.tw -- Hosting provided by SoundOn

Core Scientific shareholders voted no on CoreWeave's $9 billion acquisition proposal, and CleanSpark acquired a Texas site for a 285 MW AI site. Subscribe to the Blockspace newsletter for market-making news as it hits the wire! Welcome back to The Mining Pod! For this week's roundup, we break down Core Scientific shareholders voting NO on the $9B CoreWeave acquisition, CleanSpark's plans for a new 285 megawatt Texas site for AI workloads, and TeraWulf's record 25-year contract with FluidStack. Plus, Ethan Vera from Luxor joins to analyze the ASIC market and where hash rate growth is really coming from. And for this week's cry corner, why the filter soft fork is doomed to fail. Notes:  • Core Scientific shareholders rejected CoreWeave deal • Hashprice dropped to $43.73 per petahash daily • Difficulty adjusted upward 6.3% • Hashrate reached 1.1 zettahash on 7-day average • CleanSpark acquired Texas site with 300 MW pipeline • TeraWulf signed 25-year deal with FluidStack Timestamps: 00:00 Start 02:09 Difficulty Report by Luxor 07:47 ASIC market update 12:01 CORZ deal fails 22:14 CleanSpark data center acquisition 27:51 WULF $9.5B FS extension 33:36 Cry Corner: Fork time?

Take a Network Break! Companies spying on…I mean, monitoring…their employees via software called WorkExaminer should be aware of a login bypass that needs to be locked down. On the news front, we opine on whether it’s worth trying to design your way around AWS outages, and speculate on the prospects of a new Ethernet switch... Read more »

Take a Network Break! Companies spying on…I mean, monitoring…their employees via software called WorkExaminer should be aware of a login bypass that needs to be locked down. On the news front, we opine on whether it’s worth trying to design your way around AWS outages, and speculate on the prospects of a new Ethernet switch... Read more »

ICE raided an ASIC repair shop this week, and a look into the crystal ball for Bitcoin's hashrate at the end of the year.Click Here To Join the BitAxe Giveaway! Welcome back to The Mining Pod! Today, Ben Harper from Luxor Technologies joins us to talk about the brutal hash rate environment as hashrate surges past 1 zettahash. For news, we break down ICE's raid on a Bitcoin miner repair shop in Pyote, Texas, Core Scientific's shareholder vote for the CoreWeave acquisition, and Tether's massive 86,000+ BTC treasury.  **Notes:** • Difficulty increased 6%, up 26% over 7 adjustments • Hashrate expected to reach 1.2 zeta by year-end • Core Scientific vote scheduled for October 30th • Tether holds 85,335 BTC worth $10.4 billion • ICE arrested 12-13 undocumented workers at TX ASIC repair shop Timestamps: 00:00 Start 04:27 Difficulty Update by Luxor 06:48 ICE raids ASIC repair shop 11:30 Hashrate Forwards 23:03 End of year Forward projections 25:41 Cleanspark Ad 26:10 Core Sci Update 28:44 Tether holds more BTC than you think