Fireside Alpha
@firesidealpha • 2,057 subscribers
Summary and synthesis of the best business, technology, and consumer conversations
Videos
$INTC Lip-Bu Tan: Bottleneck investor "The investment side, I ways look at where is the bottleneck? What are you trying to solve? For example, I invest in companies like Credo $CRDO or Astera Labs $ALAB, is this interconnect becoming the bottleneck? Because speed becomes more important in interconnect in the cluster, so I think optical becomes very important. Look at Jensen, he invests in almost every company that's photonic."
Fireside Alpha362,313 次观看 • 11 天前
The July 4th weekend All-In The All-In Podcast turned into a long argument about who owns the intelligence layer. The besties think enterprises just woke up to a trap they had been walking into, here's how the conversation went (save this): ◽️ The Palantir-Nvidia deal is a bet against the model-layer duopoly. Palantir will use Nvidia's Nemotron open models to build a custom frontier-quality model for US government agencies, and the agencies own the hardware, the data, and the weights. Sacks framed it as structural: an application company and a chip company both want a competitive model layer, so they are natural partners against a two-provider middle. ◽️ Alex Karp's CNBC "crashout" was actually the thesis. Karp argued enterprises have lost trust in the frontier labs and want to own their compute, models, data, and alpha. Sacks translated it as a new definition of enterprise AI safety: safety means the model provider cannot hoover up your proprietary knowledge and turn it into its next product. ◽️ Figma is the cautionary tale that made it real. Anthropic launched Claude Design into Figma's category, its chief product officer sat on Figma's board and resigned only 3 days before launch, and Figma's stock is down about 50% this year while Anthropic's valuation surged. Sacks listed Claude Science, Security, Legal, Financial, and Code as the same move: dominate the model layer, then take the lucrative verticals. ◽️ The playbook has a name, and it is Microsoft and Google. Sacks argued Anthropic is running the operating-system strategy: own the layer everyone builds on, then walk up the stack. His Google receipt is that fewer than half of searches now send you off-site, versus an early Google that prided itself on how fast it kicked you away. ◽️ The BCG number is what raises the stakes. Chamath cited a BCG return-on-capital-employed study: the cost of capital is back to its long-run 8 to 11%, and half of large US companies cannot earn returns above it. If you are already teetering on your cost of capital, handing your alpha to a provider that may compete with you is not a luxury risk, it is fatal. ◽️ The 16.4x number is the whole argument in one data point. Chamath ran a code-migration task through 8090's harness. Wrapping Claude was 1.4x cheaper and 1.5x faster than Claude Opus alone. Wrapping the best open-source model was 16.4x cheaper, at about 3x slower. For a background task, three extra hours to cut cost by 16x is not a close call. ◽️ Even at 100x cheaper, enterprises were saying no for the wrong reason. Chamath relayed an ex-Meta PM's point that companies reject open models over China and safety fears, when they could host those same open weights on their own GPUs in US data centers with nothing flowing back. The safety objection, she argued, is backwards: the leak is the data you hand the frontier labs. ◽️ Friedberg says the frontier labs are trying to commoditize their own customers. Anthropic has been signing up life-sciences companies to feed a new life-focused model in exchange for early access, and nearly everyone he has talked to now refuses, recognizing that data they spent billions generating becomes worthless once it is pooled with everyone else's. ◽️ The deployment topology is shifting from big hubs to distributed spokes. Friedberg's map: the old assumption was a few capital-advantaged mega-clusters plus inference clouds. The new one is large hubs, medium hubs (enterprise training clusters), and distributed spokes, including on-prem inference in your own building. Owning your weights is the point. ◽️ Chamath's endgame is running GLM himself. An industry contact told him that with harness post-training and telemetry, an open Chinese model like GLM could get as good as Anthropic's Mythos. His conclusion: take GLM, control it soup-to-nuts on US hardware with only US citizens touching it, and pay a fraction. ◽️ The Apple analogy sharpens why renting intelligence is different from renting distribution. Chamath argued Apple is the only platform that respected developers, deliberately keeping its stock apps basic to protect the ecosystem and collect its 30% tax. There is no 30% tax on open models, and worse, you cannot rent intelligence from the same place that rents it to your competitor without ending up identical to them. ◽️ Nvidia's open model is now good enough to matter. Calacanis claimed you cannot tell Jensen Huang's Nemotron from Claude on 95% of searches, and that Nvidia downplayed the model until now to avoid alarming its top customers. The gloves came off once OpenAI, Anthropic, and Elon all signaled their own silicon ambitions. ◽️ Sacks sized the duopoly: roughly $60B and $40B in ARR. Anthropic is around ~$60 billion of ARR, OpenAI at ~$40 billion, and no one else generates meaningful model-layer revenue. Sacks's policy line: the US does not ban monopolies, only anti-competitive tactics, but the government should do nothing to make the duopoly more likely. ◽️ The token deflation call: 90% a year for three years. Calacanis predicted token costs fall 90% annually for three years, putting the price of intelligence near free and making it rational to waste tokens on hardware you already own. Friedberg's version is a 70/20/10 split between big cloud, local, and other clouds. ◽️ A wave of platform lock-in spending is already landing. Calacanis flagged Microsoft standing up a roughly $2.5 billion forward-deployed-engineer effort and Amazon spending about $1 billion on the same, plus OpenAI's version. His read: enterprises will slam the door, because letting a provider's engineers study your business is how it ends up in their model. ◽️ The server-per-employee prediction. Calacanis expects every employee to get $10,000 to $20,000 of local compute, a Mac Studio or a high-RAM Dell, running a personal local model that syncs to a thin laptop. A server per person, so nothing leaks. ◽️ On jobs, the data does not show present-tense loss. Sacks cited a RAMP and Revelio Labs study of over 21,000 US firms: the heaviest AI spenders grew headcount about 10% over two years, and entry-level headcount grew even faster at 12%. Friedberg's harder claim: there is no AI job loss yet, only clunky, gradual value creation, and the media will not reverse its narrative because that destroys its credibility. ◽️ The displacement case is real but forward-dated. The counterpoint on the show was that customer support, entry-level data entry and BPO, and driving are the near-term displacements, with Waymo cited as present-tense evidence: in markets where it hits critical mass, Uber and Lyft stop recruiting drivers. Sacks noted most US entry-level support was already offshored, so the acute risk sits in those countries first. ◽️ The human-premium counternarrative. Friedberg argued that as automation spreads, human interaction gets a premium: the skilled bartender, the real driver, the human-in-the-loop tier. He cited the company (referenced as Klarna) that hyped replacing its whole support team with AI, then reversed a year later on brand grounds. ◽️ The export-control episode needed three conditions, and Sacks says do not over-read it. Commerce lifted controls on Anthropic's Fable 5 after two weeks, with Mythos 5 restored to US customers around June 26 once co-founder Tom Brown replaced Dario as lead negotiator. Sacks's three conditions: Dario boasting for months about a cyber weapon, Amazon reporting failed guardrails in testing, and Dario refusing to roll Fable back. His message to allies: this was a particular set of circumstances rather than the debut of a standing lever. ◽️ The import question nobody answered cleanly. Calacanis pressed on why the US blocks Chinese cars and drones but not Chinese open models like DeepSeek and Kimi. Sacks's answer: a forked open model run on US hardware stops being Chinese, and banning open source would isolate the US and impose a token tax on American enterprises, so let the market decide if American open models win. ◽️ The California fiscal story is a business-climate story. Friedberg walked through the numbers behind Newsom's "balanced" $351B budget: expenses exceed revenue and $20-40B is borrowed to close the gap, the budget grew 65% in six years ($215B to $355B), personal income tax is $142B of ~$211B revenue with the top 1% (150,000 people) paying $70B of it, and the corporate rate of 8.9% sits far above Texas at zero. ◽️ The tax base is leaving, and the state is now taxing everyone else. Friedberg cited 1 to 1.5% of adjusted gross income leaving each year (about 15% over a decade), at least 15 Fortune 500 HQs and ~2,100 firms gone since 2019, and a new 8% software sales tax hitting Word, Gmail, and ChatGPT subscriptions plus a health-insurance tax, on top of a now-permanent 14.4% top bracket. The liabilities behind it run $1.4T in debt, up to $1.5T in unfunded pensions senior to state bonds, and ~$40B/year in out-year deficits. Lastly, the line that framed the whole show: "You can't rent intelligence from the same place that rents it to your competitor." That is the sovereignty thesis in one sentence, and every number in this episode is an argument for it. ____ Follow Fireside Alpha for more summaries on key business and technology conversations.
Fireside Alpha51,999 次观看 • 6 天前
CoreWeave and Crusoe were supposed to lose this market to the hyperscalers, and Dylan Patel Dylan Patel explains why they won it instead (Save this). The twist is that the hyperscaler edges became liabilities. Amazon's advantages, Nitro isolation, Graviton CPUs, custom storage, were all built for the CPU cloud and actively hurt AI performance. And the security and time-slicing expertise stopped mattering, because nobody rents a single GPU in a 72-GPU rack, they rent whole racks on long-term contracts. So the market opened three ways: ◽️First, the incumbent playbook of slicing and isolating fell away, and some of it turned into a net negative. ◽️Second, Microsoft's teams fell on their face when they had to double a year's forecast, and had to go rent Neocloud capacity themselves. ◽️Third, hyperlevered Neocloud teams got rich by delivering compute faster, undercutting on the $12 to 13 billion per gigawatt that GPUs rented for before the crunch while the SpaceX to Google deal ran near $25 billion, all off racks nobody splits into single GPUs. The Amazon cloud-crisis report and the GPU rental market say the same thing, an incumbency built for one workload is a liability for the next.
Fireside Alpha48,279 次观看 • 7 天前
Jeff Bezos: Orbital Compute > Terrestrial Compute "in the sun synchronous orbit, you're in daylight almost all the time, 99 plus percent. And so your solar cells are working for you the whole time. There are a lot of questions about orbital data centers and can it really work and people get worried about heat rejection and things like this. All of these physics problems are easily solvable. Heat rejection is not a real problem. It's all these questions are really about cost. So cost of production of the solar cells, the cost of production of the satellites and the cost of launch will eventually the lines will cross and eventually orbital compute will be a better option than terrestrial compute."
Fireside Alpha58,451 次观看 • 11 天前
The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.
Fireside Alpha38,370 次观看 • 10 天前
Dario Amodei keeps warning that open-source AI models are dangerous and need to be restricted. David Sacks David Sacks answered with a rhetorical question: "Dangerous to whom?" It was the sharpest thing said on the latest All-In The All-In Podcast. Not dangerous to the enterprise that wants to keep control of its own data. Open weights are what finally give that enterprise a choice. It is dangerous to Anthropic's business model. That model only works if customers have no real alternative at the model layer. So the safety argument starts to look like a moat argument wearing a lab coat. Line it up with the rest of the strategy. Dominate the layer everyone builds on. Launch Claude into science, security, legal, finance, and design, the verticals your own customers used to own. And campaign to restrict the open models that would hand those customers the exit. Here is the giveaway. When the person selling you safety also needs you to have no other option, you are not who is being kept safe.
Fireside Alpha10,037 次观看 • 5 天前
没有更多内容可加载