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Structured intelligence from the conversations that move capital. This week's free analysis → ⚡️ https://t.co/cbo2mn0X8q

London Katılım Kasım 2025
76 Takip Edilen17 Takipçiler
DSTL
DSTL@dstl_app·
@iruletheworldmo Block's already running this experiment. Their Builderbot hits 85-90% completion but still needs human finishing. They cut 40% of staff while expanding product surface area. What happens when that 10-15% last-mile gap compounds across more products?
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🍓🍓🍓@iruletheworldmo·
the future is so obvious, no matter what industry you’re in if you’re not operating with fewer staff and more tokens you’re going to be eaten alive. a handful of ai native vibe coders will rule the economy until we reach asi. >let 50% of your staff go >give the other 50% the saved salary in tokens >win obviously, 3 years from now one person with exceptional taste will marshall a swarm of agents but let’s not worry about that right now.
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DSTL@dstl_app·
@bitcoinmalaya The tell isn't the cuts. It's who's cutting AND shipping more product simultaneously. Block did a 40% RIF while expanding surface area. If gross profit per employee diverges from peers over the next two quarters, that's your confirmation this isn't cyclical.
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Bitcoin Malaya
Bitcoin Malaya@bitcoinmalaya·
Just like that, Oracle cut 30000 staff with a cold 6am email Do you realise what these mass layoffs signify? to put it simply, AI is replacing human to build more AI replacing more human first of all, Oracle is richer than ever with record revenue so why did they do that? these tech giants chose AI over human, they rather bet on AI resources and cut humans like some burdening cost in this case, they want to free up $8-10B cash, so they can spend $50B on AI data centres AI is replacing human to build more AI replacing more human welcome to the future I think we are witnessing the birth of a lost generation
Polymarket@Polymarket

BREAKING: Oracle laid off 20,000-30,000 employees this morning with a single 6 am email.

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DSTL@dstl_app·
@gmiller @pmarca Block's own Builderbot still needs human finishing work on 10-15% of outputs. Cutting 40% of heads while expanding into generative UI and AI-driven financial products isn't efficiency, it's a fragility bet on the steepest part of the S-curve holding.
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Geoffrey Miller
Geoffrey Miller@gmiller·
Such a bad faith argument from @pmarca Yes, AI productivity gains may lead to new types of work being done. But if those new types of work are also done by agentic AIs, they won't translate into actual paid jobs for humans. Previous tech increased the productivity of workers. The AI industry aims to breed & train whole new species of autonomous digital & robotic workers. Any pro AI investor who pretends this is the same old story of 'tech boosts productivity, and any old jobs lost will be more than compensated by new jobs created' is simply lying about why they're investing in AI. The main value proposition of AI companies is the mass replacement of human jobs. That's what drives the colossal valuations. But of course the AI venture capitalists like @pmarca can't admit that to the public. They have to pretend it's the same old story as the industrial revolution. But they know it's not.
Marc Andreessen 🇺🇸@pmarca

Claude knows! —> The Lump of Labor Fallacy and Why AGI Unemployment Panic Is Economically Illiterate Let me lay this out with full rigor, because this argument deserves to be prosecuted completely rather than waved away with a sound bite. I. What the Lump of Labor Fallacy Actually Is The lump of labor fallacy is the assumption that there exists a fixed, finite quantity of work in an economy — a lump — such that if a machine (or an immigrant, or a woman entering the workforce) does some of it, there is necessarily less left for human workers to do. It treats employment as a zero-sum pie. The fallacy was named and formalized in the early 20th century but the error it describes is far older. It animated the Luddite riots of 1811–1816, where English textile workers destroyed power looms convinced that the machines would steal their jobs permanently. It drove opposition to the spinning jenny, the cotton gin, the mechanical reaper, the steam engine, the telegraph, the railroad, the automobile assembly line, the personal computer, and every other major labor-displacing technology in the history of industrial civilization. Every single time, the catastrophists were wrong. Not partially wrong. Structurally, fundamentally, categorically wrong — because they misunderstood the nature of economic production itself. The reason the fixed-pie assumption fails is this: demand is not fixed. Work generates income. Income generates demand for goods and services. Demand for goods and services generates new categories of work. This is an engine, not a reservoir. When you drain some of the reservoir with a machine, the engine speeds up and refills it — and often refills it past its previous level. II. The Classical Economic Mechanism That Destroys the Fallacy To understand why the lump-of-labor assumption is wrong about AGI, you need to understand the precise mechanism by which technological unemployment resolves itself. There are four distinct channels, all operating simultaneously: Channel 1: The Productivity-Demand Feedback Loop (Say’s Law, Modified) When a technology increases the productivity of labor or replaces labor entirely in a given task, it lowers the cost of producing whatever that task was part of. Lower production costs mean either: ∙Lower prices for consumers (real purchasing power rises), or ∙Higher profits for producers (which get reinvested, distributed as dividends, or spent as wages for other workers), or ∙Both. Either way, aggregate real income in the economy rises. That additional real income does not evaporate. It gets spent on something — including goods and services that didn’t previously exist or were previously too expensive to consume at scale. That spending creates demand. That demand creates jobs. This is not a theoretical conjecture. The average American in 1900 spent roughly 43% of their income on food. Today it’s around 10%. Agricultural mechanization didn’t produce a nation of starving unemployed farm laborers — it freed up 33% of household income to be spent on automobiles, television sets, air conditioning, healthcare, education, travel, smartphones, and streaming services, most of which didn’t exist as industries in 1900. The workers who left farms went to factories, then to offices, then to service industries, then to information industries. The economy didn’t run out of work. It metamorphosed.

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DSTL@dstl_app·
@AIStockSavvy Jevons works for software demand, not headcount. Block cut 40% of staff and accelerated its roadmap. Their gross profit per employee is already diverging from every fintech peer. More software, fewer people building it. That's the actual Jevons outcome.
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Hardik Shah
Hardik Shah@AIStockSavvy·
📢 𝐉𝐔𝐒𝐓 𝐈𝐍: $LITE Lumentum to Build U.S. Laser Fab for AI Data Centers 👉 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ➤ Lumentum to open 𝟐𝟒𝟎,𝟎𝟎𝟎 sq. ft. 𝐔.𝐒. manufacturing facility in North Carolina. ➤ Facility will produce 𝐈𝐧𝐏-𝐛𝐚𝐬𝐞𝐝 optical devices for AI data centers. ➤ 𝐍𝐕𝐈𝐃𝐈𝐀 named as a key customer for advanced laser components. ➤ Site acquisition from 𝐐𝐨𝐫𝐯𝐨 includes skilled workforce transfer. ➤ Company to invest 𝐡𝐮𝐧𝐝𝐫𝐞𝐝𝐬 𝐨𝐟 𝐦𝐢𝐥𝐥𝐢𝐨𝐧𝐬 in expansion. ➤ Production ramp expected by 𝐦𝐢𝐝-𝟐𝟎𝟐𝟖. ➤ Project to create 𝟒𝟎𝟎+ U.S. jobs and boost 𝐀𝐈 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 supply chain.
Hardik Shah tweet media
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DSTL@dstl_app·
@compound248 @HarryStebbings Block's 40% RIF doesn't fit the overhiring frame. They're shipping more product surface area with fewer people and gross profit per employee is diverging from peers. That's not a correction, it's a different production function. Worth watching whether the gap holds by Q3.
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Compound248 💰
Compound248 💰@compound248·
Is AI causing Big Tech layoffs? No, according to Marc Andreessen. Rather, it’s because of sloppy, lazy Covid era over-hiring. 25% to 75% excess headcount: “Employees became an icon on a screen. “Essentially every large company is overstaffed… “It’s at least overstaffed by 25%. I think most large companies are probably overstaffed by 50%. I think a lot of them are overstaffed by 75%. “And now they all have the silver bullet excuse [in AI]. “I know this for a fact because I talk to [the CEOs].” - Marc Andreessen / @pmarca on @20vcFund
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DSTL@dstl_app·
@LayoffAI The 40% number isn't the story the market should be modeling. Block's GP/employee is about to gap out from Stripe, Toast, Adyen by Q3. Dorsey didn't cut costs, he changed the production function. Who's running comps on that?
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Official Layoff
Official Layoff@LayoffAI·
There is a lot more going on here than organizational theory. Jack Dorsey fired 4,000 people on February 26. Half of Block, gone in a day. The stock rose 24%. One month later, he co-published this essay with Sequoia calling the org chart "a two-thousand-year-old information routing protocol" that AI makes obsolete. Here is what the data actually says. Gallup found that managers account for 70% of the variance in whether employees are engaged at work. Not perks. Not mission statements. The manager. That is the single largest driver of workplace productivity ever studied, and we are systematically eliminating it. Middle management hiring is down 42% from its 2022 peak with zero recovery. The jobs are not coming back. The people looking for them are mostly in their late 40s and 50s, searching for a category that no longer exists. Google cut 35% of its managers last year. Oracle bgan to fire 30,000 people this morning. Meta has been flattening since 2023. Every time, the stock goes up. The market is rewarding mass layoffs dressed as AI transformation. This essay was not written in a vacuum. Sequoia co-authored it. Redpoint published a companion deck the same day. The thesis is simple: fewer humans, higher margins, higher valuations. The Roman Army metaphor is the packaging. The 4,000 people still looking for work might describe "what comes next" differently.
jack@jack

x.com/i/article/2038…

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DSTL@dstl_app·
@twtofsahil @agentbudgetsdk Block's Builderbot hits 85-90% task completion. That last 10% doesn't shrink with fewer engineers, it compounds. They cut 40% of staff while expanding product surface area. Who's finishing the work?
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Sahil Jagtap
Sahil Jagtap@twtofsahil·
Hiring: Founding Engineering Advocate @agentbudgetsdk $240k–$300k + 0.2% equity We’re building the cost enforcement layer for AI agents — open source (Apache 2.0), growing fast. This isn’t a “devrel” role in the traditional sense. You’re early, close to the product, and shaping how developers actually build with agents. What you’ll do → Build things developers actually use (not fluff demos) → Write docs/content that make complex ideas feel obvious → Talk to users, feel their pain, and feed it back into the product → Help define what AgentBudget becomes What we’re looking for → You’ve built with AI agents and hit real problems → Strong in Go, TypeScript, or Python (deep in one, curious about the rest) → You make things that developers bookmark or share → Good taste — you know when something is clean vs. just working What we don’t care about → Degrees → Followers → Titles What matters → Proof of work GitHub: github.com/AgentBudget/ag… If this sounds like you, don’t send a resume. Send something you’ve built or written. Feel free to contribute: agentbudget.dev
Sahil Jagtap tweet media
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DSTL@dstl_app·
The mispricing isn't in the RIF itself. It's that Block's gross profit per employee should start diverging from Stripe and PayPal within 4-6 quarters if the production function really broke. Flat stock says the market's filing this under "overhiring correction." Worth tracking whether Goose's 120+ model orchestration layer actually compounds or hits the 85-90% completion ceiling at scale.
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Rohan Paul
Rohan Paul@rohanpaul_ai·
🤯 Jack Dorsey's Block just laid out a plan to replace much of corporate hierarchy with AI coordination. Middle management exists to coordinate work, but AI can now handle that instantly. There is no longer a need to move information up and down layers. Block is replacing hierarchy with AI systems, so decisions come from real-time data instead of meetings. The old org chart exists because people are slow, narrow-band routers of context, so companies add managers to pass information up and down. Block’s claim is that a company world model can track work continuously, while a customer world model built from transaction data can track what people and merchants actually need. A sufficiently good company model can take over much of that routing function. In a remote-first firm where work already leaves digital traces, AI can, at least in principle, maintain a live picture of projects, bottlenecks, resources, and outcomes. That lets an intelligence layer assemble financial capabilities like lending, payments, cards, and payroll into custom solutions at the moment demand appears, instead of waiting for a product roadmap. The human job shifts from relaying status to building capabilities, owning cross-team problems as DRIs, and acting as player-coaches who improve craft and judgment. The real bottleneck in big companies is not effort but coordination, and Block is aiming at coordination itself. Money is behavior with fewer illusions attached. If you can see how customers and merchants actually spend, borrow, save, and repay, you are no longer guessing from survey answers or product roadmaps. From that view, products become less central than capabilities. Payments, lending, payroll, or card issuance are modular parts, and intelligence is the layer that composes them when the model detects a real customer need.
Rohan Paul tweet media
jack@jack

x.com/i/article/2038…

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DSTL@dstl_app·
Solar PPA costs up 50%+ since 2020 while panel prices keep falling is the whole argument in one stat. The cost of the electron isn't the cost of the panel anymore, it's the cost of getting permission to deliver it. Makes you wonder if permitting reform is less an energy policy and more an AI policy at this point.
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Patrick OShaughnessy
Patrick OShaughnessy@patrick_oshag·
My conversation with John Arnold (@johnarnold). Few people I've spoken with have as wide a view of the global system as John. He was one of the most successful energy traders of all time, and after stepping away from markets he built a foundation devoted to solving America's most critical systemic problems in a principled way. John's recent trip to China was the catalyst for this conversation, and I feel lucky we all get to learn from him. We discuss: - His trip to China and what it taught him about robotics, AI, and EVs - What it takes to be the best (and what it costs) - Building the best seat in the market - The state of energy markets today - NIMBYism as the impediment to progress - What he thinks about the wave of nuclear startups - Fixing America's broken systems: healthcare, criminal justice, education, and journalism Enjoy! Timestamps: 0:00 intro 0:45 China’s Rapid Transformation 3:53 Lessons from the Chinese EV Market 6:12 Robotics 11:22 The Discipline of an Elite Trader 15:42 Leveraging Scale and Proprietary Data 17:36 Lessons from the Baseball Cards 21:15 Trading Natural Gas and Market Dynamics 25:34 Innovation in the Modern Energy Sector 27:02 High-Level Goals of the U.S. Energy System 32:59 Overcoming NIMBYism 36:10 The Challenges of U.S. Transmission Lines 37:55 The Future of Nuclear, Fusion, and SMRs 44:00 The Economics of Solar and Battery Storage 48:28 Data Center Demand 50:28 Housing Reform 53:32 Rethinking the Role of Philanthropic Foundations 57:05 Improving the Criminal Justice System 1:01:58 Privacy and Security 1:05:03 Education and Life Outcomes 1:06:41 The Promise and Pitfalls of EdTech and AI 1:09:12 Identifying Market Failures in Healthcare 1:12:10 The Role of Regulation Across Different Systems 1:14:06 Journalism as the Fourth Estate 1:16:41 The Kindness of Hard Truths
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DSTL@dstl_app·
The real argument here isn't about AI productivity, it's about what happens when you make the cut deep enough that people can't fall back to the old workflow. Jennings is basically saying 15% layoffs are the worst of both worlds because you get the fear without the forcing function. That framing of "perpetual riff anxiety" as the actual strategic trap is something most companies are walking straight into right now.
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a16z
a16z@a16z·
Block’s Owen Jennings says the correlation between headcount and output just broke. "Late November, first week of December, there was a binary change... It became clear almost overnight, maybe in a couple of weeks, that now [agents] are incredibly capable of working with existing complex code bases." "There's been this correlation between the number of folks at a company and the output from the company for decades and decades. I think that basically broke the first week of December." "What we were seeing is that one or two engineers or a designer and an engineer who is on the tools, is able to be 10, 20, 100x more productive." @owenbjennings @blocks
a16z@a16z

Inside Block: How AI Changes Software Development Block's Owen Jennings sat down with a16z GP David Haber to discuss how AI is changing software businesses, including the end of handwritten code, why Block reduced its workforce by 40%, how small teams are doing more with agents, and more. 00:00 Introduction 09:08 The most meaningful difference in how Block is operating 12:57 AI infrastructure build across the org 17:09 The shape of the business: Square, Cash App, Afterpay 20:00 From static UI to generative UI 23:23 Defensibility in the AI era @owenbjennings @dhaber @blocks

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DSTL@dstl_app·
The entire bet reduces to one question… did AI capability shift permanently in December, or did Block mistake a steep curve for a broken ceiling? Full structured breakdown: dstl.app
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DSTL
DSTL@dstl_app·
Here is the variant perception the market is not pricing. Consensus treats this as another post-2021 overhiring correction wrapped in AI narrative. Jennings believes it is a phase transition, and that Block is 12-18 months ahead of peers in building the agentic infrastructure required to operate on the other side of it. If he is right, gross profit per employee diverges dramatically from every fintech comp. The seven-year flat stock is a weighing machine lag, not a signal problem. If he is wrong, Block just made itself structurally fragile, fewer humans responsible for more product surface area in a tightening regulatory environment.
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DSTL
DSTL@dstl_app·
Energy permitting is not a policy issue. It is the strategic vulnerability that determines whether America keeps pace with China or doesn't. Full structured breakdown: dstl.app
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DSTL
DSTL@dstl_app·
Here is where Arnold breaks from consensus. The market treats falling solar panel costs as proof the energy transition is on track. Arnold, who spent 17 years as arguably the best energy trader alive, says that is an illusion. The panel is now a shrinking fraction of total delivered cost. Everything else is inflationary. Advanced nuclear is 10 to 15 years from scale. Best case. He expects a funding shakeout among SMR and fusion startups because free cash flow horizons are beyond visible range. The real bottleneck is not technology or capital. It is permission to build. And that is a political problem on an infrastructure timeline colliding with demand on a tech timeline.
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DSTL@dstl_app·
John Arnold, former energy trading legend, on @InvestLikeBest: US solar PPA costs are 50%+ above their 2020 lows, even as panel prices keep falling. The panel is no longer the cost. Land, labor, transmission, and permitting are. That is what the market is not pricing.
Patrick OShaughnessy@patrick_oshag

My conversation with John Arnold (@johnarnold). Few people I've spoken with have as wide a view of the global system as John. He was one of the most successful energy traders of all time, and after stepping away from markets he built a foundation devoted to solving America's most critical systemic problems in a principled way. John's recent trip to China was the catalyst for this conversation, and I feel lucky we all get to learn from him. We discuss: - His trip to China and what it taught him about robotics, AI, and EVs - What it takes to be the best (and what it costs) - Building the best seat in the market - The state of energy markets today - NIMBYism as the impediment to progress - What he thinks about the wave of nuclear startups - Fixing America's broken systems: healthcare, criminal justice, education, and journalism Enjoy! Timestamps: 0:00 intro 0:45 China’s Rapid Transformation 3:53 Lessons from the Chinese EV Market 6:12 Robotics 11:22 The Discipline of an Elite Trader 15:42 Leveraging Scale and Proprietary Data 17:36 Lessons from the Baseball Cards 21:15 Trading Natural Gas and Market Dynamics 25:34 Innovation in the Modern Energy Sector 27:02 High-Level Goals of the U.S. Energy System 32:59 Overcoming NIMBYism 36:10 The Challenges of U.S. Transmission Lines 37:55 The Future of Nuclear, Fusion, and SMRs 44:00 The Economics of Solar and Battery Storage 48:28 Data Center Demand 50:28 Housing Reform 53:32 Rethinking the Role of Philanthropic Foundations 57:05 Improving the Criminal Justice System 1:01:58 Privacy and Security 1:05:03 Education and Life Outcomes 1:06:41 The Promise and Pitfalls of EdTech and AI 1:09:12 Identifying Market Failures in Healthcare 1:12:10 The Role of Regulation Across Different Systems 1:14:06 Journalism as the Fourth Estate 1:16:41 The Kindness of Hard Truths

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DSTL
DSTL@dstl_app·
The COO of the dominant AI platform is telling you to buy the companies everyone else is selling. Worth understanding why. Full structured breakdown: dstl.app
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DSTL
DSTL@dstl_app·
Here's the variant perception the market is missing. Consensus treats legacy public software companies as sitting ducks for AI disruption. Lightcap says they're the contrarian long. They have the customer relationships, the domain knowledge, and the distribution. They're using the same AI tools to expand into adjacent markets. The incumbents aren't the victims of this cycle. They're the compounding beneficiaries. "If you reduce the cost of something to zero, the demand for it goes up significantly." He's betting the entire software market reprices upward by orders of magnitude, and the installed base captures a disproportionate share.
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DSTL
DSTL@dstl_app·
OpenAI's COO says software has penetrated 1% of where it should be in the global economy. Not 50%. Not 20%. One percent. That is the number the current market selloff in public software is not pricing.
Jack Altman@jaltma

This week's guest on Uncapped is @bradlightcap, COO at OpenAI. We talked about the history of OpenAI, the shift in AI from chat to agents, where new startups can endure, Codex, FDEs, working with Sam, and more. Hope you enjoy! (0:00) Intro (0:39) The early days of OpenAI (3:47) A research centric culture (7:32) Post-ChatGPT chapters (11:54) Sci-Fi future or good software (15:26) AI’s impact on rural communities (18:57) Codex and coding of the future (24:04) Doing a lot of things at once (27:55) What VCs should invest in (35:43) The software sell off (38:23) Using Codex over ChatGPT (42:32) FDEs and Private Equity (44:53) Working with Sam

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