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Legacy media is now pushing another narrative: “Virtually all of Elon Musk’s wealth came from government help.” This is completely false. Government contracts are not subsidies. SpaceX wins contracts because it offers the best product at the lowest price. NASA pays SpaceX for real launches, real cargo missions, and real astronaut transport, not charity. SpaceX is expected to get less than 5% of its revenue from NASA this year. Add up every government incentive Tesla and SpaceX have ever received, and it is still less than 2% of the value of Tesla and SpaceX. Tesla did not become the world’s most valuable car company because of tax credits. It became huge because it built better EVs, better software, better batteries, and the best charging network. And when the $7,500 EV tax credit was removed, Tesla sales increased. The real reason they’re obsessed with Elon Musk is that he exposed how irrelevant they have become. People no longer wait for newspaper editors, TV anchors, or corporate journalists to tell them the truth. Legacy media lost its monopoly on information. Now all they have left is clickbait, outrage, and anti-Elon narratives. And that’s exactly why the trust in media is at an all time low.

$AMD| The FOMO to buy @AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: @WSJ yesterday came out with an article that @OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from @AnthropicAI has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited @AnthropicAI is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why @OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

Dario Amodei, anthropic's CEO, just answered the question everyone is asking. should you still learn to code: 1. coding is going away first. the AI models are doing it already. the broader task of software engineering takes longer but that's going too. if you're learning to code purely for job security, you're learning the wrong thing. 2. even at 5% of the task you're still valuable. if AI does 95% and you do 5%, you become 20 times more productive. comparative advantage is surprisingly powerful even when the gap is massive. 3. the professions with the most runway are human-centered ones. things that mix people, the physical world, and analytical skills together. he uses the radiologist example. the doctor who understands patients and context, not just reads scans. 4. critical thinking might be the most important skill of the next decade. when AI can generate anything, the ability to tell what's real from what's fake becomes rare and valuable. you don't want false beliefs. you don't want to get scammed. that's his actual advice to a 25 year old. 5. AI can make you stupider if you use it carelessly. anthropic ran studies on this. depending on how you use the model, de-skilling in coding is measurable and real. the tool doesn't cause it. carelessness does. 6. the semiconductor space is his pick for a capitalistic win in the next decade. physical world, traditional engineering, direct AI tailwind. not software but chips.

Why Most CIOs Are Quietly Praying for Retirement — And the Few Who Aren’t Are About to Get Very Rich I had a moment this week where I was sitting across from a Director of IT and it hit me — this poor bastard has the toughest job in the entire company. The business folks get to be full-time dreamers: “Hey, can we automate this? Can the AI just know what to do? Can it walk my dog while I’m in this meeting?” Meanwhile he’s over there thinking about data security, system reliability, whether some employee is gonna click on an email that says “You’ve won a $1,000 Walmart gift card!”, whether Ukrainian hackers are going to steal their customer data at 2 a.m., and whether his entire team is about to get replaced by three interns and ChatGPT — all while knowing none of this stuff actually works the way the brochures promised. And here’s the part that makes me feel for the guy — for his entire career he’s been rewarded for keeping the machines running and not getting fired. Now we’re asking him to suddenly become a profit center, to be out over his skis with AI initiatives. It’s like telling the hall monitor he’s now responsible for running the company’s underground poker game. Did I just compare our AI software to an underground poker game? Yeah, probably not the best analogy, but hang with me here, I’m rolling. Meanwhile the C-suite is over there wondering why nothing’s happened yet, completely oblivious to the fact that they’ve spent twenty years brutally punishing IT for not playing defense. Hell, I know CIOs who got fired because Windows 95 sucked. The real kicker is how to even get started. Our philosophy has always been to start small — automate one workflow, prove it works, and then compound fast. Smart in theory. In practice, with a big organization, that feels like bringing a birthday candle to a forest fire. The C-suite doesn’t get excited about incremental. They want to see something that actually moves the needle. So you’re stuck trying to thread this ridiculous gap: build something small enough to actually work, get real user adoption, and make sure the vendor isn’t full of shit. Honestly, I don’t envy that seat one bit. At Collide, we’re committed to being real partners with the folks actually doing the building. I’ve got serious scar tissue from getting fired for not being “openly collaborative” with other oil and gas companies on well spacing back in the shale days, and I’m never making that mistake again. We’re gonna share what we learn, educate when we can, and actually listen — God knows we have a lot to learn too. Truth is, my tech guys are dying to find some partners in crime — and I really gotta stop with the crime analogies, I swear that’s not what we’re doing here — because they get all excited explaining the latest and greatest AI breakthrough and I respond with the technical sophistication of a man asking if his rotary phone has Bluetooth. Sip slowly, my friends.

/teach is live Learn anything, from rubik's cube to vocal harmonies to software fundamentals. npx skills add mattpocock/skills --skill teach Best skill I've ever built, video coming soon

Katherine Boyle on Elon Musk: “I think that Elon’s most important contribution to this country is training two generations of engineers over twenty years on how to work with their hands again. We had lost that capability in the US. The pendulum had moved so far to software that we didn’t have someone thinking from first principles of how to build as quickly as possible and build in new ways and capabilities that didn’t exist before. You don’t learn it at university, and you don’t learn it at existing primes, where they’re given a list of requirements and told, ‘Build it exactly as we say.’ Elon’s way of thinking is how do you engineer something for production and build for manufacturing. You don’t separate those capabilities. You want to make it as simply and cheaply and as quickly as possible so that you can mass-produce something. We’ve watched the diaspora out of SpaceX, with SpaceX talent taking that knowledge and building new capabilities. They’re taking everything they’ve learned from that methodical approach and bringing it to new capabilities that SpaceX isn’t working on. That is what you’re seeing with the diasporas out of companies like SpaceX and Anduril, they all know something they’ve learned from the previous generation of companies.”

New MIT study. Code volume surges by 300%, but output increases by only 30%: The AI dividend meets an awkward reality Autonomous AI coding agents raised commits by 180%, but releases rose only 30%. The paper’s main idea is that software production has weak links, so faster code writing does not help as much when humans still need to review, connect, test, package, and ship the work. The authors also check app marketplaces and find more new apps, but no increase in total usage, which means more software appeared without clear evidence that users adopted more software. The marketplace evidence points the same way: more new apps appeared, but total usage did not rise. The authors compare more than 100,000 GitHub developers before and after they start using 3 generations of AI coding tools, from autocomplete to more independent coding agents. Autocomplete raised commits by 40%, interactive coding agents raised them by 140%, and autonomous coding agents raised them by 180%. The 180% commit gain shrank to 50% for the number of projects and 30% for actual releases. The estimated "elasticity of substitution" is 0.25 i.e. for every big improvement in AI’s usefulness, only a small amount of human work can be replaced. Because AI can write code faster, but humans are still needed to decide what to build, check if the code works, connect it with the rest of the product, fix messy edge cases, and actually ship it. --- papers .ssrn.com/sol3/papers.cfm?abstract_id=6859839

Elon Musk identified which jobs go first, and it destroys every assumption about who’s safe. Musk: “AI is going to take over those jobs like lightning. Anything that is digital, which is like just someone at a computer doing something.” Not factory workers. Office workers. The people who spent decades assuming education and desk jobs meant security are actually first. Musk: “Anything that’s physically moving atoms… those jobs will exist for a much longer time.” Output is a file? Vulnerable. Output is physical? Protected. That’s the entire framework. Musk: “AI is really still digital.” AI doesn’t need a body. Doesn’t need an office. Just needs access to the same software you use. Executes faster. Never tires. Costs nothing to scale. But it can’t weld. Can’t wire a building. Can’t fix pipes or work soil. Musk: “Literally welding, electrical work, plumbing. Those jobs will exist for a much longer time.” Trades aren’t the vulnerable jobs. They’re the durable ones. Physical presence, real-world adaptation, manual dexterity provide protection no digital credential offers. Analyst, accountant, paralegal, programmer, anyone producing files and documents, automates first because digital work is exactly what AI does natively. Person moving atoms has natural defense. Physics, unpredictable environments, material resistance create friction AI can’t scale past. Person moving bits has nothing. No friction. No physical barrier. Just software AI already operates better than most humans. The assumption that desk work and degrees represent safety just inverted completely. College graduate producing documents faces faster displacement than the electrician producing installations. Society spent generations telling people trades were beneath them. Pushed everyone toward offices and screens. Turns out the people who didn’t listen built the most automation-resistant careers. Most ironic outcome of the AI revolution. The work society treated as inferior turned out to be the work society couldn’t replace. And the work society valued most turned out to be the easiest to eliminate.

Elon Musk thinks coding dies this year. Not evolves. Dies. By December, AI won’t need programming languages. It generates machine code directly. Binary optimized beyond anything human logic could produce. No translation. No compilation. Just pure execution. Musk: “You don’t even bother doing coding.” Code was never the point. It was friction. A tax we paid because machines didn’t speak human. AI just learned fluent human. The tax is gone. Now plug that into Neuralink. No syntax. No keyboard. No screen. Musk: “Imagination-to-software.” Thought becomes executable. You imagine an outcome, the system architects and compiles it into reality instantly. We’re not automating programming. We’re erasing it from existence. The entire profession collapses into a thought. Decades of training reduced to irrelevance. The gap between idea and instantiation hits zero. You don’t build anymore. You imagine, and it materializes. Not incremental progress. Total phase shift. The way humans have created things for ten thousand years just became obsolete. Welcome to a world where the limiting factor isn’t skill, resources, or time. It’s whether you can picture what you want clearly enough for a machine to birth it into existence.

Elon Musk on why Community Notes is so powerful: “Community Notes is the best. It’s awesome because everybody gets checked, including me All the software is open source, and all the data is open source. So you can recreate any note independently. Total, absolute transparency in everyway Sometimes people ask me to remove a note. I’m like...... I don’t even remove notes on my own account. Nothing. If I did that, it would stick out like a sore thumb immediately” The best counter to misinformation isn’t censorship It’s better information - checked in real-time by millions of people who can actually look at the source material themselves That’s how you get closer to the truth