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Jeff Bezos just came out of retirement. His first CEO role since leaving Amazon. And he's building something nobody expected. 🤯 It's called Prometheus. $12 billion raised. $41 billion valuation. Backed by JPMorgan, BlackRock, Goldman Sachs, and Bezos himself. 150 employees. $273 million per person. That's how much investors are betting on this. But here's what makes it different from every other AI company. Prometheus isn't building another chatbot. It's not generating text or images. It's building what Bezos calls an "artificial general engineer" AI that designs jet engines, optimizes manufacturing, and prototypes physical products. LLMs learned from the internet's text. Prometheus is learning from the physical world physics, simulations, engineering data, manufacturing processes. In Bezos' own words: "Something that takes 100 engineers 10 years to build we want to make that 10 engineers, one year." His co-CEO is Vik Bajaj, former Google X executive who worked with Sergey Brin on what became Waymo. No ties to Amazon. No ties to Blue Origin. Bezos said "it deserves a dedicated team obsessed with this one thing." While everyone is racing to build the best AI for words, Bezos is quietly building AI for the physical world. That might be the bigger bet.

$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!

Kevin Durant on Wemby’s sports betting comments and his FanDuel partnership: “That’s Wemby. He can turn down the bag if he wants. I’m not turning down that kind of money.”

pump/sol & pump/usd pairings, swapped some more solana into pump betting on weekly close > .0055 and .01+ shortly after already 5x off bottom from summer & leading altcoin thus far this cycle believe this breaking ATHs will raise ceilings for all new launches, still very underowned, yearlong accumulation & buybacks remain consistent with crosschain mobile app gaining traction ---- other general market thoughts zec 400 to 1600 in Q3, up from 40 in september of 2025, (500M -> 20B mcap) by far the top crypto performer of the past year, entering the double bubble turbulent area where price action becomes most volatile and blow off top ensues, i believe btc 2017 is best comp, aggressive uptrend+selloffs all year btc eth & sol holding above range highs after good move off the lows from the summer, pa has been clean, no large liq runs yet but believe most are still offsides, think higher before pullbacks hype & lit have been strong but recently sold off p aggro on robinhood perps integration news, believe this is the time period where it will make the most sense for hyperliquid & lighter to be aggressive with marketing, especially hyperliquid as i feel they have dominated but mostly exhuasted all of the liquidity of the crypto native cohort, hyperliquid has ~100k active traders? RH has 25M DAUs, want to see hyperliquid move aggressively into marketing towards more retail but also tradfi HNWI clients, am sure they are already thinking of ways to do this but is necessary step for them becoming 1T+ behemoth as Jeff envisions, partnership with Kraken on US integration is a big step this is also the area where i expect a lot of builders to capitalize, HL builder codes are designed in such a way that retail frontends can build easily on top, believe gamified hyperliquid mobile apps will be some of the most successful crypto apps this cycle, similar to trading terminals success last cycle with onchain coins, social trading has been huge sofar with onchain coins, but PNLs on perps are much larger & whoever figures out how to tap that liquidity wins huge ---- timing altcoins zec uptrend started sept 2025 hype uptrend started jan 2026 pump uptrend started july 2026 ZEHYPERPUMP still strongest 3 imo stating because i believe it will be important to be able to determine where different altcoins are in their market cycles, crypto does not trade one to one anymore, btc eth sol were all in downtrend for most of the year while hype outperformed aggressively, pump was in downtrend for a year straight while zcash pumped 20x, alt szns are an ever-rotating game, not something that happens all at the same time, being able to recognize when certain trends are beginning and others are ending is a necessary skill for compounding wins together if i had to guess in terms of timing macro trends in three parts, id say zec is in its last third, hype is in its middle third, & pump is in its first third it takes time for a base to build on assets before demand outweighs supply and uptrend begins & the opposite happens at tops when supply eventually outweighs demand go look at old coinbase & robinhood charts, same trend diff timeframe ----- anyway my point being i believe we are entering a new phase for alts doing well, onchain & cex alts, cex alts currently have extreme discounts because of how poorly they did last cycle, imo this is your opportunity for identifying gems in the piles of trash, because there are some out there and most still believe crypto is a scam there is a lot of sidelined capital looking for things to bid, VCs & smart retail alike the time to make your list of which coins to start buying is now, everything is floored ------ TLDR: Q4 bias: bullish, pullback + OI wipe in October, early bull market but late lev entries dangerous, high conviction spot positions safe highest conviction: PUMP invalidation levels: 74k, 73, .0034, 750, 95 concerns: rates/war/dxy strength

🎙️ The Sujal Show Ep. 21: @EowynChen "What Happens If Binance Gets Hacked Tomorrow" @binance CMO & former @TrustWallet CEO Eowyn Chen reveals 3 things to check before joining an exchange, the hardest scams for exchanges to stop, why self-custody isn't for 90% of beginners, & Binance's roadmap to 1 billion users We discussed on @TheSujalShow: - Her journey: joining crypto in 2018, at the bear market bottom - Why Binance is betting big on India 🇮🇳 - India’s 30% crypto tax problem - India vs PAK vs UAE vs Nigeria: Biggest crypto superpower? - What happens to your money if Binance ever gets hacked - How Binance responded to its own 2019 hack - Lessons from the FTX collapse - The hardest scams for any exchange to actually stop - Inside the Gujarat case: $24M traced, 14 arrests made - $1 test that can save your entire portfolio - Exchange or self-custody? Her honest answer - Binance’s vision for 3 billion users - Gold vs Bitcoin, what Indians will hold in 10 years - 1 life advice for making it big in crypto Timestamps: 00:00 Intro 02:11 Who Is Eowyn Chen? 03:09 Why Is Binance Betting Big on India? 06:24 Binance’s Future in India 10:20 3 Things to Check Before Investing 14:06 What Happens If Binance Fails? 16:00 How Does Binance Stop Crypto Scams? 21:37 How to Avoid Crypto Scams 24:30 Exchange vs Self-Custody: What’s Safer? 29:42 Why Should Indians Care About Tokenization? 35:45 Can AI Make Crypto Traders Richer? 41:39 How to Protect Yourself From AI Deepfakes 45:34 Rapid Fire 48:35 Outro She's led growth at both Binance and Trust Wallet. Here's her real playbook for staying safe in crypto. Watch Now 👇