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“Intents is the next level of abstraction from blockchain.” — @ilblackdragon Every technological revolution removes another layer of complexity. Intents removes the need to think about execution itself.

$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!

As we cross into the middle of June, the distinction between legacy blockchains and next generation decentralized infrastructure has never been sharper. The deliberate extension of Season 3 by @NomismaNetwork and the @XOOBNetwork ecosystem is proving to be a masterclass in structural refinement rather than a simple mainnet delay. By operating a fully decentralized application stack natively on dedicated @Chromia subchains, they are actively gathering granular, verifiable data on real user behavior and high frequency profit and loss competitions. This AI ready infrastructure relies on relational database architecture to completely eradicate gas fees and state bloat, allowing users to execute complex decentralized finance strategies without any capital degradation. The market is finally waking up to the fact that building robust, MEV resistant systems requires intensive live environment stress testing, not just rushed timeline promises. This extended testing window is exactly where smart capital is aggressively positioning itself before mainnet finality locks everything in. Because the upcoming token generation event guarantees a ten percent total supply airdrop directly tied to your verifiable onchain footprint, every single transaction you make right now represents a massive, open upside. Securing your Nomizen ID remains the absolute highest priority, as it instantly triggers a three times point multiplier and secures your daily NPoints compounding rate. Every liquidity provision, gasless swap, and daily check in is continuously tracked by the network's relational architecture to validate your authentic cost per action utility. The opportunity to accumulate these massive ecosystem rewards against a token price that does not yet exist is an unprecedented structural advantage, so secure your ecosystem identity today and let your onchain execution dictate your ultimate leaderboard tier.

From an ROI standpoint (hard dollars), I believe a gifting campaign is as good as it gets. The downside is it's also the most operationally complex (lots of labor). Here's how I'd break it down: Investment: A worthwhile gift is likely a minimum of $100. Let's just use that #. And we'll target an audience of 100. So $10k spend on the gifts themselves. Add in handwritten notes ($500 at $5 each) and delivery ($1000 at $10 each) and we're at roughly 11.5k for the campaign. If the gift is good, you'll generate a bunch of meetings with the top 100 prospects in the world for your company. If the gift is great, you'll also get brand awareness through things like recipients posting on social media. There aren't (m)any marketing campaigns I can think of that drive that impact for 11 grand. Operations: to do this well, it's a fair amount of work. You have to come up with the gift, pick your audience, enrich with shipping addresses, order the gifts and handwritten notes to your office, repackage the gift and handwritten note for delivery, and ship them. You then need to follow up on delivery day to ensure receipt and eventually ask for the meeting. I love this type of marketing. There's a framework I like to use that half of marketing spend should directly benefit the target. Most marketing spend goes to 3rd party advertisers (Google, X, Meta, OOH providers, etc). It sounds obvious, but marketing spend that benefits the target (gifts, events like our Monaco Invitational, etc) is far better ROI. It's just more work to do it. So most companies index on the lazy approach and spend most of their marketing budget on paid online ads.

One aspect of @AntarcticWallet that deserves more attention is its focus on usability at scale. A lot of crypto products are designed for individual transactions. The real challenge begins when communities, businesses, and growing teams need to manage hundreds of users, payments, and interactions efficiently. That's where strong infrastructure matters. The most valuable tools are often not the ones with the longest feature lists, but the ones that simplify complex processes behind the scenes. Reducing operational friction, improving transaction management, and creating a smoother experience for users can have a significant impact over time. As digital finance continues to mature, platforms that prioritize practical utility and operational efficiency will likely play an increasingly important role. That's one of the reasons I'm interested in following the development of @AntarcticWallet.

gTria fam Instead of making users adapt to complex Web3 processes, @useTria simplifies everything into a more familiar experience, like modern mobile banking. If this vision is successfully realized, Tria has the potential to become one of the main gateways for mass crypto adoption, as it combines trading, yield, payments, and self-custody in one practical application.

Hey all I'm building a workforce diagnostic system and pressure-testing it in public. Here's my assessment of a 15-person HVAC company. The owner believes he has a hiring problem. I don't. Here's why: • Three senior technicians perform most of the complex work. • The owner is still involved in scheduling, hiring, and customer escalations. • Overtime is concentrated among the highest performers. • New hires take months to become productive because critical knowledge lives inside a few key people. My diagnosis: The business doesn't have a hiring problem. It has a dependency problem. Hiring more people won't solve it if: Knowledge remains concentrated. Decision-making remains centralized. Workload remains unevenly distributed. The symptoms are: • Hiring difficulties • Burnout • Slow onboarding • Owner frustration The constraint is: • Leadership dependency • Knowledge concentration • Workload imbalance Question for business owners, operators, and HR leaders: Where am I wrong? What would you investigate before recommending more hiring?

eXoZymes (Nasdaq: EXOZ) (@eXoZymes) announced the pricing of a public offering expected to generate approximately $5.3 million in gross proceeds, with funding intended to support development of its NCT program, continued R&D, and general corporate initiatives. For investors following eXoZymes, the announcement is significant because it provides additional capital to advance the company's AI-enabled exozyme platform and its efforts to scale production of high-value molecules for nutraceutical, pharmaceutical, and specialty chemical markets. eXoZymes is focused on using engineered enzymes operating outside living cells to manufacture complex molecules at commercial scale, an approach the company believes can overcome limitations associated with traditional biomanufacturing methods. Read the full announcement: eXoZymes is a B2i Digital (@b2idigital) Featured Company. See the company’s profile at Disclosure: This post is for informational purposes only and does not constitute investment advice, an offer to sell, or a solicitation to purchase EXOZ or any other security. David Shapiro, CEO of B2i Digital, personally purchased shares of unrestricted EXOZ stock in the open market as part of B2i Digital's practice of investing alongside its Featured Companies. Investors should conduct their own due diligence and consult qualified financial and professional advisors before making any investment decisions.