https://blossom.primal.net/207842644aec15af4571a83a839720c8c2690b3174407bfab43b9a70bdd21fda not looking so hot for ai bros

what if ai fails to deliver ? | 1 | **Capex/ROI mismatch** The master risk. The industry is spending as if AI revenue will arrive quickly and at high margins. Goldman’s baseline model implies **$765B annual AI capex in 2026**, rising to **$1.6T in 2031**. If enterprise ROI is slow, the denominator of the ROI equation explodes before the numerator arrives. | 2 | **Hyperscaler capex reversal** The most important near-term trigger. If Microsoft, Google, Amazon, Meta, Oracle, or CoreWeave slows AI capex guidance, the market immediately questions Nvidia demand, data-center growth, cloud growth, and AI multiples. Microsoft disclosed **$37.5B Q2 FY26 capex**, with roughly **two-thirds on short-lived GPUs/CPUs**. | 3 | **Token price compression / open-source commoditization** Cheap models do not need to beat frontier labs everywhere. They only need to be good enough for ordinary enterprise workflows. OpenAI’s API page lists high-end text pricing in dollars per million tokens, while DeepSeek lists V4 pricing far lower, including extremely cheap cache-hit pricing. | 4 | **GPU depreciation and impairment risk** ai chips are short-lived assets. If useful economic life is shorter than accounting life, reported earnings overstate true economics. Microsoft explicitly said roughly two-thirds of capex was for short-lived GPUs/CPUs, and CoreWeave’s Q1 2026 results already show **$1.147B depreciation and amortization** in one quarter. | 5 | **Utilization gap: capacity before workloads** Capacity is being built before enterprise workflows are fully proven. If clusters sit underutilized, providers discount pricing, resale values fall, and debt/capex ROI collapses. This is the classic infrastructure-bubble failure mode: demand is real, but timing and scale are wrong. | 6 | **Nvidia + Mag-7 concentration risk** The S&P 500 is highly exposed to AI-premium names. Nvidia’s own filing showed four direct customers accounted for **22%, 17%, 14%, and 12%** of accounts receivable as of Oct. 26, 2025, showing concentrated payment channels even if end demand is broader. ([SEC][4]) | 7 | **Infrastructure credit / GPU-backed debt risk** The AI boom is becoming a credit story. CoreWeave reported **$99.4B backlog**, but also **$536M interest expense**, **$740M net loss**, and heavy depreciation in Q1 2026. If utilization or customer demand weakens, GPU-backed debt and data-center finance become fragile. | 8 | **Enterprise ROI evidence remains weak** Adoption is not the same as ROI. NBER found most firms report little measurable impact so far, including **89% reporting no impact on labor productivity** over the past three years. PwC’s 2026 CEO Survey found many CEOs still do not see AI financial benefits. | 9 | **Software gross-margin leakage** AI may make software better but margins worse. SaaS companies historically enjoyed very high gross margins, but AI introduces inference costs, model costs, monitoring, and support. If customers expect AI features to be included, vendors absorb cost without proportional pricing power. | 10 | **Agentic AI cost explosion** Agents use many more tokens than chatbots. GitHub is moving Copilot to usage-based billing from **June 1, 2026**, with usage calculated from input, output, and cached tokens. That is a clear signal that agentic workflows are too compute-intensive for simple flat-rate economics. | 11 | **Power/grid/social-license backlash** AI infrastructure needs electricity, land, water, substations, and local political permission. IEA projects data-center electricity consumption will reach around **945 TWh by 2030**, growing about **15% per year**. Data Center Watch says **$64B** of U.S. data-center projects have been blocked or delayed. | 12 | **Model routing destroys premium inference mix** Enterprises will not use premium frontier models for everything. They will route easy tasks to cheaper models and reserve premium models for hard tasks. That means usage can rise while premium revenue mix falls. This is especially dangerous if capex was justified by high-priced frontier inference. | | 13 | **Data cleanup + integration debt** AI demos are easy; enterprise integration is hard. Messy permissions, legacy systems, poor metadata, siloed data, and bad process design mean companies need data engineering and workflow redesign before AI creates real ROI. This cost is often ignored in capex optimism. | 14 | **Verification/rework tax** Workday found nearly **40% of AI time savings are lost to rework**, including correcting, rewriting, and verifying outputs; only **14%** of employees consistently see clear positive net outcomes. That directly reduces net productivity. | 15 | **AI features become table stakes** Customers may expect AI inside every product without paying much extra. That converts AI from a pricing-power tool into a defensive cost of doing business. This is dangerous because the capex was spent assuming incremental monetization, not merely product maintenance. | 16 | **Backlog quality / circular demand risk** Backlog only matters if it is durable, profitable, collectible, and not circularly financed. If AI labs, cloud providers, chip vendors, and neoclouds are funding each other’s demand, headline revenue may overstate true end-customer economics. | 17 | **Legal/IP/data-licensing tolls** Anthropic’s proposed **$1.5B author settlement** and California’s training-data transparency rules show the free-data era may become more expensive and legally constrained. That raises model-development cost and can slow scaling. | 18 | **On-prem/private AI drift** Enterprises may still adopt AI, but through private, hybrid, open-weight, or on-prem systems. That can weaken centralized API margins and reduce cloud vendor pricing power, even while total AI usage rises. | 19 | **Customer concentration** AI supply-chain demand is more concentrated than it appears. Nvidia’s receivable concentration shows that a small number of direct customers matter heavily. If a few hyperscalers or integrators slow orders, the revenue impact can be fast. ([SEC][4]) | 20 | **Procurement discipline and usage caps** As AI bills become visible, CFOs will demand usage caps, cheaper models, vendor competition, and outcome-based pricing. GitHub’s move to usage-based billing makes token consumption financially explicit, which will train customers to optimize costs. | 21 | **Search/ad cannibalization** AI search may reduce traffic to websites and pressure the content ecosystem. Pew found Google users clicked traditional links in **8%** of visits with an AI summary versus **15%** without one. This can hurt publishers, content supply, and search monetization. | 22 | **Physical supply-chain bottlenecks** AI buildout needs more than GPUs: fiber, optics, transformers, substations, cooling, construction labor, and power generation. Bottlenecks raise cost and delay monetization, reducing returns on already-committed capex. | 23 | **Human accountability limits labor substitution** In legal, finance, healthcare, compliance, and high-risk operations, humans still need to review and approve outputs. That caps labor savings and turns AI into augmentation rather than full substitution. | 24 | **Synthetic-data / scaling limits** Epoch AI estimates the effective stock of quality-adjusted public human text at around **300T tokens**, with full utilization possible between **2026 and 2032** if trends continue. If public data becomes scarce, future model improvements may cost more. ([Epoch AI][12]) | 25 | **Consumer surplus captured by users, not providers** AI may be enormously useful but economically deflationary. Users and customers may capture the benefits through cheaper intelligence, while infrastructure owners compete returns away. This is the telecom-fiber analogy: society wins, overfunded capital loses. 1. AI vendor subsidizes usage during early adoption 2. Users build habits around cheap/flat-rate tools 3. Agentic workflows multiply token consumption 4. Vendor discovers gross margin pressure 5. Vendor shifts to credits/usage billing 6. Customer discovers true cost 7. CFO imposes budget caps 8. Teams route to cheaper models 9. Premium AI revenue growth slows 10. AI labs either cut prices or lose volume 11. Hyperscaler capex ROI weakens

following my previous post

energy $CEG is the ultimate ai trade

OpenAI to raise $110B at $730B valuation, expands cloud & chip partnerships • OpenAI to raise $110B in new investment at a $730B valuation • Expanding AWS agreement by $100B over 8 years • Broadening long-standing partnership with NVIDIA

Oracle (ORCL) is trading higher today, February 25, 2026, following a major analyst upgrade and easing concerns regarding its massive capital investment strategy. The stock rose roughly 4% in today's session after Oppenheimer upgraded the shares to Outperform from Perform, citing a favorable risk-reward profile after a significant multi-month selloff. Oracle is raising $45 billion to $50 billion in 2026 through debt and equity to fund a historic buildout of Oracle Cloud Infrastructure (OCI). The company's growth is heavily tied to major AI contracts with OpenAI, Meta, NVIDIA, and TikTok. Easing concerns about OpenAI’s funding stability has improved investor sentiment. Analysts project Oracle’s EPS could double or triple by fiscal 2030 as massive capital expenditures begin to monetize, though short-term free cash flow remains under pressure. PT $286

Fed Will Be Main Buyer of Treasury Bills in 2026 The share of T-bills outstanding has increased to 22%, and 85% of Treasury gross issuance is T-bills. $10 trillion of government debt will mature over the next year, which is 33% of all debt outstanding. conclusion : own hard assets ( gold / bitcoin / land / metals , ... )

Power, lack of available power, reliable power has become the biggest bottleneck for ai

AI DATA CENTERS WILL QUADRUPLE GLOBAL POWER DEMAND IN THE NEXT 10 YEARS Battery storage led by $TSLA & $EOSE will stabilize the grid by storing excess generation and discharging during peak inference cycles.

" It’s Better To Be a Warrior in a Garden Than To Be a Gardener in a War " - Miyamoto Musashi

for my future self on how to solve problems : 1. identify the problem 2. break it down into basic elements, the most fundamental facts or truths that cannot be reduced further 3. challenge assumptions : question everything about the problem 4. rebuild from scratch, use first principles to construct a new solution / free from preconceived notions

if your peace depends on everything going right, its not peace, its control. learn to be steady and calm / grounded during uncertainty , thats real growth.

european central bank : There is an urgent need to channel retail savings into capital markets to develop those markets and finance EU priorities. An EU savings standard could increase retail participation, benefiting savers, boosting investment and supporting strategic priorities. wtf ?

All men are created equal, but some train harder in the preseason.

relationship questions : 1 - If someone told you, you were a lot like your partner would this be a compliment? 2 - Are you truly fulfilled or just less lonely? 3 - Are you able to be unapologetically yourself, or do you feel the need to show up differently to please your partner? 4 - Are you in love with your partner as a whole, or are you only in love with their good side, their potential or your idea of them? 5 - Would you want your own child to date someone like them?

jpow on today fomc: stagflation

You’re not as special as you think. Most people are average, and that includes you. The world doesn’t revolve around you, and expecting otherwise leads to entitlement and disappointment. Success comes from hard work and luck, not inherent uniqueness. Time is your most finite resource. Every second you waste is gone forever. You’ll never get back the hours spent procrastinating or chasing meaningless distractions. People often realize this too late—when health fails or loved ones are gone. Life is inherently unfair. Good people suffer, and bad people often win. Hard work doesn’t always pay off, and talent can go unnoticed. Systems favor the privileged, and random chance plays a bigger role than most admit. You can do everything right and still lose. Everyone you love will die, and so will you. Mortality is non-negotiable. You can’t escape it, and neither can anyone else. The longer you live, the more loss you’ll experience, and one day, you’ll be the one leaving others behind. Happiness is fleeting, not a default state. You’re not entitled to constant joy. Life is a mix of highs and lows, and chasing endless happiness leads to frustration. Contentment comes from accepting this ebb and flow, not from external achievements or possessions. Most people don’t care about you. Beyond a small circle of family and friends, the world is indifferent to your struggles or successes. People are too busy with their own lives to prioritize yours, and expecting otherwise sets you up for loneliness. You’re responsible for your own problems. While circumstances can be tough, blaming others—parents, society, or luck—keeps you stuck. You have to take ownership of your choices and their consequences, even when it’s painful to do so. Relationships require constant effort. Love, friendship, and family ties don’t survive on autopilot. Neglect them, and they’ll fade. People change, and so do their needs—staying connected means adapting, compromising, and showing up consistently. You’ll never have all the answers. Life is chaotic and unpredictable. No matter how much you plan or learn, uncertainty will always be there. The need for control can paralyze you; sometimes, you just have to act without knowing the outcome. Your health will decline, and you’ll regret not caring sooner. You can’t outrun aging. Poor habits—bad diet, no exercise, stress—catch up. By the time you notice the damage, it’s often harder to fix. Prioritizing health now is easier than fixing it later. Money matters more than you’d like to admit. It doesn’t buy happiness, but it buys security, freedom, and options. Poverty is a trap that limits your choices and amplifies stress. Ignoring financial reality can lead to a life of struggle. You’ll disappoint people, and they’ll disappoint you. No one’s perfect, including you. You’ll let down those you care about, and they’ll do the same. Holding onto grudges or guilt over this wastes emotional energy—forgiveness is often the only way forward. Society rewards conformity, not authenticity. Being true to yourself can make you an outcast. People fear what’s different, and systems are built to enforce norms. Standing out often comes with rejection, criticism, or isolation. Your dreams might never come true. Not every goal is achievable, no matter how hard you try. Timing, luck, and circumstances play a role. Clinging to unrealistic dreams can stop you from finding fulfillment in what’s actually possible. You’re more selfish than you realize. Self-preservation is hardwired into humans. Even your kindest acts often have an underlying motive—validation, reciprocity, or feeling good about yourself. Pure altruism is rare, and that’s okay. Comfort kills ambition quietly over time. Stay comfortable long enough, and you'll forget who you were becoming. pain of discipline > pain of regret

global debt $324 trillions as of q1 2025

🤨 fiat endgame