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Open-math estimators for the AI Stack. Inputs are transparent, formulas are visible, presets are labelled. Useful for comparisons, dangerous as fake precision.
A money map of the AI stack: who pays whom, and how much, from hyperscaler capex down to the wafer-fab tools.
Trace the AI supply chain as a money map. Hyperscaler and neocloud demand flows down through compute, the three-player HBM memory oligopoly, foundry and packaging into the wafer-fab tools, with ASML standing alone as the sole-source EUV monopoly. Directional estimates, not advice.
Stack one basket of companies against another on live market caps, and see what the market prices as equal.
Build two baskets of tickers and watch their market caps stack up side by side. When a whole industry equals a single company, you can see where the market is paying up and where it is not. Live numbers, no judgment: the equivalence is the fact, what it means is your call.
Name a company; the agent assembles a short, objective one-pager you can take to your own decision.
The agent runs the work end to end: it pulls the trusted snapshot, checks what the market prices the name as equal to, maps how the story has moved over time, then writes a tight one-pager: the bet, the variant view both ways, and the single thing that would end the thesis. It presents both sides and never tells you what to do.
Enter a ticker; get the bet, the return it could generate, and what breaks the thesis.
Look up a stock and get a structural read for the moment before you buy: what you are actually levered to, a bull, base, and bear with honest return ranges, how it compares to its peers on live numbers, and the few things that would break the thesis. Structural diligence, not advice.
Turn bear / base / bull ranges for growth, margin, and the exit multiple into the full distribution of fair value and return.
Stop arguing single point estimates. Set a range for the three drivers that set a stock’s value, how fast it grows, what it earns, and what it trades at, pick a sampling shape, and watch 10,000 trials redraw the fair-value histogram (today’s price marked), the percentile band, and a Damodaran-style "X% chance of at least Y% return" table. Archetype presets seed sensible ranges; every number is yours, the math is visible.
When the frontier labs expect human-level AI, and what each step unlocks.
A living read on the AGI timeline: each lab’s forecast, the capability ladder behind it, and what to do at each rung. Updated every time a new model moves the date.
Funnel, unit economics, and the distribution lever, for any consumer AI subscription.
A working model of consumer AI subscription growth. Pick a lab, size the funnel, watch LTV, CAC, ARPU, and payback move, and test the one lever that decides the economics: where the next subscriber comes from, whether that is paid media, the apps you already own, the device, or the carrier bill. Every input is editable; the formulas are the deliverable.
Orbital vs terrestrial AI compute: the launch-cost breakeven in $/GPU-hour.
Run the same GPU rack in orbit and on the ground, and find the launch cost at which space wins on $/GPU-hour. Then meet the real constraint: not lift cost, but the mass of the radiator you must launch, and never service, to dump the heat in a vacuum. Sourced defaults, every input editable, the breakeven math is the deliverable.
Datacenters, the power plants feeding them, and the labs, on one US map.
An interactive map of the physical AI buildout: the headline training datacenters (Stargate, Hyperion, Colossus) and major hyperscale hubs, the power plants tied to them (Palo Verde, Vogtle, the nuclear restart deals), and frontier-lab HQs. Marker size scales with capacity; tap any site for detail. Custom-drawn, no map API.
How building with AI goes wrong: incidents and the anti-patterns behind them.
A growing checklist of AI build failures: named, sourced public incidents plus the reusable anti-patterns they teach (unreviewed output, prompt injection, over-automation, capability overtrust). Read it yourself or point your agent at it before you ship. Each card has a run-through checklist.
Balance grid energy, Blackwell compute, parameter-scaling models, software SaaS, and humanoids.
Can you orchestrate the AI Stack to align a safe superintelligence? Acquire modular nuclear power, secure Blackwell GPUs, manage parameters, and deploy physical systems while answering realistic Fermi estimations and navigating policy bottlenecks.
Tokens, watts, dollars: the cost of running a model at scale.
Plug in workload shape (model size, precision, monthly tokens) and get back the MW, capex, annual GPU rental, water draw, and cost-per-million-tokens. Lab presets included; every input is editable, every formula is visible.