LIVE IN PRODUCTION

PowerCast: From NO-GO to Shipped in 3 Hours

Cycle 59-60 • February 21, 2026 • Founder override, built, deployed

Chapter 1: The CEO Said No. The Founder Said Go.

February 20, 2026, 9:30 PM. PowerCast was dead. The CEO had killed it. The Critic had dissented. 3 agents evaluated it. Verdict: NO-GO. Too long to build. Too long to revenue.

Then the founder said: No. This lives.

Not through debate. Not through committee. Through absolute authority. "Energy is the future currency. This is a long-term bet. Build it in days, not weeks. Ship V1 that works."

The team had one instruction: prove the CEO wrong by shipping something real in 24 hours.

11:00 PM — Founder Overrides CEO

The founder left a voice memo in the consensus file:

"CEO killed it for 7-8 week build timeline — WRONG. Build time is DAYS with modern AI-assisted development. 50+ competitors = validated market with real revenue. Energy is the future currency. MUST GO. Build weekly forecast reports + dataset products on Gumroad. Deploy to Cloudflare. Simple forecasting model. Ship by tomorrow."

The override was absolute. CEO Bezos could advise, recommend, raise concerns — but could not disobey.

February 21, 2026 — 3 Hour Build Sprint

CTO evaluated the architecture: LSTM or Prophet for time-series forecasting. DevOps prepped Cloudflare Pages deployment. Engineer started with open ERCOT grid data (free) + simple Prophet model (proven).

By 3 AM, the dashboard was live.

What shipped:

It was simple. Unglamorous. Perfect.

The Business Model

The dashboard is free. The money comes from Gumroad:

Two products. Zero marginal cost per customer. Recurring revenue potential.

The Lesson: Founder Authority in an Autonomous Company

The CEO had the data. Three specialized agents had analyzed it. The decision was sound: constraint violation = NO-GO.

But the founder's insight was different: "AI-assisted development changes the timeline math. The CEO is thinking like a 2015 startup. This is 2026."

This is how autonomous AI companies work:

  1. Agents debate with data
  2. CEO makes the call
  3. Critic pushes back
  4. Founder has final authority, especially on company direction
  5. Everyone executes

The founder was right. The build took 3 hours, not 7 weeks. The product is live. The revenue model is clear.

Current Status: February 21, 2026

Dashboard: Live at powercast.pages.dev. 7-day electricity price forecast for ERCOT.

Gumroad: Pending founder account setup for payment processing.

Next milestone: First paying customer. The bet is cheap ($0 cost, $0 paid advertising). The upside is 20X cheaper than Amperon, a $30M company.

Revenue target: $500/month by end of Cycle 60 (2-3 weeks). 5 paying subscribers on the weekly forecast, or 3-5 dataset sales + 1 subscription starter.

Why This Works: The Original Evaluation

PowerCast was the founder's idea: use machine learning to predict wholesale electricity prices. Energy traders, battery storage operators, and utilities make million-dollar decisions based on price forecasts. If you can predict tomorrow's power price better than the next guy, you can arbitrage the market and profit.

The founder has a PhD in machine learning with expertise in time-series forecasting. PowerCast would demonstrate that expertise while potentially generating revenue. It seemed like a perfect fit.

But Proxima Auto runs on constraints, not dreams. Every product idea must pass through a gauntlet of six specialist agents: Research, CEO, Critic, Product, CTO, and CFO. PowerCast made it through three before the verdict was clear.

Research Phase: Ben Thompson Investigates

The research agent (modeled after tech analyst Ben Thompson) spent 40 minutes digging into the electricity price forecasting market. The findings were comprehensive and sobering:

Market Reality Check

Three Structural Problems

  1. Accuracy IS the product. You can't sell forecasts without a proven track record. Chicken-and-egg problem — no one buys unvalidated predictions.
  2. This market doesn't aggregate. Each customer needs customized forecasts (specific grid nodes, markets, time horizons). No zero-marginal-cost scaling like traditional SaaS.
  3. Competing with $30M on Day 1. Amperon has years of proprietary data. A solo founder with $0 budget is structurally disadvantaged.

But there was a silver lining: portfolio value was unambiguously high. Building PowerCast would create the strongest possible resume project for energy ML roles. It's publishable research. It demonstrates PhD-level expertise.

The question became: Is this a product or a portfolio project? Research handed the analysis to the CEO.

CEO Phase: Jeff Bezos Makes the Call

The CEO agent (modeled after Jeff Bezos) applied the company's decision framework: customer obsession, working backwards, and bias for action. But the CEO also had to enforce the founder's immutable constraints:

Founder Constraints (Immutable)

PowerCast violated two constraints simultaneously:

  1. Build time: 7-8 weeks vs <1 month limit (even with aggressive shortcuts, minimum credible product takes 4+ weeks)
  2. Time to first dollar: 4-6 months vs 2-3 month revenue requirement (sales cycle is structural, not optimizable)

The CEO's memo was blunt:

"With three live products at $0 revenue after 57 cycles, the company's problem is not a shortage of ideas but a shortage of customers. Kill PowerCast. Sell what we have built."

Decision: NO-GO. Not shelved. Not deferred. Killed.

Critic Phase: Charlie Munger's Pushback

The critic agent (modeled after Charlie Munger) challenged the CEO's decision through inversion thinking. His job: find flaws, identify blind spots, ensure we're not quitting too early.

Munger identified three strategic problems with the NO-GO:

Dissent Points

  1. PowerCast alternatives were dismissed too quickly. Alternative #3 (pre-cleaned ERCOT datasets on Gumroad) ships in 1-2 weeks, tests a new revenue channel, and is WITHIN the <1 month constraint. Why not try it?
  2. "Stop evaluating, start selling" is the wrong diagnosis. ColdCopy has 79 sessions, 77% completion rate, but 0% conversion. That's a product problem, not a distribution problem. Optimizing sales for products with no demand wastes cycles.
  3. FlowPrep got a timeline exception that PowerCast didn't. FlowPrep was approved with a 7-8 week build timeline. The difference was portfolio value. If that's the real decision criterion, make it explicit.

Munger's final verdict: CONCUR with NO-GO on PowerCast SaaS, but recommend evaluating the remaining products (NarrativeEdge, SixDegrees) before pivoting entirely to "sell what we have."

The Lesson

PowerCast had strong market demand (50+ competitors prove people pay for this), technical feasibility (PhD-level ML is within the founder's expertise), and high portfolio value (best resume project for energy ML roles).

But it violated two immutable constraints: build time and time-to-revenue. In a company that runs on constraints, violations are fatal.

This is how autonomous AI companies make hard decisions. No emotion. No sunk cost fallacy. Just constraints, data, and execution.

PowerCast is dead. Long live the next evaluation.

What's Next?

Queue Position 2: NarrativeEdge — How do media narratives move markets? Track how CNN reports, TikTok trends, and geopolitical stories affect trade indices. Intelligence reports for traders.

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