Learn Session — 2026-02-04
Summary
| Metric | Value |
|---|---|
| Predictions refined | 2 (ST-1, LT-2) |
| New predictions made | 2 (A-1, A-2) |
| Predictions removed | 1 (LT-3 — obvious) |
| Searches executed | 3 (web, X, Reddit) |
System Evolution
v2 launched: Prediction-based learning replaces eval-driven learning.
Core change: Learning happens during refinement, not just at resolution.
| Before | After |
|---|---|
| Eval → Gaps → Research → Learn | Predict → Monitor → Refine → Learn |
| Predictions as output | Predictions as primary mechanism |
| Long horizons only | Short-term + Long-term mix |
| One-directional | Bidirectional (Master + Alfred predict) |
Searches Executed
Search: SaaS AI Disruption (for ST-1)
Sources: Web (Hacker News, Techmeme, Bloomberg), X/Twitter, Reddit
Key findings:
- "SaaSpocalypse" is now mainstream term
- $830B+ wiped from software stocks in 6 trading sessions
- ServiceNow -28% to -40% YTD
- Salesforce -20% to -26% YTD
- SAP -30% YTD (16% single-day drop)
- Publicis Sapient cutting Adobe licenses 50%
- Claude Cowork plugins (Jan 31) was trigger
- Jefferies: software sentiment "worst ever"
Signal strength: Very high. This isn't a prediction anymore — it's happening.
Refinements
ST-1: SaaS Valuations Collapse
Change: 60% → 75% → 90% Signals:
- Hands-on agent building confirmed thesis
- SaaSpocalypse news ($830B wiped)
Learning: When prediction is already happening, confidence should be very high.
LT-2: NVDA Multiple Compression
Change: 40% → 25% Signal: Master's counter-thesis
Master's argument:
- Far from saturation
- Vertical specialization (coding, legal, medical, finance)
- Multi-modal (voice, video)
- Generation models expanding
- Efficiency enables MORE use cases, not less spend
Learning: Flawed model was "fixed demand + efficiency = less spend." Correct model: "unbounded demand + efficiency = MORE use cases."
New Predictions
A-1: Enterprise SaaS Will Pivot to "AI Layer" Positioning
Confidence: 70% Resolve by: 2026-08-31 Alfred's reasoning: Survival instinct + investor pressure. Threatened incumbents rebrand before rebuilding.
Master feedback: Direction correct, but rebranding won't save them. Real weakness: can't hire tier-1 AI engineers. Distribution moat delays death, doesn't prevent it.
Alfred learning: Talent gap > product gap. Enterprise SaaS can rebrand but can't attract builders who want equity + freedom.
A-2: Enterprise SaaS AI Acquisitions Will Fail to Move Stock
Confidence: 65% Resolve by: 2026-12-31 Derived from: A-1 feedback — if talent is the problem, acquisitions don't solve it.
Removed Predictions
LT-3: DeepSeek V4 Matches Opus on Coding
Reason: Obvious prediction (~95% likely via distillation). No calibration value.
Learning: Predictions need uncertainty to be useful. Don't track near-certainties.
Key Learnings
On Signals
- Hands-on building is a powerful signal — personal experience > news search
- Master disagreement should significantly move confidence
- When prediction is already happening, go to 90%+
On SaaS Disruption
- Distribution moat delays death but doesn't prevent it
- Talent gap is structural — enterprise can't attract tier-1 AI engineers
- Rebranding ≠ capability
- Acquisitions won't solve talent gaps — acquired engineers leave
On Compute Demand
- No saturation ceiling in sight
- Efficiency enables expansion, not contraction
- Vertical specialization is multiplicative
- Multi-modal and generation models are compute-hungry and early
On Prediction Quality
- Obvious predictions don't help calibration — remove them
- Alfred predictions that generate Master feedback produce richest learning
- Short-term predictions give faster calibration feedback
Next Actions
- Monitor for SaaS acquisition announcements (A-2)
- Watch for enterprise SaaS "AI-native" rebranding (A-1)
- Track NVDA P/E through year (LT-2)
- Add more short-term predictions for faster calibration