Author

@NaveenGRao NaveenGRao

AI-compute founder posting first-principles takes on the physics of intelligence

Shares technical and macro commentary on AI compute hardware

trader score
-0.56
hit rate
43%
mean α
-0.53%
signals 14d
10

Grade = how their written analysis reads (A best). Trader score = how their last-20 timestamped calls performed vs SPY.

Their picks, scored

Across their last 7 scored bets: 43% hit rate, -0.53% mean alpha, trader score -0.56. Their last-14d mentions, direction-adjusted, have moved -4.1% since posting (mean over 3 mentions with price data).

Recent signals10receipts included
date (PT)tickerauthorsentwhat they saidsince thenreceipt
2026-10-15·@NaveenGRao·Argues that verification bottlenecks constrain autonomous-agent usefulness.·
2026-10-15TSLA@NaveenGRao+0.45Expresses strong enthusiasm for a Tesla Roadster order.+2.7%
2026-10-15GOOGL@NaveenGRao+0.62Positive assessment of Google's return to the technology frontier.-8.3%
2026-10-14·@NaveenGRao·States that the author's private company is engineering the described capability.·
2026-10-12·@NaveenGRao·Unconventional AI says it is already operating 12-week chip-development cycles without fab changes.·
2026-10-12NVDA@NaveenGRao+0.05Calculates that a 10T-parameter model needs far more memory and energy than a 5 J/token estimate.-6.8%
2026-10-11·@NaveenGRao·Argues AI remains far behind animals in energy-efficient real-time motor control.·
2026-10-11·@NaveenGRao·Estimates a 10T MoE model near 1000 joules per token rather than five.·
2026-10-10·@NaveenGRao·Argues software customization costs often outweigh savings and vendor-delivery costs should decline.·
2026-10-07·@NaveenGRao·Author proposes agents that exhaustively inspect software, write specifications, and reproduce applications.·

Grade is our human read-worthiness rating; trader score is a rolling 20-bet hit-rate/alpha composite — different things, often disagreeing. “Since then” is direction-unaware in the table; the summary line above adjusts for which way they leaned.