Uploaded July 2026 | Updated September 2026, 3 weeks ago
GPT 5.5 and Opus 4.8 landed within three points on the same finance benchmark while failing in opposite directions. GPT got the arithmetic right. Opus got the methodology right. One leaderboard number flattened both failures into a single noisy sample, exactly what happens when benchmarks reward one scaffold and vendors sell the data used to climb the tests they designed.
The scarce asset is no longer another isolated answer. It is process data: the reasoning trace, sequence of decisions, state changes, failures, recoveries, and verified outcomes that turn general competence into real expertise. Static datasets depreciate as models improve, so the durable moat is a live pipeline into real work plus the infrastructure to retrain when the base model changes.
Speaker info:
- https://x.com/SeanZCai
- linkedin.com/in/sean-z-cai
- seancai.com/philosophy/state_of_data_jan_2026
Timestamps:
0:00 - The data market nobody sees
1:15 - Data as industrial fuel
2:31 - Type one and type two data
3:23 - Compute, data, and talent
4:24 - State data versus process data
5:54 - The three axes of verifiability
8:14 - When benchmarks become snake oil
10:08 - Three finance benchmark tests
12:04 - Predicting the next AI domain
13:21 - The robotics counterexample
14:22 - Where the economic value lives
16:02 - Why data companies move enterprise
17:09 - The durable moat
GPT 5.5 and Opus 4.8 landed within three points on the same finance benchmark while failing in opposite directions. GPT got the arithmetic right. Opus got the methodology right. One leaderboard number flattened both failures into a single noisy sample, exactly what happens when benchmarks reward one scaffold and vendors sell the data used to climb the tests they designed.
The scarce asset is no longer another isolated answer. It is process data: the reasoning trace, sequence of decisions, state changes, failures, recoveries, and verified outcomes that turn general competence into real expertise. Static datasets depreciate as models improve, so the durable moat is a live pipeline into real work plus the infrastructure to retrain when the base model changes.
Speaker info:
- https://x.com/SeanZCai
- linkedin.com/in/sean-z-cai
- seancai.com/philosophy/state_of_data_jan_2026
Timestamps:
0:00 - The data market nobody sees
1:15 - Data as industrial fuel
2:31 - Type one and type two data
3:23 - Compute, data, and talent
4:24 - State data versus process data
5:54 - The three axes of verifiability
8:14 - When benchmarks become snake oil
10:08 - Three finance benchmark tests
12:04 - Predicting the next AI domain
13:21 - The robotics counterexample
14:22 - Where the economic value lives
16:02 - Why data companies move enterprise
17:09 - The durable moat
