Core theses
What we believe, on what evidence, and what would change our mind.
7 essays, each in the same structure: the claim, the mechanism, the evidence, the strongest counterargument, what would change our mind, what we do as a result, and the open questions. Judgments change with evidence, and the changes are logged. Updated 2026-09-25.
01
Value lands in the application layer and at the open-source chokepoints; the model layer cannot hold it
This cycle's AI value lands in two places: ARR in the application layer, and the open-source chokepoints every model passes through. The model layer cannot hold it: large models have no network effects and no switching costs, so pricing power dissolves as capability converges. The bubble sits in the middle layer, which carries the most debt and lives on refinancing.
7 pieces of evidence · 4 counterarguments · 4 falsification tests →
02
The bubble sits in the middle layer that must keep refinancing: the FutureX seven-dimension bubble scorecard
The AI bubble sits in the middle layer that must keep refinancing: compute leasing on borrowed money, circular financing, GPU-backed debt. FutureX Capital has scored seven dimensions daily since March 2026; the August reading lit six, and liquidity triggered on August 17. The scorecard answers one question: which layer cannot be bought at the consensus price.
7 pieces of evidence · 3 counterarguments · 4 falsification tests →
03
Open source breaks the deadlock: the cost curve has a new author
The dividing line of this AI cycle is the cost curve, and open source pushes it down. A closed model's moat is capital expenditure that must be refinanced; an open model's moat is a living community that needs none. The curve's author has changed from US closed labs to Chinese open models; value moves to applications and open-source chokepoints.
7 pieces of evidence · 4 counterarguments · 4 falsification tests →
04
Verifiability times feedback cycle decides the order in which AI remakes industries
The order in which AI remakes industries equals verifiability times feedback cycle. Where a result checks itself in seconds, AI moves first; where a human stays in the loop and feedback takes months, it moves last. Each sector gets a 12-to-18-month window after capability crosses its threshold and before enterprise budgets arrive.
7 pieces of evidence · 4 counterarguments · 4 falsification tests →
05
Four moats, three screens, and outcomes-as-a-service pricing for AI application companies
Profit in this AI cycle lands in the application layer; the model layer cannot hold it. Application companies have four moats: taste, extreme efficiency in one workflow, several models combined, and process as product. Three screens decide whether they earn: distribution, retention, and gross margin that climbs as token prices fall. Outcomes-as-a-service is replacing seat subscriptions.
7 pieces of evidence · 4 counterarguments · 4 falsification tests →
06
US and China in AI: one cycle, two positions
The US and China sit at two positions in one AI cycle: the US late in its mania phase, China early to mid. The gap is about 14x in capital, about 2.7% in model capability and about one third in valuation. Founders who bill in dollars are closing that discount themselves.
7 pieces of evidence · 4 counterarguments · 4 falsification tests →
07
Embodied AI: 2026 is the hardware year, and 80 points equals zero
2026 is the hardware year for humanoid robots; the intelligence year has not arrived. A robot that completes eight tenths of a task has completed nothing, so 80 points equals zero. FutureX Capital (天际资本) backs dexterous hands and data and stays out of the full-body arms race.
7 pieces of evidence · 4 counterarguments · 4 falsification tests →