The All-In After Loss Trap

The All-In After Loss Trap | Generated by AI

Home 2026.07

Question: read the old essay + your added context — house loss (~1M CNY), lost the 380K job, chased LLM training, got a couple AI interviews, six months of grinding, landed a foreign bank overseas gig that was rough. You’re asking if the throughline — “keep making the same mistakes / things keep going sideways, partly macro, partly my own inconsistency” — is right.

Answer: Yeah, mostly right, and it’s worth taking seriously rather than just noting it and moving on.

Look at the actual pattern across both periods:

2020: burned through investor money, had to beg friends for cash, then clawed back to repay it through public housing fund withdrawal + freelance work + honesty. The lesson you took was “don’t owe anyone, do fair trades, low-cost iterate.”
2024-25ish: bought a house at the wrong time (lost ~1M), lost a well-paying job, then instead of stabilizing, went all-in on training foundation models — a field where you were competing against labs with 100x-1000x your compute and headcount. That’s not “unstable execution,” that’s a sizing error. GPT-2 760M on MI300X is a great learning exercise, but it was never going to be a business, and chasing it while cash-negative from the house loss compounds the risk instead of de-risking.

The pattern isn’t “I keep failing randomly.” It’s: you go all-in on high-variance bets right after taking a loss, instead of stabilizing first. 2017-2020 was startup capital lost → begging → repaying. 2023-2025 was house capital lost → job lost → jumping straight into the highest-variance thing available (pretraining) instead of taking the steady banking gig first and rebuilding from there. The six-month job search and the “煎熬” (grinding/suffering) at the bank was the tax for having sized the LLM bet too big relative to your runway.

Your own essay already has the fix written down, you just didn’t apply it the second time: “低成本试错” (low-cost trial and error), don’t borrow against a future you can’t guarantee, don’t scale before the smaller version proves out. Training a 760M model on rented compute is low-cost trial and error. Trying to make it your main bet after a 1M loss and a lost job is not — that’s un-hedged position sizing, same mistake class as taking 500K from an investor in 2017 before you’d proven the unit economics of Fun Live.

Macro is real — 2020 hiring froze, and 2024-25 AI hiring is brutal unless you’re already known (Karpathy/Brockman-tier or ex-FAANG). But macro doesn’t explain going all-in twice in a row on the highest-variance option right when your buffer was thinnest. That part’s on sizing discipline, not on the market.

If there’s a rule to actually keep this time: after any large loss, the next move is the smallest, most reversible one that stops the bleeding — not the biggest swing. The bank job was that move, even though it felt like 煎熬. The AI company consulting + agent dev you’re doing now (ww, iclaw, zz) is the right shape: cash flow first, model training as R&D on the side, not as the bet-the-house move.

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