Human judgment × machine intelligence
DTRM Research Lab tests a precise proposition: in uncertain, open systems, computational power creates more value when human judgment retains control of the objective, constraints and final decision.
Narrative protocol
Five connected chapters explain the hypothesis without claiming victories the research has not yet demonstrated.
DeepBlue, AlphaGo and AlphaStar demonstrated extraordinary search and optimization inside bounded environments. Their victories begin after humans have already specified what the game is and what winning means.
The machine solves the game. The human defines it.
A normalized comparison between human-controlled selection, machine recommendations, AI-managed instruments and the S&P 500 benchmark.
| Rank | Movement | Asset | Adjusted | Trump score | Signal |
|---|---|---|---|---|---|
| 01 | WMWM | Adjusted score: 92.6 | Trump Score: 95.9 | strong | |
| 02 | TMOTMO | Adjusted score: 89.4 | Trump Score: 94.1 | strong | |
| 03 | JCIJCI | Adjusted score: 88.9 | Trump Score: 95.3 | strong | |
| 04 | MCKMCK | Adjusted score: 88.2 | Trump Score: 92.4 | strong | |
| 05 | MRSHMRSH | Adjusted score: 86.7 | Trump Score: 89.5 | strong | |
| 06 | TTTT | Adjusted score: 85.5 | Trump Score: 93.0 | strong | |
| 07 | BKNGBKNG | Adjusted score: 85.1 | Trump Score: 91.8 | strong | |
| 08 | PMPhilip Morris International Inc. | Adjusted score: 84.9 | Trump Score: 90.0 | strong | |
| 09 | NVDANVIDIA Corporation | Adjusted score: 84.7 | Trump Score: 99.4 | watch | |
| 10 | ADPADP | Adjusted score: 83.1 | Trump Score: 87.7 | watch | |
| 11 | DHRDHR | Adjusted score: 82.7 | Trump Score: 93.6 | watch | |
| 12 | MOMO | Adjusted score: 81.5 | Trump Score: 90.6 | watch | |
| 13 | HCAHCA | Adjusted score: 80.7 | Trump Score: 91.2 | watch | |
| 14 | MCDMcDonald's Corporation | Adjusted score: 78.9 | Trump Score: 81.3 | watch | |
| 15 | VZVZ | Adjusted score: 77.9 | Trump Score: 84.2 | watch | |
| 16 | DUKDUK | Adjusted score: 77.8 | Trump Score: 78.9 | watch | |
| 17 | AMTAMT | Adjusted score: 77.7 | Trump Score: 87.1 | watch | |
| 18 | COFCOF | Adjusted score: 76.0 | Trump Score: 79.5 | defensive | |
| 19 | AAPLApple Inc. | Adjusted score: 76.0 | Trump Score: 83.0 | defensive | |
| 20 | MARMAR | Adjusted score: 75.7 | Trump Score: 82.4 | defensive | |
| 21 | FTNTFTNT | Adjusted score: 75.6 | Trump Score: 85.9 | defensive | |
| 22 | PHPH | Adjusted score: 75.1 | Trump Score: 77.4 | defensive | |
| 23 | TMUST-Mobile US, Inc. | Adjusted score: 74.8 | Trump Score: 84.8 | defensive | |
| 24 | SBUXSBUX | Adjusted score: 74.6 | Trump Score: 78.3 | defensive | |
| 25 | NEENEE | Adjusted score: 73.6 | Trump Score: 74.2 | defensive |
The rebalance date, 60-day cadence, eleven-asset output and 100% allocation are controlled. The selection mechanism is the experimental variable.
The ranking narrows the 174-asset universe to a Top 25 candidate squad. Human judgment then combines Trump Score, adjusted score and market context to define the final eleven. Higher adjusted scores generally inform the offensive, higher-weight positions.
ChatGPT selects eleven assets directly from the validated 174-asset universe and independently determines their proportional weights. The private advisory protocol is repeated every 60 days on the same date as DTRM, with no user memory or retrieval context.
The operational prompt remains in the private agent. This public snapshot exposes only the approved protocol identity and integrity digest.
SHA-256 · a853eb5d6ccd881d…One private daily batch curates the same FMP evidence set used by the model. DTRM agent claims describe possible impact on published positions; they never alter the locked 60-day alignments.
27 Aug, 13:00 UTCFinancial Modeling Prep (opens in a new tab)
Scrutiny ahead of Marvell’s Q3 earnings could sway sentiment around AI chip demand and networking silicon, which may spill over to NVDA and AVGO positions.
27 Aug, 12:16 UTCFinancial Modeling Prep (opens in a new tab)
Okta’s reported strong financial performance tied to rising AI security demand may support a constructive read-through for enterprise AI adoption that could be modestly supportive for MSFT and GOOGL.
27 Aug, 17:00 UTCFinancial Modeling Prep (opens in a new tab)
An earnings preview highlighting IREN’s AI cloud performance could signal ongoing AI infrastructure demand, which may be supportive for AMZN’s cloud exposure and AVGO’s AI-related hardware ecosystem.
27 Aug, 11:04 UTCFinancial Modeling Prep (opens in a new tab)
Veeva’s strong earnings and AI-driven growth narrative may reinforce confidence in enterprise software spend, which could be mildly supportive for MSFT and GOOGL.
27 Aug, 08:13 UTCFinancial Modeling Prep (opens in a new tab)
The article’s framing of NVIDIA’s AI dominance and strong growth outlook may directly support bullish sentiment toward the NVDA position.
27 Aug, 08:06 UTCFinancial Modeling Prep (opens in a new tab)
Salesforce’s strong earnings and AI narrative may be read as continued enterprise AI investment, which could be supportive for MSFT and GOOGL as key AI/platform vendors.
27 Aug, 03:37 UTCyoutube.com (opens in a new tab)
Commentary that Nvidia’s results suggest the AI investment cycle has room to run and could spark a catch-up trade may be supportive for NVDA and for other mega-cap AI positions including MSFT, GOOGL, AMZN, and META.
27 Aug, 05:30 UTCwsj.com (opens in a new tab)
Nvidia’s aggressive supply-chain spending strategy could strengthen its ability to meet AI demand (supportive for NVDA) but also introduces execution and capital-allocation risk that could temper near-term sentiment.
LLM-assisted research annotation · Probabilistic claims · Not financial advice
Checked-in fallback snapshot for builds without a backend publication bundle. Set DTRM_PUBLICATION_BUNDLE to consume a verified daily_publication output. Not financial advice.
Human checkpoint
The Resistance was never meant to be a solo project. It is not a movement against artificial intelligence. It is a refusal to stop thinking simply because our machines have become extraordinarily capable.
The people leading today's AI revolution deserve admiration for what they have built. But even extraordinary products can become dangerous ideas when we fall so deeply in love with them that we confuse automation with progress and remove humanity from the decision.
Humanity should not fear machines that think, but men and women who stop doing it.
The future we want gives people better machines, better evidence and a wider search space — while keeping purpose, judgment and responsibility human. The machine can search. The human must still define the game.
We should never build a future that looks back at its own ruins and can only say:“People lived here.”