Quantum Capital
Fundamental AI research applied to trading and investment
The next OpenAI will be built in finance.
The Insight
Current finance AI is fragmented.
Many savants, but no unified mind.
Traditional quants build narrow, fragile models heavily reliant on human guidance. One algorithm for credit risk. Another for price prediction. Siloed intelligence that breaks under regime change.
Even DeepMind attempted trading AI and fizzled out. Market complexity proved too hard for conventional approaches. The problem isn't compute or data. It's architecture.
No one has built an AI that reasons about markets holistically.
The Opportunity
Algo Trading
$21B→$43B
by 2030
AI in Finance
$190B
by 2030
Algo Volume
70%
of trading
Firms Using AI
85%
by 2025
Polymarket
$9B
volume, 2025
Target Surface
$100T+
global assets
The value of an AGI that captures even a fraction of global market inefficiencies is enormous.
Why Now
The convergence that makes 2026 the moment.
01
AI Leaps
GPT-4, BloombergGPT (50B params), breakthroughs in reinforcement learning have redefined what's possible.
02
RL + LLMs
ICLR 2025: combining LLMs with RL trading agents significantly outperformed conventional models.
03
Compute Access
Mid-sized players can now train models that only nation-states could afford before.
04
Data Abundance
Decades of market data, alternative data sources, and synthetic simulation environments.
05
White Space
No one has claimed "OpenAI for Finance." First mover window is open.
The toolkit to attempt financial AGI is arriving. The question is who builds it.
The Approach
Multi-Modal Intelligence
Markets are influenced by numbers AND language: news, tweets, earnings calls, Fed statements. We train hybrid models combining numeric time-series with NLP.
A trading agent that reads a Fed statement AND adjusts strategy accordingly.
Reinforcement Learning in Market Simulators
Deep RL trains agents by "learning through trading" in simulated environments for equities, FX, crypto, and prediction markets. Agents self-play and discover strategies.
AlphaGo for markets. Stochastic and adversarial elements for robustness.
Large-Scale Foundation Models
Finance GPT variants trained on SEC filings, news, social sentiment. Models that digest information AND generate hypotheses with natural language interfaces.
Explainable agent reasoning. Not a black box.
Continuous Adaptation
Unlike static algorithms, our AI continuously learns from new data. Detects performance drift and autonomously retrains.
An AI that updates itself like an evolving organism.
Founding Partners
Michael Gonzales
Shawn Henry
Zan Shaikh
Proven pattern recognition across categories.
Exit
FitTea
Partnership
Lamborghini
Partnership
UFC
Partnership
Kardashians
The same pattern recognition that built category-defining consumer brands now applied to the largest market opportunity in AI.
Advisory network from top AI research labs and quantitative hedge funds.
The Invitation
We're selecting partners.
Not pitching.
This isn't a fundraise deck. This is an invitation to participate in building the intelligence layer for global finance. We're looking for partners who understand long-term research bets.
The opportunity exists whether you participate or not.
Request the Full Deck
Get MaterialsWhitepaper. Technical appendix. Partnership materials.