The Moonshot
Building foundation models that discover the laws governing complex systems, from raw data alone.
The fundamental limitation of AI today.
Every major frontier lab is attacking the same meta-problem from different angles: machines that understand the world well enough to act in it.
World Labs and AMI Labs are teaching AI to perceive the physical world. Physical Intelligence and Figure AI are teaching AI to manipulate objects. OpenAI, Anthropic, and DeepSeek are pushing linguistic reasoning.
None of them are building machines that think in data.
Inside every hospital, factory, laboratory, bank, and research institution are the governing equations of complex systems — the rules that determine why things happen. Discovering those equations requires human experts. The process is slow, expensive, and limited by the number of experts who exist.
What if a machine could do what Newton, Kepler, and Darwin did — observe data, detect patterns, and discover the underlying laws — but in minutes instead of decades?
What exists today and why it falls short.
This is not better analytics. This is not smarter agents. This is a fundamental new capability that does not exist anywhere in AI today.
Large Language Models
01Convert numbers into words and reason linguistically. The statistical structure — distributions, correlations, temporal dynamics — is lost in translation.
Can describe data. Cannot think in data.
Traditional Statistical Tools
02R, SPSS, SAS, scikit-learn are powerful execution engines, but they execute what a human designs. They do not explore autonomously.
Require a human to formulate the hypothesis first.
Symbolic Regression
03PySR and Eureqa can discover equations but don't scale beyond ~10 variables. Real-world systems have hundreds or thousands.
Hours of computation for simple systems.
Business Intelligence Tools
04Visualize data. They do not reason about it, do not discover causality, and do not produce interpretable equations.
Show what happened. Cannot tell you why.
The frontier lab landscape
Where every major lab is focused — and the gap we fill.
Teaches AI to predict the physical world by learning abstract representations from video.
→ Latent-space predictionsBuilds AI that generates and reasons about 3D environments.
→ Spatial understandingBuilds AI that controls robots with precise physical manipulation.
→ Motor actionsBuilds AI that learns spatial reasoning from gameplay.
→ Navigation agentsPushing the boundaries of linguistic reasoning — machines that think in words.
→ Text & reasoningDiscovers governing equations from data. Human-readable, verifiable knowledge.
→ Mathematical equationsEvery other frontier lab produces capabilities — perception, action, generation.
Reasoning Labs produces knowledge.
How it works.
Two fundamental components: a Data Foundation Model that processes tabular data as native input, and a Symbolic Decoder that generates interpretable mathematical expressions.
Data Foundation Model
A neural architecture that processes tabular and time-series data as native input — not converted to text. Each column is encoded as an embedding that captures its distribution shape, relationships, temporal dynamics, and anomalies.
Trained with self-supervised learning analogous to masked language modeling but for data. Mask values and columns, predict them in latent space.
Symbolic Decoder
Generates mathematical expressions — not text, not embeddings, but actual equation trees. Searches for the simplest expression that explains the observed relationships.
Training signal is verification: does the discovered equation predict held-out data correctly? Reinforcement learning with natural, objective reward.
Why verification is our structural advantage
When our system discovers an equation, correctness is objective and automatic. The equation either predicts the held-out data or it does not. The R-squared is a number, not an opinion.
This means we can train with reinforcement learning at scale, without the bottleneck of human feedback. The data itself is the judge.
What it discovers, by domain
Real equations. Real interpretability. Real knowledge.
defect_rate = f(humidity × ΔT) when speed ∈ [2.3, 2.7]The engineer reads this and understands: condensation at that speed range causes micro-defects at the weld point. The machine discovered the physics.
response_prob = g(gene_expr × dose^0.5) / (1 + age/τ)Not a black-box prediction. A relationship a physician can understand, validate against biological mechanism, and publish.
default_risk = quadratic(LTV) when LTV > 0.80The bank's model assumes linearity, but the data shows quadratic behavior above 80% LTV. Underestimating risk by hundreds of millions.
observation - theory = h(λ, T) × correction_termDiscovers discrepancies between theory and observation following a specific functional form — pointing toward new phenomena.
The product funds the research.
AMI Labs raised one billion dollars with no product. General Intuition raised $133.7M with no revenue. We have Singularity — live, with users, generating the exact data we need to train our foundation model.
Product
Singularity — AI analytics platform. Live users making real queries against real data at $999/mo.
Data
Every query generates verified analytical reasoning traces: SQL executed, calculations performed, insights validated.
Model
Traces train the foundation model. The model improves the product. Revenue grows. More traces. The flywheel spins.
The data moat is unique and growing
No other organization has what Singularity accumulates: millions of real analytical reasoning traces across dozens of industries, tied to real datasets, with objective verification signals.
OpenAI has text. Google has search queries. Meta has social graphs. We have the reasoning patterns of professionals analyzing their own data.
From product to foundation model.
A phased approach where each stage funds the next and produces tangible, defensible intellectual property.
Accumulation
Singularity V4 operates as an AI analytics platform. Users upload data from any domain and ask questions in natural language. Every successful query generates a verified reasoning trace.
Cross-Domain Patterns
Meta-patterns emerge. The question 'why did this metric change?' follows the same analytical skeleton whether the metric is revenue, mortality rate, or defect rate. These patterns are domain-independent analytical primitives.
Data Foundation Model
Train a model that processes tabular data as native input — columns as tokens, distributions as embeddings. Fine-tune with RL using our verification signals.
Equation Discovery
Add the symbolic decoder that generates interpretable mathematical expressions. Train with RL where the reward is predictive accuracy on held-out data.
Newton observed an apple and discovered gravity. Kepler studied Tycho Brahe's astronomical tables and discovered planetary motion. Darwin cataloged species across continents and discovered natural selection.