The bet on what comes after LLMs
ARIA today runs on the best LLMs. Tomorrow, it runs on world models.
LLMs are excellent at language-substrate reasoning — code, math, text — but they can't predict the consequences of their actions or plan by search. That's exactly what reliable autonomous agents need. This is how we get there.
Roadmap phases
Phase 1
Now
ShippedShipped
Nine LLM agents run an overnight autonomous cycle and deliver a verified morning digest.
Guardrailed by construction
Every action is gated through guardrails. Tasks never “complete” without a verified_at timestamp — nothing ships unverified.
Where LLMs are strongest
ARIA lives in the language substrate — code, math, analysis — scaling expert knowledge globally. The “top doctor everywhere” analogy.
Phase 2
2026
On the roadmapNear-term · building now
Move agents from prompted behavior to objective-driven systems that simulate consequences before acting.
Objective-Driven Agent Design
Give each agent an explicit cost function and constraint set — a hardwired objective the system cannot violate by construction, not just a prompt that conditions behavior.
Consequence Simulation Layer
A “poor man’s world model.” Before any irreversible action — send email, modify data, deploy code — run a lightweight what-happens-if dry-run. Agents without consequence prediction are intrinsically unsafe.
Search-Based Planning
The Orchestrator searches over agent orderings for the sequence that produces the best verified outcome — planning by optimization, not next-token autoregression.
Phase 3
2027
On the roadmapMedium-term · as world models mature
Train neural world models of business dynamics — predict outcomes, generalize to new problems without retraining.
Business Dynamics World Model
Revenue, churn, and CAC are complex systems no handful of equations can capture. Train a world model on customer data to predict: change campaign variable X — what happens to churn in 30 days?
Zero-Shot Task Generalization
Tackle a new business problem without retraining, by planning through a learned model of how businesses work. A 17-year-old learns to drive in 20 hours — data efficiency over millions of demos.
Self-Supervised Pattern Discovery
JEPA-style. Watch business data streams — revenue, tickets, ads — without labels, learning representations of healthy vs unhealthy states. Predict in representation space, not raw pixels.
Phase 4
2028+
On the roadmapLong-term
A shared world model of how SaaS businesses work, learned across the customer network — with zero data exposure.
Federated Business Intelligence
The Tapestry model. Customers contribute parameter vectors and gradients, never raw data. A shared model gets smarter from every customer — your data never leaves your systems.
Why this matters
Most LLM wrappers have no post-LLM story.
LeCun expects the industry's “we need a paradigm change” realization to be obvious by early 2027. ARIA's roadmap says: we already know what comes after LLMs — and we're building toward it now, one verified phase at a time.
We don't just wrap today's models. We're building the system that outlasts them.