Foundational technology for more efficient intelligence

DotrixAI is an independent AI research lab developing efficient, licensable AI technologies.

Explore research CIR research program
Our purpose

Expand the amount of useful intelligence available to humanity.

Intelligence accelerates progress. Making intelligence more efficient and widely accessible can accelerate the progress of civilization itself.

Thesis

Progress is limited by the intelligence available to solve problems.

01 · Intelligence drives progress

Science, engineering and invention move at the pace of available reasoning. More usable intelligence means faster discovery and understanding.

02 · AI adds cognitive capacity

Artificial intelligence is a form of cognitive capacity that can scale. It can work on problems alongside people.

03 · Cost concentrates access

When intelligence is very expensive to train and run, access concentrates. Only those with vast capital and compute can use it fully.

04 · Efficiency widens access

DotrixAI researches technology that makes intelligence cheaper to build and operate. For us, efficiency is a mechanism for access.

The reinforcing cycle

  1. More accessible intelligence
  2. Faster discovery
  3. Better technology
  4. Greater cognitive capability
  5. Faster progress

CIR

Searching for lower cost to capability architectures. CIR asks what it really costs to reach a given capability.

  • StatusActive research
  • TypeResearch program
  • BaselineStrong Transformers
Explore CIR

Capability per unit of economic cost

The measure behind every DotrixAI program. Progress should not depend only on larger models and clusters.

  • TopicResearch philosophy
  • Applies toEvery program
Read the philosophy

Evidence before claims

Major technical claims should be open to independent evaluation. Our publication approach explains how.

  • StatusResearch in progress
  • PapersNone published yet
Our evidence standard
Research scope

We research the technology beneath AI models.

These are directions, not finished products. Only some are active programs today.

  • Architectures

    Neural architectures that reach capability with less computation.

    Active through CIR

  • Learning systems

    How models learn from data, and how efficiently they do it.

    Active through CIR

  • Memory and retrieval

    Mechanisms for storing, recalling and using information at low cost.

    Research direction

  • Training and inference systems

    Systems that lower the real cost of training and serving models.

    Partly studied in CIR

  • Runtimes and compilers

    Software that maps models onto hardware with less waste.

    Research direction

  • AI infrastructure

    Compute and distributed systems for more efficient intelligence.

    Long term direction

  • Safety

    Reliability, controllability and secure deployment as capability grows.

    Principle and direction
The measure

CapabilityEconomic cost

Research philosophy

We judge intelligence technology by what useful capability really costs.

Progress is often assumed to require larger models, more GPUs, more data and bigger clusters. We look for genuine efficiency gains instead. Cost includes computation, energy, memory, infrastructure and capital.

  • Rigorous baselines

    Compare against strong, fairly tuned alternatives

  • Adversarial evaluation

    Try hard to break our own results

  • First principles research

    Derive new mechanisms when existing ones fall short

  • Negative results kept

    A failed idea still narrows the search

  • Hardware aware analysis

    Measure cost on real machines, not only on paper
Long term vision

Intelligence should extend human capability.

Humans created artificial intelligence. Over time, it can support how people think, research, learn and design.

We see AI as a possible extension of human cognitive capacity. It need not develop as a separate, disconnected system.

Biological cognition does not have to be the ceiling of human intellectual capability. Effective capability can combine both forms of intelligence.

Our part is foundational technology that makes this extension efficient, safe and broadly available.

Human intelligence + machine intelligence

Technology direction

Research designed to become useful technology.

Successful DotrixAI research may become proprietary technology that others can license.

Possible future users include AI labs, model developers, enterprises and infrastructure providers.

DotrixAI has no licensees today. This describes our direction, not current offerings.

See the technology stack
  1. ResearchToday
  2. Architectures and learning systemsToday
  3. Software, runtimes and compilersDirection
  4. Licensable AI technologyGoal
  5. Compute infrastructureDirection
  6. AI developers and laboratoriesFuture users
  7. Models and intelligent systemsOutcome
Publication and evidence

Claims should be open to independent evaluation.

DotrixAI intends to publish rigorous evidence for major technical claims. Selected commercially sensitive implementation details may stay protected.

Public research materials will be released as programs mature.

How we publish
Long term direction

A loop that funds its own research.

Research produces technology. Licensing technology funds more research. Each turn should make intelligence more efficient and more widely usable.

  1. Research
  2. Technology
  3. Licensing
  4. More research
  5. More efficient intelligence
  6. Broader cognitive capacity

Research collaboration and future technology partnerships.

We welcome conversations with researchers and organizations working on efficient AI.

Contact DotrixAI