Algorithm over ornament
We are interested in structural advances, not cosmetic optimization. The target is a more powerful and more efficient architectural primitive.
Founder-led AI infrastructure research company
MojoLynx is building the next transformer paradigm for AI infrastructure.
We are a focused research company pursuing algorithmic breakthroughs that challenge the assumptions of today's transformer stack, from training efficiency to systems-level deployment behavior.
- StageDeep research
- ModelNew transformer algorithms
- LeadershipCEO-led, founder-built
Core thesis
AI infrastructure does not need incremental tuning. It needs a new algorithmic shape.
MojoLynx is investigating transformer designs that can reset tradeoffs around compute efficiency, scaling behavior, and the way intelligence is expressed across hardware and software boundaries.
Infrastructure-native thinking
Model design is inseparable from runtime behavior, memory topology, and serving economics. Our work treats these constraints as first-class design inputs.
Long-horizon R&D
MojoLynx is intentionally designed with room for accumulation: ideas, experiments, benchmarks, papers, prototypes, and future productization.
Research surface
Current focus areas for the company homepage, with room to grow as the research matures.
Next-generation transformer architectures
Researching architectures intended to move beyond today's default assumptions about attention, representation, efficiency, and scaling.
Training and inference efficiency
Exploring how algorithmic redesign can reshape throughput, memory pressure, and deployment economics across future AI systems.
Infrastructure implications
Mapping how model architecture decisions propagate into runtimes, orchestration, hardware strategy, and product infrastructure.
Leadership
MojoLynx is led by Tao Feng, CEO and currently the company's sole operator.
The company combines deep AI interest with an intentionally interdisciplinary perspective. That breadth informs how problems are framed, how assumptions are challenged, and how new system-level ideas emerge.
Today MojoLynx operates as a highly concentrated research vehicle. The company homepage is built to support future layers: publications, benchmark results, technical essays, product direction, recruiting, and partnership material.
"The goal is not to decorate the existing stack. The goal is to discover a stronger one."
tao.feng@mojolynx.comOpen space
This site now has intentional room for future company layers.
As MojoLynx evolves, these modules can expand into product narratives, research notes, milestones, technical artifacts, team growth, and investor or partner materials.
Research updates
Benchmarks, papers, and experiments.
Product direction
What becomes software, platform, or tooling.
Partnerships
Future collaborations with aligned operators.
Company narrative
Hiring, roadmap, and strategic context.