AI Companies by Organizational Structure

Today's AI company structures fall into four broad archetypes. Each archetype
differs in how it mixes research, engineering, product, and distribution, and in
the moats it tries to build. Below is a high‑level map of those categories.

  • Foundation‑model leaders such as OpenAI, Anthropic, DeepMind, and
    DeepSeek are led by founders who treat models like physics systems and
    set ambitions decades out. OpenAI uses a capital‑ and startup‑style sprint,
    while Anthropic, DeepMind remain research‑led.

They shape the global foundation‑model road‑map; later companies cluster
around their ecosystems. Their org charts braid research, engineering, and
product, with researcher and engineer density forming the deepest moat.

  • Product‑first players like Perplexity and Cursor placed early, accurate bets
    on product form. Their headcount skews toward design, engineering, and
    distribution, though they keep talented researchers in‑house, even if model
    R&D has yet to pay off.

They compete with category 1 but rely on upstream model upgrades outside
their control, leaving them reactive. Meanwhile category 1 now offers a
$20/month platform bundle, putting Perplexity‑style firms on the defensive in
distribution.

  • Vertical (or some horizontal) 2B companies—Spur, Extend, and others—
    anchor on domain knowledge in healthcare, finance, law, etc. Headcount
    tilts toward product and sales; the product may be an LLM wrapper, but
    lasting distribution, not wrapping, is the key.

Their moat is founders’ domain insight plus launch, iteration, and distribution
speed. Big‑tech rivalry is light because giants ignore smaller niches. The
mission is to validate demand quickly and ship fast. Researchers are optional,
useful mainly for road‑map foresight.

  • 2B model‑innovation companies are rarer. Their core team is researchers
    and engineers who deliver genuine breakthroughs—for example, enterprise
    fine‑tuning shops. They build atop open‑source backbones like Llama,
    DeepSeek, or soon GPT‑4.1 to serve clients.

The risk is betting on the wrong branch: if an incumbent lifts the bottleneck the
niche disappears. The challenge is packaging tech into products that existing
2B firms can adopt; when those firms already ship, displacement is hard, but a
right bet yields a moat.

In the end distribution is both the hardest and most vital piece. Products,
models, and papers are only means to enable durable distribution. Even a
brilliant product exists to reach users more easily; if distribution stalls, the
company soon falters.