People, tools, and capital we track — shortlist, not a phone book.

CEO of OpenAI. Product cadence, distribution, and capital narrative for the lab that still sets consumer AI defaults.

Controls the SpaceXAI / X / Grok stack. Real-time distribution plus training cluster ambition in one operator.

Former OpenAI CTO; CEO of Thinking Machines Lab. Customization and steerable systems over pure scale theater.

Ex-Tesla AI / OpenAI. Eureka Labs and public teaching that still train how builders actually learn deep learning.

OpenAI co-founder; now SSI. Singular bet on safe superintelligence — research taste with maximal stakes.

CEO of Anthropic. Safety-first lab framing, Claude product line, and a clear alternative power center to OpenAI.

CEO of Google DeepMind. Research spine behind Gemini and the long game on science-grade AI systems.

CEO of NVIDIA. Owns the scarce layer — accelerators, networking, and CUDA lock-in every frontier lab rents.

Author of Situational Awareness. Timeline, power, and capital framing that still structures serious AGI debate.

Founder of Scale AI. Data, eval, and labeling infrastructure that turned into strategic leverage across labs and defense.

Founder of DeepSeek. Quant-funded open weights that repriced assumptions about frontier cost curves.

Founder of Midjourney. Small team, strong aesthetic taste, image defaults set outside the big-lab PR cycle.

PSPDFKit founder turned agentic coding operator. Practical signal on what multi-agent dev workflows actually survive contact with shipping.

OpenAI’s consumer and work surface for the GPT line. Default interface habit for non-specialists — distribution as moat.

Anthropic’s model family. Strong long-context and coding posture; preferred stack for a lot of serious agent work.

SpaceXAI’s model line, wired into X. Real-time retrieval bias and a different product culture than the SF lab consensus.

Google DeepMind’s frontier family. Multimodal + Search/Workspace distribution — infrastructure few others can match.

Answer engine with citations as the product. Search replacement wedge; model choice secondary to workflow lock-in.

Meta’s open-weight line. Sets the baseline for self-host, fine-tune, and “good enough offline” deployments.

Open and API models that punch above spend. Reasoning and coding SKUs that force Western labs to justify price.

Frontier lab behind ChatGPT and the GPT API. Still the reference customer acquisition engine for generative AI.

Claude lab. Enterprise and developer trust narrative; constitutional/safety brand as commercial differentiator.

Formerly xAI; now branded under the SpaceX orbit. Grok, large training clusters, and tight coupling to X distribution.

Google’s consolidated research + product AI org. Gemini, infra, and the academic talent density of the old DeepMind core.

Picks and shovels with pricing power. GPUs, interconnect, software stack — the bottleneck every trainer queues on.

Open-weight counterweight via Llama. Social distribution plus a deliberate strategy to commoditize the model layer.

OpenAI’s primary cloud and distribution partner. Copilot surface area across Office, Windows, and GitHub.

Data engine and eval infrastructure for frontier training and government work. Bottleneck vendor, not a chat app.

Hangzhou lab funded by quant profits. Open releases that compressed the perceived cost of competitive reasoning models.

Murati’s post-OpenAI lab. Heavy seed capital; thesis on controllable, customizable systems rather than one global chatbot.

Search-native AI company. Monetizes answers and research workflow, not raw token dumps.

Default hub for weights, datasets, and open ML plumbing. Where open models actually get distributed and debated.

AI-native creative suite for image and video. Creator-facing production loop — camera control and multi-model generation in one surface.

AI-first code editor. Became the default agentic IDE habit for a large slice of serious builders.

Google’s source-grounded research notebook. Strong when the job is “reason only over these docs,” including audio overviews.

Image generation with a distinct aesthetic lane. Proof a focused product can lead without hyperscaler theatrics.

Speech synthesis, cloning, and dubbing. Infrastructure for voice agents and media pipelines that need believable audio.

Swiss public LLM effort (ETH/EPFL orbit). Sovereignty and multilingual fit — relevant if you care about CH-local stack choices.

Community wiki for models, labs, and AI landscape notes. Fast orientation layer when the map keeps moving.

Batch machine for early AI-native startups. Still the densest network effect at pre-seed/seed for technical founders.

Large multi-stage firm with loud AI infra and apps thesis. Capital plus narrative — shapes what gets permission to exist.

Classic multi-stage power. Deep history in defining platforms; still a reference check on serious AI rounds.

Thiel-orbit firm. Concentrated bets, contrarian posture, comfortable with hard-tech and frontier ambition.

Growth investor with outsized OpenAI and internet-software exposure. Quiet compounder behind several defining rounds.

Aschenbrenner on AGI timelines, industrial policy, and power. Still the long memo serious people argue with.

Asimov, 1956. Entropy, computation, and the end state of asking machines for answers — useful myth for the current buildout.

Kokotajlo’s scenario sketch of near-term AI. Reference point for how forecasting aged once the year arrived.