what are all different areas of ai people are working on
the money that makes everything else possible. shapes what gets built and what doesn’t.
- venture capital: a16z, Sequoia, Khosla, Benchmark, general catalyst
- corporate bets: Microsoft ($13B in OpenAI), Google, Amazon, Apple
- sovereign wealth: UAE, Saudi Arabia, Singapore — nations buying into the future
- Wall Street: Nvidia stock, AI ETFs, analyst coverage, public market bets
- government funding: DARPA, NSF, national AI initiatives
- startup ecosystem: YCombinator, accelerators, seed rounds
the physical world that keeps AI running. often invisible, always essential.
- energy: data centre power demands, nuclear revival, solar farms for compute
- water: cooling infrastructure, water consumption of large training runs
- real estate: hyperscale data centre construction, land acquisition
- annotation manpower: the human labellers behind every model — Scale AI, Remotasks
- RLHF workforce: contractors in Kenya, Philippines, India doing feedback work
- rare earth & supply chain: minerals for chips, geopolitics of hardware
building the capability and trying to understand what we’ve built.
- foundation model labs: OpenAI, Anthropic, Google DeepMind, Meta AI, xAI, Mistral
- hardware & chips: Nvidia, AMD, TSMC, Google TPUs, custom silicon
- cloud infrastructure: AWS, Azure, Google Cloud, CoreWeave
- data: datasets, labelling, synthetic data — Scale AI, Appen
- tooling & frameworks: PyTorch, JAX, Hugging Face, vLLM, LangChain
- open source models: Llama, Mistral, community ecosystem
- interpretability: mechanistic understanding of what’s inside models
- alignment & safety: MIRI, ARC, Redwood Research, DeepMind safety
- benchmarking & evaluation: how do we even measure capability
- ML theory: the mathematics underneath all of it
- science communication: Karpathy, newsletters, journalists making it legible
the layer between labs and end users. building products on top of models.
- AI-native consumer apps: Perplexity, Midjourney, Character.ai, ElevenLabs
- coding tools: Cursor, GitHub Copilot, Replit
- enterprise AI: Microsoft Copilot, Salesforce, ServiceNow
- vertical SaaS: Harvey (law), Abridge (medicine), Glean (enterprise search)
- agent frameworks: autonomous pipelines, workflow automation
- API layer: the middleware between labs and builders
- edge AI compute: running models on-device, not in the cloud. Apple Neural Engine, Qualcomm AI, Samsung, Rabbit r1, Frame glasses. lower latency, privacy-preserving, works offline. a big and underrated frontier.
every domain of human work, being transformed. this is the granular tracker.
- software engineering
- creative writing & journalism
- visual art & design
- music & audio
- film & video
- medicine: diagnosis, drug discovery, genomics, radiology
- mathematics: theorem proving, research assistance
- science: physics, chemistry, biology, materials
- robotics & physical AI
- autonomous driving
- space exploration
- education
- law
- finance & trading
- agriculture
- manufacturing
- language & translation
- defence & cyber war: AI-guided weapons, autonomous drones, cyber offence & defence, battlefield intelligence, Palantir, DARPA, nation-state AI arms race
- gaming & interactive media: NPC intelligence, procedural worlds, game design co-pilots, AI dungeon masters, real-time character behaviour, Inworld AI, Ubisoft Neo NPC
not against it — trying to shape how it develops and who it serves.
- national policy: US executive orders, China AI regulations, UK approach
- supranational: EU AI Act, UN AI governance bodies
- standards bodies: NIST, ISO AI standards
- policy think tanks: CSET, GovAI, Centre for AI Safety
- international coordination: AI Safety Summits, Bletchley Declaration
- corporate governance: internal ethics boards, responsible AI teams
people who want to slow it down or stop specific uses. legitimate concerns.
- labour & unions: writers, actors, illustrators — SAG-AFTRA, WGA strikes
- artists & copyright: lawsuits, visual artists, Getty Images
- religious & ethical voices: Pope Leo, faith communities
- academic critics: Timnit Gebru, Gary Marcus, Emily Bender
- pause & slowdown movements: Future of Life Institute letter
- anti-surveillance: facial recognition bans, biometric data fights
- environmental: AI energy and water consumption concerns
not what AI does — what AI does to us. the ripple effects on how we live.
- psychology: attention, cognition, dependency, the outsourcing of thinking
- identity: what does it mean to be human when machines can do what we do
- relationships: AI companions, loneliness, parasocial bonds with models
- work & meaning: if AI does the work, what do people do with their days
- culture: who makes art, who owns stories, what is authenticity now
- education & childhood: kids growing up with AI tutors. what does that produce
- inequality: AI amplifying the gap between those who use it well and those who don’t
- political: deepfakes, information warfare, trust in institutions eroding
- spiritual: is this a tool or something more. where does consciousness fit