AI Agents
The next wave of AI is not chatbots — it is agents. AI agents can use tools (browsers, code runners, APIs, databases), plan multi-step tasks, and execute them autonomously. Give an agent a goal like 'research competitors and draft a report' and it works through the steps without you prompting each one.
- Agents can use tools, plan tasks, and execute multi-step workflows
- They use browsers, code runners, APIs, and databases autonomously
- Claude, GPT-4, and Gemini all support tool use / function calling
Did you know? Devin, a software engineering AI agent, autonomously resolved real GitHub issues end-to-end — writing code, running tests, and submitting PRs without human intervention.
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Multi-Agent Systems
Rather than one AI trying to do everything, multi-agent systems assign specialised agents to different parts of a task: a planner, a researcher, a coder, a reviewer. The agents pass work between each other. This mirrors how human teams work and produces dramatically better results on complex tasks.
- Multi-agent = different AIs with different roles working together
- A planner breaks down the task; a researcher gathers info; a coder builds
- Reduces hallucinations by having one agent verify another
Did you know? Claude Code, Anthropic's engineering tool, can spawn sub-agents for research, writing, and building — all orchestrated by a master agent in a single conversation.
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AGI — What It Means
Artificial General Intelligence (AGI) means AI that can do any intellectual task a human can do. We are not there yet. The debate is over how far away it is: months? years? decades? Most researchers agree that current AI is narrow and domain-specific, not truly general — but the pace of progress makes confident predictions hard.
- AGI = AI that matches human ability across ALL intellectual tasks
- We do not have AGI yet — current AI is narrow and specialised
- Expert estimates range from 5 years to never — the honest answer is: uncertain
Did you know? Sam Altman (OpenAI CEO) has said AGI could arrive 'in a few years'. Many researchers strongly disagree. The range of expert predictions has never been wider.
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AI Infrastructure
AI runs on specialised chips — GPUs (Nvidia) and TPUs (Google). The Nvidia A100 and H100 GPUs power most of the frontier model training and inference. Demand for these chips is so high that delivery times stretched to a year at peak. Whoever controls the chips controls the pace of AI.
- Nvidia's H100 GPUs are the primary hardware for AI training
- TPUs are Google's custom AI chips used for both training and Gemini inference
- Chip access is the primary bottleneck in AI development in 2026
Did you know? Nvidia's market capitalisation passed $3 trillion in 2024 — more than any company except Apple and Microsoft — driven almost entirely by AI chip demand.
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Emerging Model Capabilities
The next wave of model capabilities includes: better long-horizon planning, real-time audio/video understanding, memory across sessions, and reliably following complex multi-step instructions. Each of these was a research problem two years ago; now they are shipping into products.
- Long-horizon planning and reliable multi-step execution are arriving now
- Real-time audio and video understanding is shipping in frontier models
- Persistent memory across sessions is now in paid plans of major products
Did you know? GPT-4o's live voice mode processes speech in 300ms — fast enough that it can interrupt, laugh, and react mid-sentence in real time.
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Open Source vs Closed
The frontier AI race is split: OpenAI, Anthropic, and Google keep their best models closed. Meta, Mistral, and others release weights openly. Open models are now within 10-20% of closed models on benchmarks and are rapidly closing the gap. The outcome of this race determines who controls AI infrastructure.
- Closed models (OpenAI, Anthropic, Google) keep weights proprietary
- Open models (Meta Llama, Mistral) release weights anyone can download
- Open models are closing the capability gap — now within ~10-20% on benchmarks
Did you know? Llama 3 70B, released free by Meta in 2024, scored higher than GPT-3.5 on most benchmarks — and GPT-3.5 cost OpenAI billions to develop.
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AI Regulation
Governments worldwide are rushing to regulate AI. The EU AI Act is the most comprehensive law — banning high-risk AI applications and requiring transparency and testing. The US approach is lighter: executive orders and voluntary commitments. China mandates registration of AI systems. The regulatory landscape will shape your career.
- EU AI Act (2024-2026) is the world's most comprehensive AI law
- Bans certain high-risk AI uses and requires transparency and risk assessments
- US approach is lighter: voluntary commitments and sector-specific guidance
Did you know? The EU AI Act defines AI systems used in hiring, credit scoring, and law enforcement as 'high risk' — requiring human oversight and bias audits.
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Your Decade in AI
The best preparation for an AI-shaped decade: learn to prompt well (now), understand APIs and how to build with them (2025-26), follow key model releases and benchmark updates, and maintain deep domain expertise. Generalists who can direct AI will outperform pure AI specialists without domain knowledge.
- Learn prompting now — it is free and pays immediately
- Learn the API when you build your first side project
- Maintain deep domain expertise — AI fluency × domain = rare and valuable
Did you know? The 10 skills most in demand in 2030 do not exist yet in their current form — the ability to learn and adapt is more valuable than any fixed skill set.
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Multimodal AI
Multimodal models process and generate text, images, audio, and video together. GPT-4o can see your screen and speak. Gemini 1.5 can watch a 1-hour video and answer questions about it. The interface is becoming the conversation.
- GPT-4o processes text, image, and audio in a single model
- Google Gemini 1.5 Pro has a 1M token context window — roughly 1 hour of video
- Multimodal opens AI to accessibility use cases: live transcription, image description, real-time translation
Did you know? Within 2 years, the chat with your documents use case will expand to chat with your video library — the context window just needs to grow another order of magnitude.
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AI Agents
An AI agent is a model that takes a goal, breaks it into steps, uses tools (browser, code runner, file system), and iterates until done. Claude Code writes and runs code. AutoGPT browses the web. Devin (Cognition) can open a pull request autonomously.
- Agents fail at long-horizon tasks — reliability degrades with each step
- The key primitive: tool use (letting the model call APIs and execute code)
- Multi-agent systems: one model plans, another executes, a third reviews
Did you know? Claude Code, Cursor Agent, and GitHub Copilot Workspace are all agent-mode products — the shift from autocomplete to autonomous implementation is already in your IDE.
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The AGI Debate
AGI — AI that matches or exceeds human capability across all cognitive tasks — is the stated goal of OpenAI, Anthropic, and DeepMind. Timeline estimates range from 2027 to never. What matters to you: the productivity step-change is already here, regardless of whether AGI arrives.
- OpenAI CEO Sam Altman: AGI may arrive within a few thousand days
- Most AI researchers define AGI as a moving target — we keep raising the bar after each milestone
- The practical impact: plan for AI that is 10x better than today within 5 years, whatever we call it
Did you know? Every time AI beats a benchmark — chess, Go, protein folding, bar exam — the definition of real intelligence shifts. This is known as the AI effect, or Teslers theorem.
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Your Path in the AI Era
The over-20s generation will live their entire careers in an AI-shaped economy. The winning strategy: develop deep domain expertise plus strong AI collaboration skills. Neither alone is enough. Be the person who knows the domain AND knows how to direct AI to work in it.
- Prompt engineering fluency: achievable in 30 hours of deliberate practice
- Build one AI-powered side project in 2025 — the learning compounds
- Follow 3-5 AI researchers or practitioners on X/LinkedIn — the field moves weekly
Did you know? The roles that will grow most by 2030 are not AI jobs — they are every existing job, amplified. The doctor, lawyer, and designer who use AI well will outperform those who do not.