In April 2026, Sequoia Capital held its fourth annual AI Ascent conference. Three partners — Pat Grady, Sonya Huang, and Konstantine Buhler — made a stunning declaration in the Keynote: from a functional standpoint, AGI has arrived. Their definition is not academic — it's commercial: when you can dispatch an agent to execute a task and it can recover from failure and persist until the job is done, that is functional AGI.
This article synthesizes the full Keynote transcript, plus highlights from six talks by Demis Hassabis (DeepMind), Greg Brockman (OpenAI), and others. Using first-principles thinking, we break down: what functional AGI means, the exponential acceleration of the METR curve, the $10 trillion shift from Copilot to Autopilot, the three pillars of the Agent economy, and Taiwan's 18-month window of opportunity.
In academia, there is still no consensus on the definition of AGI. But Sequoia's three partners are not academics — they are investors who have reviewed tens of thousands of companies, and their definition comes from business reality.
In plain terms: if you can send an AI to do something, it can solve problems on its own, won't give up halfway, and keeps going until it's done — that's AGI.
Grady specifically emphasized that he is not proposing a "technical definition." He studied economics and researches the collision between founders and markets — in other words, "business." From a commercial, practical, functional standpoint, the emergence of long-horizon agents is a watershed moment.
The first two were "a faster horse" — boosting your productivity by 10–40%, but not changing how you work. The third is "the arrival of the car" — boosting productivity by 10–40x, fundamentally changing the nature of work itself.
In the Keynote, Sonya Huang presented research data from METR (Model Evaluation and Threat Research) — arguably the most important slide of the entire event:
The METR curve measures: how long an AI model can persist on complex tasks without "going off the rails."
AI task duration doubles every 7 months.
What is the essence of the METR curve? It doesn't measure "intelligence" — it measures reliability multiplied by persistence. An average human employee can maintain focused work for 4–6 hours. When an agent's METR score reaches that level, it is functionally equivalent to a full-time employee — except it doesn't need sleep, never takes days off, and can be infinitely replicated.
At the rate of doubling every 7 months, we reach this crossover point around 2028. This is not speculation — it's an extrapolation based on an already-observed exponential curve.
Sonya specifically mentioned AutoGPT and BabyAGI from 2022 — which also wrapped GPT-3 in a loop and let it run autonomously. The result?
Three years later, why does the same architectural concept suddenly work? The answer is that three components matured simultaneously:
Sonya used a brilliant analogy: the SaaS tools we built for humans over the past two decades have now become "prosthetic limbs" for agents. SaaS isn't dead — it's about to see the biggest user explosion ever, except the users are no longer human.
This is the most commercially impactful framework of the entire Keynote. Pat Grady opened with a chart that silenced the room:
Over the past 15 years of cloud transformation, the software TAM grew from $350 billion to $650 billion. Cloud accounted for $400 billion of that.
This is your budget for selling tools: $1 spent on software for every...
The services market AI agents can address: $10 trillion. U.S. legal services alone are $400 billion — equal to the entire software market.
This is your budget for selling outcomes: $6 spent on services.
Copilot sells tools — "This software makes your lawyer more efficient." The customer spends $1.
Autopilot sells outcomes — "This agent litigates and settles your case." The customer spends $6.
Same customer, 6x the addressable budget. That's why the AI wave dwarfs the cloud era in scale.
Using "writing code" as an example, Sonya illustrated the four stages from assistance to full autonomy:
| Stage | Year | Form | Human Role |
|---|---|---|---|
| Tab Autocomplete | 2023 | AI autocompletes beside you | Typist |
| Agentic Dev | 2025 | You direct the agent | Manager |
| Async / Background | 2026 | Agent runs in the background | Executive |
| Dark Factory | 2027+ | Fully unsupervised | Owner (occasional reports) |
"Dark Factory" is a term from manufacturing — referring to fully automated factories that don't even need the lights on. Sonya said she has already seen this pattern in production environments, including cybersecurity companies.
Sonya listed agent applications that are already happening:
Why is "services as the new software" more than just marketing speak? Because the fundamental cost structure of enterprises is changing. Scaling with humans is hard (hiring, training, management), while agents scale infinitely with compute. Humans need salaries; agents only need tokens.
Today, humans are still smarter at most things — but as Scaling Laws continue, agents will soon surpass humans in many domains. This isn't humans being replaced — it's the human role shifting from "the one who does things" to "the one who decides what to do."
Pat Grady proposed the MAD framework — for every founder building on top of foundation models:
Google DeepMind CEO Demis Hassabis made a specific yet bold assessment during his AI Ascent interview:
By his estimate, we will reach true AGI around 2030. But what's more interesting is his breakdown of the "75% completed" and the "remaining 25%."
Hassabis offered a more profound philosophical insight: information may be more fundamental than energy and matter. If the universe is fundamentally about information processing, then the significance of AI is even more far-reaching than we currently imagine — "and it's already pretty profound."
He used a precise analogy: our current AI is like the 120 years of tinkering before the steam engine was invented — something is moving, but we haven't yet discovered the "laws of thermodynamics" behind it. The foundational science of AI — why it works, why Scaling Laws exist — we still don't truly understand.
This is Hassabis's pragmatic advice for the AGI race: don't rush toward "superintelligence" — first perfect the "super tool." That step alone is already enough to fundamentally change the world.
Hassabis also shared a lesson from his own experience running a game company (Elixir Studios):
Be 5 years ahead, not 50.
Technology that's too far ahead can't find a market; technology that's too far behind can't find an edge. Five years is the sweet spot — early enough to build a moat, but not so early that you burn through your capital waiting for the market to mature.
OpenAI co-founder Greg Brockman offered a deceptively simple yet profoundly important insight during his interview:
The traditional workplace bottleneck was execution capacity — not enough people to do the work. In the agent era, execution capacity approaches infinite supply. The new bottleneck becomes judgment — "Should this be done at all? Is the result what I actually wanted?"
Not enough people, not enough skills, not enough time.
"I know what to do, but I can't find anyone to do it."
Not enough attention, not enough judgment, not enough direction.
"AI can do anything, but I'm not sure what it should do."
Brockman's point boils down to this: "doing" is being commoditized, and "thinking" becomes the only source of differentiation.
In this framework, the most valuable capability of the future isn't "being able to use AI" — it's "knowing what to have AI do." This echoes Karpathy's point from another talk: "You can outsource thinking, but you can't outsource understanding."
Konstantine Buhler covered the third segment of the Keynote — "What happens next?" He drew a sweeping analogy: the cognitive revolution will reshape the world just as the Industrial Revolution did.
Before 1700: 99%+ of physical work was done by humans and animals.
Waterpower → Steam engine → Internal combustion → Electric motor
2026: 99%+ of physical work is done by machines. Planes carry you; factories manufacture all goods.
Before 2020: 99%+ of cognitive work was done by humans.
Calculators → Digital computing → Neural networks → ???
The future: 99.9% of cognitive work will be done by machines. We are at this inflection point right now.
Before handing off to Buhler, Sonya posed a series of unsettling questions:
For the agent economy to function, three pillars are needed:
Looking back at history: the Industrial Revolution required new economic infrastructure to unlock its full value — limited liability companies, central banks, patent law, labor law, antitrust law. The cognitive revolution will likewise require entirely new infrastructure.
Whoever builds these three pillars first will control the gateway to the next economic era. This is not a technology problem — it is an institutional innovation problem.
Buhler used an elegant historical story to illustrate the impact of the cognitive revolution: in 1884, the United States capped the Washington Monument with the most precious metal in the world at the time: aluminum. 100 ounces of pure aluminum sat atop the monument, worth more than gold.
Then the electrolytic process for producing aluminum was invented. The same metal crashed in price by 99.5%. Today you wrap your sandwich in aluminum foil and throw it away.
AI's impact on "cognitive work" is like the electrolytic process's impact on aluminum. The expertise you pay millions for today may become as cheap as aluminum foil tomorrow. But the ubiquity of aluminum didn't destroy the world — it created entirely new industries: aviation, construction, packaging, and more.
The question isn't "will cognitive work lose its value" — it will. The question is "can you use cheap cognitive capacity to create unprecedented value?"
Sequoia's framework holds special significance for Taiwan. Taiwan sits at the core of the AI supply chain yet on the periphery of the AI application layer — a dangerous asymmetry.
Why 18 months? This comes from cross-referencing the METR curve with Sequoia's market analysis:
Taiwan's core challenge isn't "whether it has AI technology" — TSMC is the best AI technology there is. The question is whether Taiwan can upgrade from "selling shovels" to "mining the gold."
In a gold rush, selling shovels is good business — but when the shovels start mining gold on their own, the shovel seller who doesn't transform can only watch as agents carry the gold away.
Every time a new technology wave arrives, "bubble talk" follows. Many people are now asking: is AI a repeat of the 2000 dot-com bubble? Pat Grady's framework provides a clear answer.
| Dimension | 2000 Dot-Com Bubble | 2026 AI Wave |
|---|---|---|
| Type of Revolution | Communications revolution (information distribution) | Compute revolution (information processing) |
| TAM | $650B software market | $10T+ services market |
| Revenue Growth | Pets.com burned cash with no revenue | Multiple AI companies have surpassed $1B ARR |
| Technology Maturity | Broadband not yet widespread | Foundation models already production-ready |
| Scaling Laws | Did not exist | Pretraining + inference-time + RL — three Scaling Laws still holding |
The core lesson of the 2000 dot-com bubble isn't "the internet had no value" — it's "the value was priced in too early." The bubble burst, but Amazon, Google, and Facebook grew from its ashes.
Will AI have a bubble correction? Possibly. Some companies will die. But Grady's "compute revolution vs communications revolution" framework tells us: AI's fundamentals are stronger than the internet's, because it doesn't just change how information flows — it changes how information is processed. That's a deeper transformation.
Bubbles aren't scary. What's scary is avoiding the market out of bubble fear, only to realize — after it pops — that you missed the next Amazon.
If you're still selling software licenses (Copilot model), your addressable market is $1. If you can reposition to sell outcomes (Autopilot model), your addressable market is $6. Same customer, 6x the difference.
Action: List your top 10 customers. For each one, ask: "If we stopped selling tools and directly sold the outcome they want, how much could we multiply our pricing?"
This isn't about layoffs — it's about redeployment. One employee plus one agent can match the productivity of 10 people in the past. But this requires redesigning workflows, not simply "adding an AI tool."
Action: Conduct a "task verifiability audit" (borrowing Karpathy's framework). Prioritize automating high-verifiability tasks (code, data analysis, compliance checks).
Brockman said context is a one-time investment. Is your company's knowledge base, decision-making process, and customer data structured to a degree that agents can directly use?
Action: Assign one person (or one agent) to convert your company's core knowledge into an agent-consumable format within 90 days.
Modes (customer stickiness), Affordance (ease of use), Diffusion (market penetration). If all three are weak, you'll be displaced soon.
Action: Score your product on MAD (1–10 each), identify the weakest dimension, and concentrate resources on strengthening it.
If your moat is "our engineers are excellent" or "our software is mature," these can be leveled overnight in the agent era (Notion rewrote 8 million lines of code in 6 weeks; Brett Taylor rewrote Sierra over a single weekend). Durable moats are customer relationships, unique data, and institutional trust.
Action: List your 3 biggest moats. For each one, ask: "If agents could do everything a human can, would this moat still exist?"
Let's return to Pat Grady's metaphor: the AI applications of the past few years were "a faster horse." Now, "the car has arrived."
At AI Ascent 2025, Sequoia's message was "AI is not the future — it's now."
In 2026, they revised it: "Functional AGI has arrived. The question isn't whether AGI exists, but who will capture that $10 trillion."
The core messages across six talks:
Sonya Huang's closing remark is the best summary of the entire conference:
"Whatever you can imagine building over the next hundred years, we think is now possible in a hundred days thanks to agents."
What you planned to build over a hundred years can now be done in a hundred days with agents.
This isn't hyperbolic marketing. This is the true story of the Zed founder completing three years' worth of plans solo with Claude Code over a holiday. This is the real case of the Notion team rewriting eight million lines of code in six weeks.
The car is here. You can keep riding your horse. But the car won't wait for you.