AGI Claims, Contracts, and Benchmarks: What actually holds up

The contracts, benchmarks, and incentives behind every AGI claim, checked against what the data actually shows.

30 min read 

Ask ten people to define artificial general intelligence, or AGI, and you likely get ten different answers. Ask the people building it for a living, and the answers spread even further apart.

In March 2026, Nvidia CEO Jensen Huang said on Lex Fridman's podcast, "I think we've achieved AGI." He pointed to an AI agent capable of launching a billion-dollar company as his evidence, a claim Forbes covered in detail. That same month, ARC-AGI-3, a benchmark launched by the ARC Prize Foundation specifically to test the kind of reasoning AGI would need, had leading AI systems solving under 1 percent of its tasks. Human testers solved every one of them.

Both facts are on record. That gap, between what companies call AGI in a press release and what researchers can measure in a benchmark, shapes most of the AGI debate this year. If the outcome ever touches your paycheck, it is also worth noting how OpenAI's own definition frames the goal: outperforming humans at "most economically valuable work." That framing sits close to an older, more familiar argument about which jobs actually hold their value, one USA Beam covered in a piece on jobs that now out-earn a four-year degree, and one that connects directly to a companion breakdown of what each level of college degree actually pays. AGI's economic definition and the ordinary question of which skills still pay are closer than they look.

This guide covers what artificial general intelligence actually means, how it differs from the generative AI already on your phone, what the different types of AGI look like, which companies are racing toward it, and when the people closest to the work think it might show up. It also covers what most competing explainers leave out: the contract language that quietly changed in late 2025, why lab benchmarks lag what researchers already know, and a short checklist for reading the next AGI headline that crosses your feed.

What is artificial general intelligence?

Artificial general intelligence describes a machine that can understand, learn, and apply knowledge across any intellectual task a human can do, not just the narrow task it was trained on. No system has met that bar yet. Every AI product on the market today, including ChatGPT, Gemini, and Claude, is a narrow or general-purpose tool built for specific kinds of work, not a mind that generalizes the way a person does.

The most widely cited artificial general intelligence definition comes from OpenAI's 2018 charter, which defines AGI as "highly autonomous systems that outperform humans at most economically valuable work." IBM defines it as a hypothetical stage where an AI system matches or exceeds human cognitive abilities across any task. Google Cloud describes it the same way, as a machine that can perform any intellectual task a human can.

Notice what these definitions leave out. None of them require consciousness, emotional depth, or creativity in the way people usually mean those words. A Google DeepMind research team that studied the term in 2023 found that AI researchers have never landed on one agreed definition of general artificial intelligence, only a cluster of competing ones built around economic output, generality of skill, or how closely a system's behavior resembles a human being.

The disagreement has real consequences. Sam Altman, OpenAI's CEO, told CNBC that AGI is not a super useful term anymore, since almost nobody agrees on what it describes. Microsoft CEO Satya Nadella has gone further, dismissing OpenAI's own economic definition as a form of benchmark gaming, and offering his own marker instead: a world economy growing at 10 percent a year. When the people funding AGI research cannot agree on what they are funding, define artificial general intelligence however you like. Just expect an argument.

Artificial general intelligence vs artificial intelligence

Artificial intelligence is the umbrella term for any system that performs tasks normally requiring human intelligence, from spam filters to self-driving cars to the app that recommends your next show. Artificial general intelligence is one specific, unrealized goal inside that much larger field.

Researchers usually sort AI into three tiers. Artificial narrow intelligence, also called weak AI, handles one task or a tight cluster of related tasks and cannot transfer that skill anywhere else. It is the only tier that actually exists in deployed products today: Siri, Netflix recommendations, spam filters, and even ChatGPT by some strict definitions all count as narrow AI, since each one operates inside a fixed scope. Artificial general intelligence, sometimes called strong AI, would match human flexibility across virtually any domain, picking up a new skill the way a person does rather than needing a new model trained from scratch. Artificial superintelligence sits above both and would exceed the best human performance in every field at once.

AI and AGI are not the same thing. Every AI tool in daily use today, no matter how capable, still lives inside artificial narrow intelligence. Artificial general intelligence describes the next tier up, and nobody has reached it yet.

Artificial general intelligence vs generative AI

People confuse generative AI and artificial general intelligence constantly, partly because the same companies build both and market them with similar language. They are not the same thing.

Generative AI describes systems trained to produce new text, images, audio, video, or code by predicting likely outputs based on patterns in training data. ChatGPT, Gemini, Claude, Midjourney, and Sora are all generative AI. They are genuinely useful and improving fast, but they work through pattern completion within the domains they were trained on. A practical example of that narrow, task-bound usefulness shows up in personal finance, where USA Beam tested specific ChatGPT prompts for budgeting and money questions. Those prompts work because the task is bounded and the model has seen thousands of similar examples, not because ChatGPT has decided to learn finance on its own initiative. A generative model does not transfer a lesson from cooking to carpentry, not unless a training process specifically built that behavior in.

Artificial general intelligence describes a different, still hypothetical, capability: a system that reasons, learns, and adapts across domains the way a human does, without needing separate training for each new task. Generative AI is a product category that exists and ships updates every few months. Artificial general intelligence is a research target that nobody has hit.

The confusion carries a business logic. Framing a chatbot upgrade as a step toward AGI reads better in a funding round than framing it as a better autocomplete.

Types of artificial general intelligence

Because nobody agrees on a single definition, researchers built frameworks instead. The most cited one comes from a 2023 Google DeepMind paper, later presented at the ICML conference, which sorts both narrow and general AI into five ascending levels based on how a system's performance compares against human percentiles.

Level	Name	Performance bar	Status for general AI
1	Emerging	Equal to or somewhat better than an unskilled human	Reached, per the DeepMind team's 2023 assessment, by systems like GPT-4 and early Gemini
2	Competent	At least the 50th percentile of skilled adults	Not yet reached
3	Expert	At least the 90th percentile of skilled adults	Not yet reached
4	Virtuoso	At least the 99th percentile of skilled adults	Not yet reached
5	Superhuman	Outperforms 100 percent of humans	Not yet reached, equal to artificial superintelligence

The framework separates general performance from narrow performance, and that split matters. Narrow AI has already hit the top level in specific tasks. AlphaFold outperforms every human scientist at predicting how a protein folds. Deep Blue beat world chess champion Garry Kasparov in 1997. The chess engine Stockfish now beats any human alive at chess, every time. None of that counts as general superhuman AGI, because none of those systems can do anything outside the one task they were built for.

Critics of the DeepMind framework point out a real weakness: terms like competent, expert, and virtuoso do not have a fixed, agreed meaning, and the percentile cutoffs of 50, 90, and 99 were chosen without a clearly stated reason. The framework gives the field a shared vocabulary. Whether it gives a reliable way to measure progress is still an open argument among researchers.

Super artificial general intelligence

Super artificial general intelligence, more often called artificial superintelligence or ASI, describes a system that would exceed the best human performance in every field at once, not just match it. Philosopher Nick Bostrom laid out the concept formally in his 2014 book, Superintelligence: Paths, Dangers, Strategies, and it remains the reference point most AI researchers still cite.

In the DeepMind Levels of AGI framework, superhuman general performance and artificial superintelligence are the same thing: level 5, the top of the scale, where a system beats 100 percent of humans across a wide range of tasks, including some tasks no human can do at all. Nobody has built anything close to this at a general level. The DeepMind team's own 2023 assessment placed current systems at level 1, Emerging, two full levels below Competent and three below Expert.

Industry leaders talk about ASI more casually than the research supports. OpenAI's Sam Altman wrote in 2024 that superintelligence could arrive within a few thousand days, or roughly by 2034. Demis Hassabis of Google DeepMind has said his own timeline runs a bit longer, landing somewhere shortly after 2030 rather than in the next year or two. The distance between those two views says a lot about how unsettled this part of the field remains.

Example of artificial general intelligence

Here is the direct answer: no working example of artificial general intelligence exists today. Nobody has built full general intelligence yet. What gets called an AGI example in headlines is usually one of two very different things.

The first is superhuman narrow AI, a system that beats humans badly at one task and cannot do anything else. AlphaFold predicting protein structures, Deep Blue winning at chess, and IBM Watson beating Jeopardy champions Ken Jennings and Brad Rutter in February 2011 all fit here. Each is a real, verified example of a machine outperforming the best humans at a job. None of them can hold a conversation, drive a car, or do anything besides the single task they were built for.

The second is what the DeepMind framework calls Emerging AGI: general-purpose systems that handle a wide range of non-physical tasks at a level roughly equal to an unskilled adult. DeepMind researchers placed GPT-4, Google's Gemini, and Meta's Llama in this category back in 2023. Newer systems, including the GPT-5 family and Claude, extend that same category rather than moving past it, since nobody has published evidence of a general system reaching the next level, Competent, at the 50th percentile of skilled adults across a broad task set.

So if someone shows you a chatbot and calls it AGI, ask which definition they are using. There is a real difference between a model that writes a good email and a mind that can learn any human skill the way a person learns it.

Does artificial general intelligence exist right now

No, not by the definition most AI researchers use. Every major lab, including OpenAI and Google DeepMind, agrees that no system today performs at human level across the full range of intellectual tasks. That has not stopped some of the industry's loudest voices from saying otherwise.

Jensen Huang's own definition was narrow by design: an AI counted as AGI if it could generate serious revenue fast, not if it could reason, learn, or plan the way a person can. Financial coverage of the remark pointed out an obvious incentive: declaring AGI already achieved raises the case for buying more of Nvidia's own chips, since Nvidia's hardware trains and runs the systems in question. That incentive matters more than it looks, given how tight AI chip supply has been. A hidden bottleneck in high-bandwidth memory, not raw chip output, has been quietly capping how fast labs can actually train frontier models, a constraint USA Beam broke down in its look at the 2026 AI chip war. A company sitting at the center of that bottleneck has every reason to talk up the finish line.

The benchmark evidence backs the skeptics. ARC-AGI-2, a test built specifically to measure abstract reasoning without letting a model lean on memorized patterns, had a human panel scoring around 60 percent while frontier models started near zero when the test launched. When the ARC Prize Foundation released an interactive version, ARC-AGI-3, in March 2026, human testers cleared 100 percent of its environments while the strongest purpose-built AI agent managed just 12.58 percent, and general-purpose frontier models from OpenAI, Google, and Anthropic all scored under 1 percent.

There is one notable dissent worth naming. Anthropic president Daniela Amodei told CNBC that AGI is such a funny term, and said that by some definitions of matching human capability, Claude has already cleared that bar in narrow professional terms, pointing to coding, where it now writes code about as well as many of Anthropic's own engineers. She was equally direct about the limits, noting that Claude still cannot do a lot of things humans do without effort. Her point undercuts the idea of a single AGI finish line: AI systems can now beat humans at some tasks while still failing at closely related ones, which makes a yes-or-no milestone hard to define.

The contract clause investors actually watch

Most AGI explainers treat the term as a purely scientific argument. It is also, quietly, a legal one, and the legal version has changed more recently than most coverage reflects.

For years, OpenAI's commercial agreement with Microsoft included a provision tied to AGI. If OpenAI's own board determined the company had built a system meeting its internal AGI threshold, Microsoft's license to OpenAI's technology could lapse. That structure gave OpenAI's board unusual leverage over its most important commercial relationship, and it fed years of speculation every time a new model launched: did this count?

That changed in October 2025, when OpenAI completed a restructuring into a public benefit corporation and signed a new agreement with Microsoft. Microsoft now holds intellectual property rights to OpenAI's models and products through 2032, including whatever comes after AGI. Any future AGI declaration is now verified by an independent panel of experts rather than decided unilaterally by OpenAI's board, and a revenue-sharing arrangement between the two companies stays in place until AGI is reached, whichever comes first between 2032 and that verification.

This is worth sitting with, because it flips the old incentive on its head. Under the original clause, OpenAI's board had reason to define AGI narrowly, since declaring it too early could unravel its most valuable partnership. Under the new terms, an early AGI declaration mainly triggers an independent review rather than an automatic termination, which lowers the stakes of the announcement itself, though the revenue-sharing and IP terms still make timing far from neutral. When OpenAI's $852 billion valuation and its March 2026 funding round from Amazon, Nvidia, and SoftBank get discussed, this contract detail rarely comes up. It should, since a large share of Amazon's $50 billion commitment to that round was reported to be contingent on OpenAI reaching an IPO or the AGI milestone, whichever arrives first.

The takeaway for a reader is simple. When an executive at a company with an AGI-linked contract makes a confident claim about AGI, check whether that company's revenue, IP rights, or valuation depend on the claim being believed. That single check filters out a lot of noise.

Where the claims stop matching the data

Several specific AGI claims sound credible on first read and fall apart once checked against the record. A short comparison makes the pattern clear.

The claim	What the record actually shows
A model beating a benchmark means it has that skill generally	Benchmark contamination is hard to rule out once a model's training data postdates the benchmark's public release, which is why researchers keep building new, uncontaminated tests
AGI will arrive as a single announced moment	Every framework in active use, including DeepMind's levels and OpenAI's own charter language, describes AGI as a gradient. Even lab leaders disagree on which point on that gradient counts.
More compute, and bigger models guarantee AGI eventually	This is an active, unresolved dispute inside the field, not settled science. Meta's former chief AI scientist, Yann LeCun, left the company at the end of 2025 specifically over this disagreement
Strong coding performance is a good proxy for general intelligence	Coding is one of the most benchmark-saturated, well-defined domains in AI, which makes it a weak stand-in for tasks with less training data and less clear success criteria
Voluntary safety testing means every major lab has ruled out risk before release	The federal pre-release testing program is non-binding, and Meta has not joined it, unlike OpenAI, Anthropic, Google DeepMind, Microsoft, and xAI

The gap between how AGI gets discussed and how it actually gets measured is wider on almost every specific claim than a general hype versus reality framing suggests.

Is artificial general intelligence possible?

Most people working in the field think so, eventually. The disagreement is mostly about method, not destination.

The dominant approach right now, used by OpenAI, Google DeepMind, Anthropic, and xAI, bets that scaling up today's large language models, adding more training data, more compute, and better reasoning techniques, will eventually produce something close to general intelligence. This is the approach behind GPT-5.6, Gemini, Claude, and Grok. It is also an approach with a real physical cost. Training and running these models depends on data centers that draw enormous amounts of electricity and water for cooling, a tension USA Beam examined in a look at the permit-versus-actual-use gap in AI data center water consumption. Scaling toward AGI is not just a research question. It is also a resource question that communities near those data centers are already living with.

Not everyone agrees this path works. Yann LeCun, Meta's former chief AI scientist, left the company at the end of 2025 specifically over this disagreement. He has argued publicly that large language models cannot reach human-level understanding no matter how much they scale, because they lack a working model of the physical world. His new company, Advanced Machine Intelligence Labs, is betting on a different architecture built around what researchers call world models rather than text prediction.

A smaller group of researchers goes further, and doubts AGI is achievable at all with current computing approaches, though that remains a minority position even among skeptics. The honest summary: nobody has proven artificial general intelligence is possible, and nobody has proven it is not. The industry is spending hundreds of billions of dollars betting on the first outcome.

Why lab benchmarks lag what researchers already know

A detail that rarely makes it into consumer coverage: the benchmark score attached to a public model release is usually already out of date by the time it reaches a headline.

Public benchmarks get gamed within months of release because training data absorbs them. ARC-AGI-1 went from difficult to nearly solved within about two years, with Google's Gemini 3.1 Pro reaching close to 98 percent by early 2026. That is not necessarily because models got smarter at the exact skill the benchmark measured. It is often because the benchmark's questions, or ones structurally similar to them, ended up inside later training data.

This is why labs keep private, held-out evaluation sets that never get published. A public benchmark stops reliably measuring capability the moment a model's training cutoff passes the benchmark's release date, since nobody outside the lab can fully rule out contamination. In practice, this means a model's public benchmark score often reflects a snapshot of capability that internal researchers already knew was outdated, sometimes by six months or more, before the public ever saw the number.

The effect shows up clearly in the ARC-AGI series. Each version resists the shortcuts that made the previous one solvable, and each time, frontier scores reset back toward zero before climbing again over the following months. ARC-AGI-2 reset the gap after ARC-AGI-1 saturated. ARC-AGI-3 reset it again in March 2026. That repeating cycle, more than any single reported score, is the most honest signal available for how close artificial general intelligence really is.

For a reader, the practical lesson is to treat any single benchmark score as a lower bound on how outdated it might already be, not as a live reading of what a lab's best internal systems can currently do.

When will artificial general intelligence be achieved

It depends entirely on who gets asked. Independent researchers and company executives give wildly different answers, and the gap between them is one of the more telling facts in the whole debate.

Start with the researchers. AI Impacts surveyed 2,778 published AI researchers in 2023 and found a median estimate of 2047 for a 50 percent chance of high-level machine intelligence, a system that outperforms humans at every task and does it more cheaply. That estimate had jumped forward 13 years from the same survey conducted just one year earlier, in 2022, when the median was 2060. Only 10 percent of researchers expected that milestone by 2027.

Now compare that with the people running AI companies. Anthropic CEO Dario Amodei has said he expects a model that can do everything a human professional can do, across many fields, sometime in 2026 or 2027. Google DeepMind CEO Demis Hassabis said his own estimate runs longer, and landing somewhere shortly after 2030, while Google co-founder Sergey Brin has said he expects AGI before 2030. Elon Musk has predicted AGI by the end of 2026, after predicting it for 2025 the year before that.

That is roughly a 20-year spread between the median researcher and the most bullish executives, and the executives all have a direct financial interest in the answer. Worth remembering the next time a single headline number goes around.

The timeline question has four different answers, not one

Every AGI timeline article, including most of the ones covering the same executives quoted above, treats the question as if it has one correct date. In practice, the honest answer changes hard depending on which specific capability someone means.

For narrow economic tasks, first-draft writing, customer support scripts, and especially coding, something close to AGI-level performance already exists in specific niches. That is why Daniela Amodei's claim about Claude and coding is not wrong, just narrow to a domain that happens to have enormous amounts of clean training data.

For tasks that require modeling the physical world, robotics, causal reasoning about objects, and cause and effect in messy environments, the timeline stretches much further out. This is the exact gap Yann LeCun cites for leaving Meta, and it is the gap ARC-AGI-3 was built to measure, since its environments require an agent to build an internal model of rules nobody explained to it.

For tasks requiring long-horizon planning across many steps or sessions, current systems remain closer to the starting line than the coding comparison suggests. The sub-1 percent ARC-AGI-3 scores from GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro are the clearest evidence for this category specifically.

For regulatory or safety-gated domains- medical diagnosis, legal judgment, financial advice- even a technically capable model faces a slower real-world timeline, because deployment depends on institutions and liability rules, not just raw model capability.

The rule worth keeping: whenever someone cites a single AGI date, check which of these four categories they are actually describing. A date that fits the coding category rarely applies to the other three.

How close are we to artificial general intelligence

Closer than five years ago, and further than the headlines suggest. Both things are true at once, depending on which yardstick gets used.

By the DeepMind Levels of AGI framework, general-purpose AI systems sit at level 1, Emerging, the same level researchers assigned to GPT-4 back in 2023. No general system has been shown to reach level 2, Competent, which requires matching the 50th percentile of skilled adults across a broad set of tasks, not a handful of benchmarks.

Benchmark results tell a similar story, just with a shorter cycle each time. ARC-AGI-1, a test of visual pattern reasoning built by researcher François Chollet, went from difficult to effectively solved within a few years, with Google's Gemini 3.1 Pro reaching about 98 percent by early 2026. Researchers responded with a harder version, ARC-AGI-2, built specifically to resist the shortcuts that made the first version solvable. It reset the gap: human testers still cleared roughly 60 percent of it, while frontier scores climbed back toward 77 percent within about a year. The even harder interactive version, ARC-AGI-3, covered above, reset the scoreboard again in March 2026, with frontier models scoring below 1 percent against a full human sweep.

The pattern repeats every time a benchmark gets harder. Language models climb quickly, someone builds a version that resists the shortcuts that got them there, and scores fall back toward zero. That cycle, more than any single number, is the clearest answer available for how close artificial general intelligence really is.

Artificial general intelligence companies

A handful of companies say building artificial general intelligence is their actual mission, not just a product roadmap. Their strategies, and their balance sheets, differ sharply.

OpenAI wrote the definition most of the industry still argues about. The company closed a $122 billion funding round on March 31, 2026, backed by Amazon, Nvidia, and SoftBank, at an $852 billion post-money valuation, and it reported roughly $2 billion in monthly revenue around the same announcement. OpenAI released its GPT-5.6 model family in July 2026, now the default model behind ChatGPT.

Anthropic was founded in 2021 by former OpenAI researchers, including siblings Dario and Daniela Amodei, who left over disagreements about safety and the pace of commercialization. The company builds Claude and crossed a $30 billion annualized revenue run rate in April 2026, ahead of OpenAI's disclosed figure of roughly $25 billion at the time, despite a far smaller consumer user base and a training budget reported at a fraction of OpenAI's. Anthropic also signed a compute agreement with xAI in May 2026 for access to its Colossus data center, a deal that sits alongside its existing compute partnerships with Google Cloud and Broadcom.

Google DeepMind combines Google's research lab with its product teams and publishes much of the academic work that shapes how the rest of the industry talks about AGI, including the Levels of AGI framework. Its Gemini models compete directly with GPT and Claude, and CEO Demis Hassabis, a Nobel laureate for his work on AlphaFold, has said AGI could still be several years away even as progress speeds up around him.

xAI, founded by Elon Musk in 2023 and now a subsidiary of SpaceX, built one of the largest AI compute clusters in the world at its Memphis facility and released Grok 4.5 in July 2026. Musk has repeatedly predicted AGI within the next year, a prediction that keeps sliding forward by roughly a year at a time. His pattern of parallel infrastructure bets is not new. Starlink, his satellite internet venture, followed a similar path of aggressive buildout years ahead of any guaranteed payoff, a story USA Beam tracked through Starlink's 2026 plans and IPO chatter. Both bets rest on the same underlying assumption: that owning the physical infrastructure layer, whether satellites or GPUs, matters more in the long run than any single product announcement, a point echoed in USA Beam's broader guide to the invisible infrastructure behind wireless communication.

Meta AI took the most public detour. After paying roughly 14.3 billion dollars for a 49 percent stake in Scale AI and hiring its CEO, Alexandr Wang, to lead a new superintelligence effort in mid-2025, Meta lost its longtime chief AI scientist, Yann LeCun, who left at the end of the year over strategic disagreements about whether large language models could ever reach general intelligence. Meta is also, as of mid-2026, the only major American AI developer that has not joined the federal government's voluntary pre-release safety testing program that OpenAI, Anthropic, Google DeepMind, Microsoft, and xAI have all signed.

A five-question check for reading the next AGI headline

This is the part meant for readers who already understand the basics above. It is a working checklist, not a summary, built to be reused every time a new capability claim shows up in a feed.

A reader who runs a claim through these five questions can usually tell within a minute whether it deserves a second look or a shrug.

Common questions about artificial general intelligence

Is ChatGPT artificial general intelligence?

No. OpenAI's own products, including the GPT-5.6 family, are generative AI systems trained for specific kinds of tasks. They perform strongly in many areas, but they do not pick up a brand new domain the way a person does, and they still score poorly on tests like ARC-AGI-3 that measure general reasoning.

Is Claude artificial general intelligence?

No, and Anthropic does not claim otherwise. Its own leadership argues Claude beats many professional engineers at coding while still failing at ordinary tasks people handle without thinking, which reads as advanced narrow AI rather than general intelligence.

What is the practical difference between AGI and ASI?

Artificial general intelligence would match human-level performance across most tasks. Artificial superintelligence would exceed the best human performance in every field at once. AGI is the finish line researchers are chasing now. ASI is the next race after that one.

Which company is closest to building AGI?

Nobody can answer that with certainty, since no public benchmark measures general intelligence as a finished product. OpenAI, Google DeepMind, and Anthropic each lead on different measures, including revenue, research output, and specific benchmark scores. None of those measures has settled the larger question.

What happens after artificial general intelligence is built?

Nobody knows, which is exactly why AI safety has become its own research field. OpenAI, Anthropic, and Google DeepMind all publish safety frameworks describing how they plan to test and limit systems as capability grows toward AGI and beyond.

Why the disagreement matters

Artificial general intelligence remains a moving argument between researchers who measure progress in benchmark points and executives who measure it in funding rounds.

The most useful habit for reading AGI news is checking which definition a given claim actually uses: an economic one, a percentile one, a benchmark score, or a press release line. Jensen Huang's billion-dollar-company bar and the ARC Prize's abstract reasoning tests measure completely different things, and both get reported under the same three letters. The habit of branding a product with the biggest possible claim is not unique to AI labs either. Consumer tech has run the same playbook for years, sometimes with a launch that generates more headlines about its opening numbers than its actual specifications, as when USA Beam covered the Trump Mobile T1 Phone and its reported deposit figures. AGI claims sit on the same spectrum, just with far more money attached to the outcome.

Nobody currently building AI disputes that these systems keep getting more capable, year over year. What people dispute, loudly and often for money, is what to call the moment those capabilities finally add up to something general.

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