Published on November 6, 2025 12:20 AM GMT

I spent 3 recent Sundays writing my mainline AI scenario. Having only spent 3 days on it, it’s not very well-researched (especially in the areas where i’m not well informed) or well-written, and the endings are particularly open ended and weak. But I wanted to post the somewhat unfiltered 3-day version to normalize doing so. There are also some details that I no longer fully endorse, because the act of doing this exercise spurred me to look into things in more detail, and I have updated my views slightly^[1] — an ode to the value of this exercise. 

Nevertheless, this scenario still represents a very central story of how I think the future of AI will go.

I found this exercise extremely useful and I hope others will carve out a few days to attempt it. At the bottom there’s: (1) my tips on how to do this scenario writing exercise, and (2) a list of open questions that I think are particularly important (which I made my intuitive guesses about to write this scenario but feel very uncertain about).

Summary

2026-2028: The Deployment Race Era

• AI companies are all focused on monetizing, doing RL on LLMs to make products, and this is ultimately data bottlenecked, so it becomes a ‘deployment race’ because having more users/deployment gets you more money, more compute and more data. Some lean towards B2C (through search, social media, online shopping, etc.) and others lean towards B2B (enterprise agent plans)
• China is 15y behind in lithography but wakes up much harder on indigenizing lithography once they realize they are falling behind and their chips are bad. They start moving at 2x the speed of ASML history.

2028-2030: The 1% AI Economy

• Architecture innovations help make AIs more useful and reliable
• AI starts becoming a top 4 social issue for many
• Household robots enter their ‘Waymo-2025’ phase in reliability and adoption
• China 5x ahead in energy, talent and robots but 5x behind in capital and compute.
• China cracks mass-production-capable immersion DUV.

2030-2032: The 10% AI Economy

• ‘Magnificent 4’, US tech giants concentrate into fewer AI winners and have >$10T market caps
• AI is top social issue, 7-8% unemployment
• Robots doing several basic physical service jobs
• China cracks mass-production-capable EUV

2032 Branching point

• Superhuman Coder milestone (SC) is reached, and the story branches based on takeoff speed: fast (data efficient, brain-like algorithms) and slow (continual learning, data bound).
  • Major disclaimer: In one branch the US wins but eventually loses control over the AIs, and in the other China wins but maintains control. This is not because I think China is any more likely to prevent AI takeover a priori. Rather its downstream of correlations with takeoff speeds, my full explanation here:^[2]

Fast takeoff branch: Brain-like algorithms

• After the SC milestone is achieved, the AIs already have very good research taste, and one more month of improvements leads to full AI research automation.
• The AI helps invent a new algorithmic paradigm: Highly data efficient brain-like algorithms.
• The US is in lead and does Plan C,^[3] the company is in charge of how fast to go and the USG not completely in the dark but pretty uninformed about what is going on at the companies.
• Recursive self improvement happens very fast, reaching ASI in a few months.
• The alignment strategy is to train in a love for humans. This somewhat succeeds but mostly fails, and the AI mainly has some very weird reward correlates and proxies, but is also susceptible to value drift.
  • Humans become like the Toy Story toys, AI keeps them around (on earth) because they are fond of them, but soon forget about us and build up space tech and go out turning the rest of the universe into a weird optimal wire-heading mesh, 100y or so later value drift enough to come back and turn our solar system into the wire-heading mesh too (i.e., ‘throw us out’).

Slow takeoff branch: Continual learning

• SC achieved but is 18 months or so away from ASI by default
• There is a new paradigm which is online learning based, so it is quite data bottlenecked, and research taste is very important.
• China edges into the lead due to outproducing compute (largely by outproducing them on robots) and out-deploying the US, and has more total quality adjusted research taste (5x more researchers in sheer quantity, 2x more quality adjusted)
• China effectively does Plan B.
  • They were already centralized anyway, and the US sabotages them hard, leading to a year or so of back and forth sabotage that escalates into a drone/robot centric war that China wins due to industrially exploding harder than the US, including in compute production.
    • By the end China has ASI sooner and undermines nuclear MAD before war escalates to nuclear.
• China aligns AI to CCP values
  • China essentially turns Earth into a museum and terraforms mars. It lets other countries including the US keep their land.
  • China starts expanding into space, owns our solar system and takes 95% of the galaxies. There is a mild cult of personality around CCP leader but mostly this isn’t enforced, everyone has bigger fish to fry exploring space.
  • They donates 5% of galaxies to the rest of the world’s population, incl. some US citizens and implement basic rights, including banning nasty stuff like slavery and torture.
    • They punish some US war generals and people they deem acted recklessly/dangerously/unethically in the ‘before times’ but it’s capped upside based (they miss out on galaxies) and not downside based.
• [Daniel said the following about this ending and I agree with him: “IDK about the ending, I think it’s plausible and maybe the modal outcome, plus I think it’s the thing to hope for. But boy am I quite worried about much worse outcomes.” To emphasize, this is tentatively my (Romeo’s) ~modal view on what that China does with an ASI-enabled DSA, but it could look much worse and that is scary. I think it is important to emphasize though that believing in a much worse ending being more likely probably requires believing something adjacent to “the leaders controlling the aligned Chinese ASI terminally value suffering,” which doesn’t seem that likely to me a priori.]

A 2032 Takeoff Story

Jan-Jun 2026: AI Products Galore

In the latter half of 2025, US AI companies focused on using RLVR (reinforcement learning with verifiable rewards) to make super-products out of their AI models. Three or four leading companies are emerging as winners with massive revenue growth, and fit in two major categories, those that prioritized consumer applications (B2C) and those that prioritized enterprise applications (B2B).

• The Consumer Giants (B2C)
  • These companies have built SuperApps. Think ChatGPT but hyper-monetized, integrated with shopping websites and ads for free users.
• The Enterprise Giants (B2B)
  • These companies have built Mega-Enterprise-Plans. Think Claude Code reliability and usefulness but for everything on a computer. Docs, Sheets, Slides, Search, etc. These are still unreliable, and – just like Claude + Cursor Pro in 2025 – it’s actually pretty unclear if it even makes people more productive overall, but everyone is on it and everyone is hooked.

Most companies don’t fit cleanly into only one of these categories, i.e., some have done a pretty even mixture of both, but those that pushed harder on a single one have concentrated a lot of the market share. Between the top 4 US AI companies, their combined AI-only revenues approach $100B annualized revenue (up 4x from mid-2025), almost 0.1% of World GDP.

Jul-Dec 2026: The AI National Champions of China

China is increasingly worried about being left behind in AI but the Politurbo doesn’t have a confident understanding of what they should do. They see US AI companies benefitting from a snowball effect of more money → better AIs → more money, in a cycle they worry they may never be able to catch up to.

The mainstream position had been that the buildout of industrial capacity and electricity would win out in the long term. But with US AI companies booming, they worry their mounting capital and compute advantage might win out.

In 2025, they compelled companies to start using domestic AI chips, effectively banning US AI chip imports. What changes by late 2026 is that after a year of their AI companies facing bugs upon bugs and trying to build up CUDA-like software from scratch, and a growing understanding of how much more cost and power efficient western chips are, they realize just how far behind their domestic AI stack is.

This spurs China to double down on subsidizing their domestic supply chain, across chip designers, chip manufacturers, and semiconductor equipment companies, increasing government spending from a run rate of around $40B/year in 2025 (1% of the national budget) to $120B/year (3% of the national budget).

This effectively creates a series of AI National Champions with near-blank-cheques across their AI chip supply chain:

• SME Equipment and Lithography: NAURA, AMEC, SMEE, SiCarrier
• Memory: CXMT, YMTC
• Chip design: Huawei, Cambricon
• Chip manufacturing: SMIC, Huawei

Even without the step up in subsidies, these companies were growing fast and making speedy progress. In the most important area where China is lagging (photolithography), they also get increased prioritization from state espionage to carry out cyber attacks on the Dutch leader ASML. With the government subsidies they are also able to poach increasing amounts of talent from Taiwanese, Japanese and Korean companies by offering mega salary packages.

Jan-Jun 2027: The Deployment Race Era

There is a snowball effect happening in the US AI ecosystem – and the effect is two-fold. Better AI products have led to more money, which leads to all the usual benefits of being able to buy more inputs (compute and labor) to build even better AI products. But there’s an additional, more powerful feedback loop happening, which is that better AI products means more users, and more users means more feedback data for training future models on – a resource companies are increasingly bottlenecked on in the RL product paradigm.

If you have 100 times more users on your AI agent, you can collect 100 times more positive or negative feedback data from all aspects of the user’s use of your app – what they say while giving instructions, their tone, whether they end up changing something later – all sorts of juicy data, and it is becoming worthwhile to filter this data and use it to train into the next versions. Some users navigate three pages into the settings menu to toggle the “off” button for letting the companies train on this data, but that’s a tiny minority of users.

It’s not a ‘first-to-market takes all’ dynamic – many early startup AI agents (and for that matter all kinds of first-to-market AI apps) got totally left behind. Instead it’s a game amongst the AI company giants (the ones with enough resources to make use of the swaths of data), and more of a ‘first-to-100-million users takes all’ dynamic – once you hit a critical threshold of being the most popular app in a specific domain, the snowball effect from the user data is so strong that it is hard for anyone else to catch up.

The AI companies had seen this dynamic coming and are in a deployment race. This motivates companies to spread out a network of smaller inference-optimized datacenters across the world to have low latency and high throughput to as many large markets as possible. Mostly the deployment data is used in product-improvement focused RL, but the AI companies are also exploring how to leverage the data to make their frontier AIs more generally intelligent.

Jul-Dec 2027: China’s Domestic DUV

China’s lithography efforts have cracked reliable mass production of 7nm-capable DUV (deep ultraviolet lithography), allowing them to independently make US-2020-level chips en-masse.

In 2023, China’s Shanghai Micro Electronics Equipment (SMEE) had announced a 28nm-class Deep Ultraviolet (DUV) lithography tool (the SSA/800-10W), 21 years after they first started working on lithography in 2002. This is a milestone the Dutch company ASML had achieved in 2008, 24 years after their founding date.

Despite the lack of reports of volume production using SMEE systems, TSMC also didn’t use the 28nm-class Dutch machines until 3 years later in 2011. In other words, as of 2023, China was probably around 15 years behind ASML in photolithography.

With the benefit of being able to pull apart ASML DUV machines, hiring former employees of ASML and its suppliers, “knowing the golden path,” multiple public accounts of cyberattacks (2023, 2025), and the more recent spending boost, China is now moving through the lithography tech tree twice as fast as ASML did. ASML only spent around $30B (inflation adjusted) on R&D in total from 2000 through 2024, and around 300,000 employee-years. There are now 2 separate lithography efforts in China each spending north of $10B/yr with about 10,000 employees. So by 2027, they have reached the milestone of mass production with 7nm-capable DUV machines, around 13 years behind ASML. At this pace, they are on track for 5-nm capable EUV in 4 years.

Side note: After writing this time period, I came across these relevant Metaculus markets, which seem to agree with these rough lithography timelines being plausible. Nevertheless, I think I have also updated my lithography timelines longer than this scenario depicts after talking to some experts. It’s closer to my 30th percentile now, no longer 50th.

Jan-Jun 2028: Robotics Foundation Models

Robotics had long suffered from a lack of cheap, scalable, quality training data. AI world models built off of physics-realistic video generation have changed this in 2028 and are allowing techniques like Nvidia’s R²D² (training robots in simulated environments) to make big leaps.

In mid-2025 the best AI world model simulator was Google DeepMind’s Genie 3 which could generate worlds on the fly for multiple minutes at 720p, but it was very expensive and had shaky physics. Genie 6 (and some competitor models) drop in late 2027, and they can generate worlds in real time for multiple hours, with very good physics all while being pretty cheap. Multiple companies are not too far behind this level of world model generation, including some Chinese companies. 
[Side note: I have since updated towards this not being the main way they will scale data, I now think these other 3 alternatives are all more plausible: (1) creation of non-video simulated environments, like Waymo did for driving, but sped up by AI coding (2) paying large numbers of humans to do physical tasks wearing cameras and sensors like Waymo’s Project Go-Big, (3) Warehouses of lots of robots doing physical tasks that are evaluated by multimodal LLMs. E.g., Nonsense Factory)]

In 2025, the robotics autonomy levels of autonomy had reached ubiquitous deployment of scripted motion (level 0) robots in factory floors. Intelligent pick and place robots (level 1) were rolling out in places like Amazon’s warehouses. Autonomous mobility robots (level 2) had seen some very impressive prototypes, and low skill manipulation robots (level 3) were also starting to see some impressive demos (like laundry folding and doing dishes) but these were pretty cherry-picked (specific, short tasks) so this domain was still largely in R&D. Finally higher skill robots for force-dependent tasks (level 4) like plumbing, or electrician tasks, were very far away on the horizon.

By 2028, intelligent pick and place has become ubiquitous in factories, and autonomous mobility robots have also rolled out to many applications. Low skill manipulation robots have also now seen impressive general long-horizon demos (e.g., reliably, skillfully, and quickly doing hours worth of diverse tasks around a house), and some companies have started seriously working on prototyping robots to do high skill complex tasks.

With the data bottleneck unlocked, much of the robotics progress has been made with foundation models rapidly scaling up due to the 100x compute overhang created by language models. Over the course of a year robotics foundation models basically close this gap entirely, so parameter counts, context lengths and data have all seen a huge one-time jump in the past year.

The robotics progress is still not yet felt or seen in the public eye, most changes are still happening behind factory walls, so by now people have become extremely desensitized to cherry picked robotics video demos. Year after year there’s been clips of robots folding laundry, but even Jensen Huang’s laundry is still folded by a human.

Jul-Dec 2028: The 1% AI Economy

Thanks to key architecture improvements, AI companies have been able to unlock the next rung of economic-usefulness (and therefore revenues). Specifically, the top four AI companies surpass a combined $1T annualized revenue from AI products – around 1% of the world’s GDP is being directly generated by a handful of frontier AI models.

The algorithmic changes driving the latest gains have come from neuralese with low degrees of recurrence (scaling recurrence steps is very costly in hardware so hasn’t been scaled much). GPT-4 was a model that had to blurt out its first thought as its final answer. Then reasoning models like GPT-5-Thinking could use an interim ‘scratchpad’, where they could blurt out thoughts to plan its final answer. Now the models trained with neuralese recurrence do away with the ‘blurting’ altogether – they can think multiple sequential ‘thoughts’ before writing down anything at all, and have been trained freely with this cognitive freedom. This has made them much more efficient at getting to the same answers they used to get to, and has unlocked a higher ceiling on tasks where avoiding errors is particularly important. Instead of wasting a bunch of time planning things out in words, a single internal ‘thought’ can now usually do more planning than an entire page of the ‘scratchpad’ used to be able to do, as each model forward pass can now pass much more information to the next forward pass.

An analogy is that before neuralese recurrence, for the models it was like being the main character from Memento. They could only remember things they wrote down, and all the other information that might have been present in their thoughts from a few seconds ago is lost to the ether.

This is a major cognitive ‘unhobbling’ for the AIs, but it also completely kills ones of the main levers human developers had for controlling and interpreting the AIs – reading the chain of thought scratchpad – making the blackbox even darker.

Nevertheless, the deployed AI agents mostly have been behaving according to plan and proving very useful to people’s daily lives and especially white collar jobs. A lot of people’s work days basically consist of a conversation with their computer, where they explain what spreadsheet to make, or what report to read and extract a summary from, or what changes to make to a slide deck, reviewing and correcting things as they go. Their agent automatically listens in on their meetings, takes notes, and sometimes even pulls up relevant data or does quick calculations and interjects on the call to show it. The more you pay the more quality, memory and personalization you get from these agents. Some large companies pay tens of millions of dollars a month for their company-wide AI agent plan. The average enterprise user pays around $3K/year, and the largest AI B2B company has nearly 100 million enterprise users (that’s $300B enterprise-only annualized revenue).

While direct AI company revenues have reached 1% of the world economy, the true degree of automation and transformation of the economy has been larger. For many of the tasks that AI is used for (like creating a powerpoint, researching a particular topic, making a spreadsheet), it is miles more cost-efficient. Recall, the average ‘enterprise-plan-AI-Agent’ has a salary of $3K/yr which is $1.50/hr full time wage, but on many tasks, it is matching what humans used to get paid anywhere between $15/hr and $150/hr to do. So in terms of the 2024 economy, AI is 1% of the GDP but has automated more than 10% of what the economic tasks used to be. With this unfolding over 4 years there has been a significant reorganization of the economy as a result. Many people have been fired, many people have remained but become more productive, and many people have been hired in totally new roles.

The net effect on unemployment has been minimal, it is up from 4% in 2025 to 5% and labor participation is down -2% to 60%. So overall % of the US population with a job is down from 58% to 55%, which is not a crazy break from the long run trend but nonetheless AI job loss is starting to be a memetic social issue.

Even though the actual effect has been tiny (1% on unemployment) the turnover rate has been very high. Something like 8% of Americans have been fired from a job because of AI in the last 4 years (many of them now work a lower pay, lower skill job) and something like 5% of people have now counterfactually gotten a job in a new AI-driven industry (like datacenter buildouts). The 8% that lost their jobs because of AI make a lot more noise, and take up a lot more media air-time than the 5% that are happy with their new AI-driven jobs.

Other major issues:

• Cybercrime losses in 2028 are up almost 10x from 2024 to $100B
  • FBI, 2024: “The 2024 Internet Crime Report combines information from 859,532 complaints of suspected internet crime and details reported losses exceeding $16 billion—a 33% increase in losses from 2023.”
  • 2024->2028 averages 60%/yr increase, leading to $100B cybercrime losses in 2028.
• There are some very scary demos of AI use in biological and chemical applications but no strong regulation or government intervention yet
• ‘AI slop’ is rampant on traditional social media, and new AI specific social media apps have been forked. This has captured renewed backlash of parents who are even more displeased with it than they were in the late 2010s with normal social media, with the difference that unlike Facebook or Instagram – use among adults of AI socials is extremely low.

• That’s not to mention the more subtle popularity of AI friends, companions, girlfriends/boyfriends and erotica/porn, which are becoming more and more salient to parents and the general public.

  

  Source

Overall 4% of Americans mention AI when asked what the most important issue facing the country is, around 10x higher than the common ~0.5% rate in 2025. Now viewed as being as important as issues like race, democracy, poverty, and healthcare in 2025.

Jan-Jun 2029: National AI Grand Strategies

It is now fair to say that the US and China both have cohesive national AI strategies. China’s advantage is that they are energy rich and manufacturing rich. The US advantage is that they are capital rich and compute rich. Both are increasingly talent rich and data rich in different ways (US has more AI agent data and China has more robotics data).

[Note: I have since updated that the gap in robots will be more like 4-10x]

China’s strategy as the energy and manufacturing rich nation is to double down on its advantages, reaching absurd scales of electricity generation and robot manufacturing, and to make a long term bet on cost-efficient compute production once they crack advanced photolithography and can do a chip manufacturing explosion powered by robotics. The majority of government capital is therefore not going towards subsidizing domestic AI chips, but towards SME R&D and this is starting to pay off with promising early EUV prototypes.

JD Vance has just been sworn in to office after an election cycle where AI was a major talking point. The Republican party line has pretty much maintained its current form, treading a fine line between the tech-rights’ techno-optimistic pro-innovation beat-china-ism and MAGA’s growing anti-AI social and job loss sentiment. The strategy that has crystallized out of this contradiction is to try to preserve laissez faire-ism for AI companies and then to worry about running around applying patchwork solutions for social issues later.

The US has been struggling with power expansion and high-skilled construction and manufacturing labour. Some of the 2025-era policies against solar and wind expansion (the technologies with the easiest manufacturing process to quickly scale), have come back to bite them, and this is hard to overcome, with multiple steps of the supply chain (e.g., polysilicon) being pretty much entirely controlled by China. Hundreds of thousands of acres of potential solar farms that could have been greenlit across America’s sunbelt states are getting a trickle of overpriced solar panels from Chinese companies. Natural gas turbine manufacturers are scrambling to increase production but their plans are made with 2-3 year lead times and they have continued to underestimate AI demand. In 2028 US AI companies build more compute capacity abroad than on US-soil.

In 2029, the military applications of advanced AI are becoming more salient, so the companies are all increasingly in cahoots with defense contractors and the DoD. Through these interactions and collaboration, there is mounting evidence that Chinese espionage on US company datacenters is happening at a much higher rate on datacenters abroad than those on US soil.

Jul-Dec 2029: Early Household Robots

Household robots enter their 2025-Waymo era.

In 2025, driverless Waymo cars had been all over San Francisco for a while but the rest of the world barely knew about it. They were also very expensive (around $250K per car), and were gradually expanding to other US cities. In 2025, China also had multiple robotaxi projects that are also operating at similar scale (Baidu’s Apollo Go was at 14M cumulative public rides by August 2025, vs. Waymo’s 10 million by May 2025).

In 2029, pretty much the exact same thing has happened with household robots. There’s around 10K expensive household robots in SF homes, and ten times more of them in China (where they are around 3x cheaper). There’s a visceral feeling of ‘the future is here’ that some people get when they visit a friend in SF and see these robots in a house for the first time (much the same feeling that people had on their first Waymo ride), but after sending a couple videos home to friends and family, the novelty wears off fast and it’s not a big deal in most people’s minds – the popular AI discourse revolves around social issues (AI media and AI relationships) and job loss while the robots creep in to more and more homes and applications. On the point of popular AI discourse, the administration passes a wave of very popular restrictions on certain forms of AI relationship and media platforms and creates incentives against AI-firings to help appease the masses, while the AI companies keep getting all kinds of red tape cut in other areas.

Jan-Jun 2030: Where are the Superhuman Coders?

AIs are now competently doing multiple-hour-long tasks in the economy, helping people significantly with their jobs, so whatever happened to their disproportionate coding skills in 2025? Why haven’t the AI companies hit the point of full coding automation?

METR’s coding time horizon trend has averaged a 6-month doubling time, since early 2025, meaning that frontier AIs now actually have 1-work-month 80% reliability time horizons on a theoretically extended version of METR’s current suite – but METR now have a new suite that reflects the distribution of real-world coding tasks much more closely. In particular, this suite has better coverage of the ‘engineering complexity’ and ‘feedback loops’ gaps not well-represented in the early-2025 version of the benchmark.

On this new suite, the best AIs only have 8 hour 80% reliability time horizons, and the doubling time is around 8 months. These AIs are powering pretty extreme levels of entry level software engineering automation, in fact, they almost function like an unlimited source of entry-level software engineering interns. But higher skilled software engineering for high-stakes jobs and high-complexity jobs like optimizing training runs or product-deployment PRs still require a lot of human-time, at the very least checking the AI’s code, and in many sensitive cases, it’s still more productive to just code yourself from scratch. Nonetheless, the rapid completion time of a lot of the code at AI companies is providing a 40% overall AI R&D speedup to AI companies.

The main reason coding progress hasn’t been faster is just that it has been hard to train AIs at scale on long-complex tasks, because it has been hard to automate a good feedback signal or generate human data at scale cost-efficiently, and in high-complexity, low-feedback loop coding tasks the AIs haven’t been generalizing much beyond the task lengths they get trained on.

Jul-Dec 2030: Scaling AI bureaucracies

In 2028 architecture changes to enable neuralese and recurrence were the algorithmic frontier.

In 2030, now that AI agents can string together increasingly longer tasks, the frontier is in making these AIs coordinate efficiently as ‘hive-minds’.

There were already multi-agent scaffolds in 2025, but when an AI can only do short tasks reliably, you don’t get a big boost from delegating many different jobs in parallel, as you quickly become bottlenecked on reviewing what the spun-off copies did. Now that the AIs are more reliably doing longer tasks, there is an ‘AI bureaucracy overhang’. If you have a week-long job to do, an intelligent AI multi-agent scaffold might be able to slice up the problem in parallelizable chunks, and with shared memory and other coordination optimizations, these mini AI companies might get it done not only faster, but qualitatively better, with each subagent being able to focus on a specific subtask.

In 2025, you could pay $200/month for ‘Pro’ versions of a model that did pretty basic best-of-10-attempts-type scaffolds, which were a little smarter than the $20/month versions. Now there are $2,000/month versions of models that spin up very compute intensive shared-memory AI bureaucracies, with up to 100 subagents working in parallel and coordinating on different aspects of the task you gave them. To work reliably at one-week long tasks, especially complex ones, they need the human to stick around and oversee their work and provide a lot of intermediate feedback, but these bureaucracies enable the continued growth in economic usefulness and AI revenue to continue.

Jan-Jun 2031: Domestic EUV

China now has mass production of 5-nm and 3-nm wafers with domestic EUV machines and High-NA EUV prototypes (8 years behind ASML, now crossing the lithography tech tree at 4x their pace).

China has translated the last 4 years of domestic DUV independence into a massive domestic DUV wafer production capacity, around 10 times bigger than what TSMC’s 7nm capacity ever reached before they moved on to better nodes, and had also been able to build up some inefficient 5nm capacity by pushing the limits of multi-step DUV techniques. Now with EUV, they are able to rapidly get a 3nm fab online, and scale up 5nm. In terms of raw <=7nm wafers, they have passed the west, but in quality-adjusted performance terms, they are producing 2x less due to the majority of the western supply chain production being <=2nm. In the last 6 years, that gap has come down from 10x less quality adjusted production, meaning that naively they are closing the gap at an average rate of 30% per year – meaning a naive extrapolation has them passing the western supply chain in quality-adjusted compute production within 2.5 years.

Jul-Dec 2031: The Magnificent Four

There are four US companies that have emerged as the major AI winners, and their combined market capitalizations have passed $60T, with combined earnings of around $2T (making their average PE ratios around 35). Two of these Magnificent Four companies were already in the 2025-era Magnificent Seven (Apple, Microsoft, Alphabet, Amazon, Meta, Nvidia, Tesla), my best guess is that these will be Alphabet and Nvidia, but the other 5 out of 7 are not able to fully capitalize on the last 6 years of AI-driven growth, and their growth over the past 5