{"slug": "on-computers", "title": "On Computers", "summary": "The computer-brain industry (CPU, memory, accelerators) grew from a $15B niche in 1984 to a $350B heavy industry in 2025, nearly doubling from $200B in 2020, driven by AI boom datacenter construction. This explosive growth is expected to continue, reshaping where value accrues for infrastructure founders and application builders.", "body_md": "[← Blog](/blog)June 21, 2026 · By\n\n[Keith Adams](/team/keith-adams)\n\n# On Computers\n\nAbstractA walk through the computer-brain industry (CPU, memory, accelerators) from a $15B footnote to a $350B heavy industry in 2025. Its recent, and we speculate, near-future doublings change where value accrues for infra founders and application builders alike.\n\nKeywordscomputing, semiconductors, AI, datacenters, investing\n\nEver since my dad brought home an Apple IIe in 1984, I've loved\n*computers*. Not computer science, or computer programming (though\neventually I'd come to love these too): the power-sucking, pixelated,\nthrumming physical gadgets. I don't love them because they are\nuseful or intellectually stimulating, but because they are intrinsically\nfun machines. I like it when they feep; when they chime at power\non; when their fans run, heating up the room; the ever-changing\nacoustic and tactile feedback of their keyboards; and the clunking\nand screeching and clicking of their storage devices, though there\nis [less of that](https://www.amazon.com/dp/B00RXEWOAA) in these\nsolid-state days. Computers are just *cool* to me, the way muscle\ncars or mechanical watches are to their enthusiasts.\n\nFor most of my life, this fascination has been wholly disconnected\nfrom the role these physical machines play in the economy. Even though\ncomputers impact a bunch of important industries (software, IT services,\nthe Internet, etc.) the computers *themselves* have been kind of\nmarginal in capitalism's grand parade. It can be tricky\nto decide what counts exactly as *computers* as distinct from semiconductors,\npersonal electronics, and other categories more cleanly captured in economic\nstatistics. For my purposes, I think of the \"physical compute substrate\"\nas being composed of CPUs, RAM, and accelerators where applicable (lately,\nof course, GPUs). These three are often the principal components of what\nproblems a given computing device is fit to solve.\n\nAs the 2022 vintage AI boom has progressed, leading to ever more financial heroics in datacenter construction, my childhood notion of this \"core\" segment of the hardware market as a niche industry has been feeling ... off. Financed with sophisticated combinations of equity and debt, compute spend is driving multiyear funding plans that have edged the margin-rich and asset-light hyperscalers of yore to look more like classic heavy industries than the cottage industry I came of age in. But I am in AI-besotted San Francisco near the peak of a market cycle, and our intuitions can deceive us in moments and places like this. What do the numbers show? And how will they change, to the extent we can foresee?\n\nTrying to guesstimate the revenue of a particular slice of an industry\nlike this always involves some guessing, but between\nEpoch AI's epic chip sales dataset, WSTS's industry billings,\nand a stack of Intel, Nvidia, and AMD 10-Ks,\nwe can put together a guess that computers *qua* computers were\nabout a $15B 1 industry all-in in\n1984\n\n. To give some sense of scale, that is about the size of Major League Baseball. MLB is, of course, considerable, and irreplaceable to its aficionados. But it is also not a lynchpin of global commerce. I believe the US DoW has no contingency plans to wage war against the Dominican Republic should it disrupt the baseball talent supply, for instance.\n\n[2](#user-content-fn-methodology)When I grew up and joined the workforce of software engineers in 2000, the computer brain industry had grown up to about $90B in revenues; about the revenue of global distilled spirits.\n\n2020, the last time I drew a paycheck as a software engineer, it reached about $200 billion. About the global pet care industry.\n\nBut then in 2025, it nearly doubled to $350 billion. (Global cement.) And we have every reason to expect this explosive growth to continue, given the hyperscalers and the frontier labs' commitments to compute spend over the foreseeable future.\n\n| Year | Revenue (2025 USD) | Biggest slice | Comparable industry |\n|---|---|---|---|\n| 1984 | $15B | memory (DRAM) | MLB |\n| 2000 | $90B | CPUs and memory | Spirits |\n| 2020 | $200B | memory, CPUs, and GPUs | Global pet care |\n| 2025 | $350B | datacenter AI accelerators | Cement |\n| 2026 (est.) | $700B | AI accelerators and HBM | Cosmetics |\n\nThe vibeshift around compute, then, is not wholly illusory. The economy around the physical substrate for the computing revolution has spiralled up in size from Major League Baseball to cement. If it should double again, as seems very likely in the next year or two, it will be comparable to the global cosmetics industry. Another doubling from there would put it in the heady air of the advertising and apparel industries, among the largest on Earth.\n\n## So what?\n\nSo, computing machinery is *bigger* than you think it is, and getting bigger\nfaster than you can update your intuitions about how big it is.\nAs technologists, we at Pebblebed find these machines fascinating. But we're also\ninvestors, and with our fiduciary hats on, we're compelled to ask \"[So\nwhat?](https://now.fordham.edu/fordham-magazine/tribute-don-valentine-silicon-valley-pioneer/)\"\nOK, computers are blowing up. How do we act on this information?\n\n### Infrastructure Software\n\nOne consequence of computing moving up an order of magnitude is\nthat *infrastructure software*, loosely defined as software that\nexists to unlock latent capabilities in the hardware, can create\nproportionately more value. When we were both at Facebook, my partner\nPamela Vagata once earned a coveted piece of corporate swag with\nan understated patch casually boasting \"$1B SAVED\", with a graphic\nsuggesting the transition from exponential decay to exponential\ngrowth. She saved the company over $1B by inventing the [ORC file\nformat](https://orc.apache.org/docs/), which radically improved\nthe storage and compute efficiency of many critical workflows.\n\nBack in 2013 when she was doing this work, computing was too small to support a venture-scale outcome for a company driven by these kinds of insights. But in the context of 2026 hardware budgets, a comparable feat of invention and technical derring-do might easily save $10B, or more. \"Savings\" of this order of magnitude aren't best modeled as cost reduction, but as unshackling the company to face enormously more ambitious projects. Capturing even a small fraction of the value created in this way can lead to outcomes that would have been historic 10 years ago.\n\nIn the Pebblebed portfolio, we focus our infrastructure investing close to\nthe hardware/software interface. We believe that unlocking inefficiencies\nand operational ease at this layer is going to explode over the decade\nahead. [Cedana](https://cedana.com/), which allows GPU jobs to be migrated,\nincreases neoclouds' revenue per MW. [Northflank](https://northflank.com/)\nprovides the undifferentiated heavy-lifting that has to happen to turn raw\nk8s running on your cluster into a usable, observable, resilient system.\nAnd [Lemurian Labs](https://www.lemurianlabs.com/) are building the modern\nanalog to the Java Virtual Machine, bringing write-once/run-anywhere to\naccelerated compute.\n\n### Application software\n\nThe boom in AI compute is radiating out into the larger economy in\nvarious ways, and we are starting to see its impact in our application\nsoftware as well. [Build](https://build.inc) makes AI for the built\nworld. In product terms, they build AI capable of performing\npreviously-labor-intensive information-gathering processes in commercial real\nestate. From their start, Build has made hard choices to prioritize serving those\ndevelopers who are building datacenters. This was not an entirely\nconsensus perspective at the time of our investment a year ago, but\nit has panned out even better than all but the most ardent bulls\nwould have predicted so far.\n\nThese are our perspectives to date. We don't have any glib conclusions, and will not know with precision how this all turns out for a while. Computing has gotten much, much bigger, and sheer momentum guarantees that will continue for a while. Anyone offering firmer conclusions than this is probably epistemically overconfident. I feel grateful to my younger self for finding these unusual machines so compelling.\n\n## Footnotes\n\n-\nAll dollar figures are inflation-adjusted to 2025 USD equivalents unless otherwise noted.\n\n[↩](#user-content-fnref-inflation) -\nWe are counting the merchant market for the three things I'm calling a computer's brain: CPUs; memory (DRAM, with a little SRAM); and compute accelerators crunching numbers beside them (the discrete FPUs of yore, and GPUs, inclusive of datacenter GPGPUs). With the help of my friendly\n\n[local AI](https://claude.ai), I have tried to tally the worldwide spend in dollars for each chip, then used CPI to turn them into 2025 dollars. Recent accelerator figures lean on[Epoch AI's chip-sales dataset](https://epoch.ai/data/ai-chip-sales). The rest is assembled year by year:- 1984:\n- CPUs: a\n[Dataquest worldwide table](https://archive.computerhistory.org/resources/access/text/2013/04/102723388-05-01-acc.pdf), checked against[Intel's 1984 annual report](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/2025-05/history-1984-annual-report.pdf) - DRAM: the same\n[Dataquest table](https://archive.computerhistory.org/resources/access/text/2013/04/102723388-05-01-acc.pdf)(MOS-memory line), plus[USITC anti-dumping filings](https://www.usitc.gov/publications/701_731/pub1862.pdf)\n\n- CPUs: a\n- 2000:\n- CPUs:\n[SIA](https://www.eetimes.com/sia-pegs-2000-chip-industry-growth-at-37-percent/)product-line billings, with[Intel's 10-K](https://www.sec.gov/Archives/edgar/data/50863/000091205701503434/0000912057-01-503434-index.htm)behind the figure - DRAM:\n[Gartner Dataquest](https://www.eetimes.com/dram-plunge-shuffles-top-10-chip-ranking-in-2001/), against the SIA billings - GPUs:\n[NVIDIA's 10-K](https://www.sec.gov/Archives/edgar/data/1045810/000101287001500492/0001012870-01-500492-index.htm)\n\n- CPUs:\n- 2020:\n- CPUs:\n[WSTS](https://www.wsts.org/67/Historical-Billings-Report)and[IC Insights](https://www.icinsights.com/news/bulletins/DRAM-Leads-In-Revenue-NAND-With-Top-Percentage-Growth-In-2020/), with the[Intel](https://www.sec.gov/Archives/edgar/data/50863/000005086321000010/0000050863-21-000010-index.htm)and[AMD](https://www.sec.gov/Archives/edgar/data/2488/000162828021001185/0001628280-21-001185-index.htm)10-Ks behind the figure - DRAM: the same\n[WSTS](https://www.wsts.org/67/Historical-Billings-Report)and[IC Insights](https://www.icinsights.com/news/bulletins/DRAM-Leads-In-Revenue-NAND-With-Top-Percentage-Growth-In-2020/)lines - Accelerators:\n[Jon Peddie](https://www.jonpeddie.com/news/global-add-in-board-market-8-8-billion-in-q325-with-a-cagr-of-0-7-to-2029/)for the gaming boards;[Nvidia's segment filings](https://www.sec.gov/Archives/edgar/data/0001045810/000104581021000007/q4fy21pr.htm)for the datacenter\n\n- CPUs:\n- 2025:\n- CPUs and DRAM: the same houses as 2020, plus\n[TrendForce](https://www.trendforce.com/presscenter/news/20251218-12843.html)for the high-bandwidth memory - Accelerators:\n[Epoch AI](https://epoch.ai/data/ai-chip-sales)\n\n- CPUs and DRAM: the same houses as 2020, plus\n- 2026 (estimates):\n- Memory:\n[WSTS](https://www.wsts.org/esraCMS/extension/media/f/WST/7310/WSTS_FC-Release-2025_11.pdf)and[TrendForce](https://www.trendforce.com/presscenter/news/20260122-12893.html)projections - Accelerators:\n[Nvidia's quarterly run-rate](https://www.sec.gov/Archives/edgar/data/1045810/000104581026000019/q4fy26pr.htm)\n\n- Memory:\n\nThe 1984 number has the spottiest sources. Back then memory, not logic, was the major revenue driver: in 1984 the world bought about $6.2 billion of memory chips but only $3.2 billion of microcomponents (CPUs, and the microcontrollers and glue that ride along with them), of which true CPUs were a sliver. So, dollar-for-dollar, the brain of that Apple IIe was mostly RAM. (Those figures come off Dataquest's 1984 worldwide table, scanned into the Computer History Museum's archive.) Also, an important judgement call: I am leaving out the processors in phones and tablets (which I file under personal electronics, vs. computers), and I subtract the high-bandwidth memory soldered onto the AI accelerators from the memory column, since it is already counted in the price of the accelerator and I'd rather not double-count. Memory revenue is also violently cyclical (DRAM can halve in a year), so any single snapshot is partly a story about where we happened to catch the wave. 2026 is half a forecast, riding a memory supercycle; please do not take it as more than one significant digit.\n\n[↩](#user-content-fnref-methodology) - 1984:", "url": "https://wpnews.pro/news/on-computers", "canonical_source": "https://pebblebed.com/blog/computing-is-bigger", "published_at": "2026-06-28 05:55:00+00:00", "updated_at": "2026-06-28 06:34:54.520571+00:00", "lang": "en", "topics": ["ai-infrastructure", "ai-chips", "ai-research", "ai-startups", "ai-products"], "entities": ["Epoch AI", "WSTS", "Intel", "Nvidia", "AMD", "Apple IIe", "Keith Adams"], "alternates": {"html": "https://wpnews.pro/news/on-computers", "markdown": "https://wpnews.pro/news/on-computers.md", "text": "https://wpnews.pro/news/on-computers.txt", "jsonld": "https://wpnews.pro/news/on-computers.jsonld"}}