I will just copy/paste the same comment I made in response to a person wondering why I reference spreadsheets whenever this topic comes up.

We aren’t quite to the endgame for the tech but it’s quite close.

VisiCalc didn't make mainframes smaller. It made the use case portable to a machine that was already cheap. I'm old enough to watch the industry pull off the same trick, four times now, over fifty years.

1979 — VisiCalc. Dan Bricklin watches a professor at HBS update a paper financial model on a chalkboard, has the idea, and ships the first electronic spreadsheet for the Apple II. The Apple II cost $2,000. The mainframe time-sharing services doing financial modeling — IDC, ITS, the rest — cost thousands a month plus per-CPU-second fees. Within twelve months businessmen are buying Apple IIs as "VisiCalc accessories" — they don't want a personal computer, they want the spreadsheet, and the cheap hardware comes along for the ride. The mainframe didn't get smaller. The demand for "I need a five-year cash flow model by Tuesday" got routed to a $2K box. Bricklin's own retrospective on bricklin.com is worth reading from the source.

1980 — 8087 math coprocessor. Floating-point used to be a mainframe / minicomputer thing. Engineering workstations from DEC, Sun, and Apollo sold a moat made out of fast FP and structural FORTRAN. Intel ships the 8087 — FP coprocessor for the 8086. I remember installing math coprocessors onto motherboards in the early 1990s. Eventually, the 486DX integrates floating point on-die.

Fun aside: one of my earliest gigs was soldering pins onto 486 "SX" CPUs en masse for customers when I worked out of a computer shop in Las Vegas. If you simply soldered some pins back on? You could enable the coprocessor. It made certain operations -- notably, video encoding for the dial-up-porn shop upstairs that hired us go dramatically faster.

By 1996 there's MMX, by 1999 SSE, but they didn't gain much traction in the consumer marketplace for a few years. FP goes from "expensive allocated resource you submit a job to" to "system call." Sun peaks in 2000 and is sold for parts to Oracle in 2010. Racks of minicomputers in datacenters didn't get smaller. The thing they were for — fast numerical work — moved to the cheap commodity Intel box.

1996–2009 — GPU eats SGI. Silicon Graphics sold $50K–$100K+ workstations to Hollywood, NASA, and the DoD, in addition to the small-time computer game studio I worked at at the time. SGI's moat was dedicated 3D silicon. Three SGI alumni — Sellers, Smith, Tarolli — leave in 1994 to found 3Dfx and put 3D acceleration on a $300 add-in card for PC gamers.

I owned every generation of 3dfx card through the Voodoo II, and used two in SLI mode religiously when "nvidia sucked".

It made me laugh when NVIDIA shipped GeForce 256 in 1999 calling it "the first GPU." Tedium's history of that moment is great. SGI's own people had pitched a PCI graphics card internally; it got killed because the margins were too low for SGI's existing customers. Textbook Christensen. By 2005 a $400 commodity GPU renders what an SGI Onyx farm rendered for Toy Story in 1995. SGI files Chapter 11 in 2009. The massive rendering farms didn't necessarily get smaller. The job moved to farms of the cheap commodity boxes, and an individual user with enough spare time could do similar things at home.

2024–now — AI coprocessor. Same pattern, fourth time. Every Copilot+ PC ships with 40+ TOPS of NPU. Apple Silicon has the Neural Engine on-die since the A11. Qualcomm Hexagon, Intel NPU on Lunar Lake, AMD XDNA on Ryzen AI. And then there's Taalas — hardwired Llama 3.1 8B at 17,000 tokens/sec on a single ASIC, claimed 1000× perf-per-watt versus an H100. Their pitch: two months from model weights to silicon.

With the recent drop of Gemma 4 31B from Google, you could cast the model into ASIC by end of summer. Near-frontier-class tool-using model capability in your laptop's coprocessor for the cost of metal mask layers, running thousands of times faster than on GPU.

The part that should make every API-tier AI subscription investor reach for the antacids: Apple just signed a multi-year deal with Google for Gemini at ~$1B/year. Same Google that published Gemma open weights, including small variants explicitly designed for on-device deployment. IMHO, Apple is less interested in paying for frontier API access than in licensing the substrate: a model family they can distill, quantize, and bake into silicon they already control, the way they baked H.265 into the Media Engine and FP into the FPU. ANE running a distilled-from-Gemini model on every iPhone in 2027 is the likely play.

The Christensen frame, The Innovator's Dilemma, talks about this in great detail (and is worth the read!): low-end disruption starts worse-than-incumbent on metrics incumbents care about (raw capability), better on metrics incumbents don't care about (cost, latency, locality, privacy, offline operation), and eventually crosses the "good enough" threshold on the headline metric. At which point the incumbents' margin structure becomes a trap. OpenAI can't run a $20/month consumer subscription business when the laptop and phone you already own do 95% of the work for free. They can sell frontier capability to enterprises that need GPT-6-class reasoning — that will probably remain a real business for some time — but it's a much smaller business than the consumer subscription thesis their valuation is built on.

Now, I know this endgame sounds like sci-fi. But I'm an old fart. I've traced the same trajectory repeatedly for half a century of lived experience in the IT business. I think within a few years, the internet stops being a content delivery network and becomes a prompt and context exchange. Tutorials, blog posts, recipes, how-tos, lookups — your local model generates them on demand from indexed/cached source material. What flows over the wire is the prompt, the citations, occasional fresh facts, the recipes for getting good output. The browser becomes a thin client for a local model that drafts most of what you read against material the model already has. Certainly not this year. Probably not next year. But the trajectory seems like it's following exactly the four cases above, and we're at the "wait, is the cheap box actually good enough?" moment that always precedes the demand curve flattening.