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AI eats the world (Spring 26) [pdf]
- throwaw12 5mo ago> What happened the last time that everything changed? * Hardware era (pre 1995s) -> IBM, Intel, Microsoft, Apple * Internet era (1994-2001) -> Amazon, Google, Meta, Salesforce * Mobile era (iPhone+ era) -> Uber, Mobile Games, Youtube, Snapchat, Tiktok, Airbnb * Cloud era (AWS+ era) -> AWS, GCP, Azure, Snowflake, Databricks and bunch of other data & database startups AI era (ChatGPT+ era) -> Change is inevitable
- jaccola 5mo ago[dead]
- MyHonestOpinon 5mo agoNice breakdown! I would separate the Hardware era between Mainframe era and PC era. I would extend Internet era a bit more, Perhaps 2007 when the IPhone was released. Edit: I hadn't seen the original presentation yet. I see that Evans already divided the eras like I suggest above.
- hennell 5mo ago? That appears to be arbitrary eras then arbitrary companies from that era. Do you think Amazon and Google disappeared after 2001? Do you think databricks is now bigger than IBM? Change might be inevitable, but I'm not sure your list shows or proves that.
- AlecSchueler 5mo ago> Do you think Amazon and Google disappeared after 2001? I don't think that was implied at all, just that the context of the web is what allowed those companies to pop up.
- benedictevans 5mo agoWith each platform shift, some of the old players disappear and some of them become irrelevant - IBM is still with us but no one cares
- throwaw12 5mo agoWhat I wanted to say is every era gave birth to something big. AI era will get its own winners, but there will be some new big players as a result of this era I think
- percivalskunk 5mo ago[dead]
- charlesholloway 5mo ago> * Internet era (1994-2001) -> Amazon, Google, Meta, Salesforce Meta, née Facebook, wasn’t started until 2004.
- 2817635 5mo agoDidn't Ben Evans previously shill for bitcoin, which is now omitted in the graphs for "disruptive technologies"? This is a marketing Gish Gallop talk that pretends to invalidate counterarguments with a couple of fantasy graphs.
- benedictevans 5mo ago[flagged]
- tovej 5mo agoWhy do you need this persons name?
- adamtaylor_13 5mo agoThe implication is that it's a bot saying this, not a person.
- tovej 5mo agoDoesn't seem like a bot, and even if it were, the critique is germane. Calling for a name is a little threatening.
- benedictevans 5mo agoLooking at a 80 slide deck and saying that the charts are 'fantasies' is not a germane criticism at all. it's handwaving.
- tovej 5mo agoOne of the graphs has two series: net revenue for one company, gross revenue for another. Absolutely ridiculous. And that's just one example. You also haven't adjusted for inflation in your graphs that span multiple decades. Not to mention that the graphs themselves are not related to what you're discussing most of the time. You're just pointing at random historical developments and seemingly claiming they imply something for AI. They don't. Also you don't name your sources. You just say "Companies" for most of them. Or a single name. Ridiculous. Those are not sources. You should identify the documents. This is incredibly low quality work. A college freshman would do better.
- btucker 5mo agoYou can find the 4 versions of Benedict's deck here: https://www.ben-evans.com/presentations https://www.ben-evans.com/presentations I appreciate the temporal view into this thinking. My interpretation: Nov 2024: Don’t dismiss this; it may be the next platform shift. But the actual questions are still unsettled: scaling, usefulness, deployment, and business model. May 2025: The model layer is already showing signs of commoditization, so the important question shifts toward deployment: products, use cases, UX, errors, and enterprise adoption. Nov 2025: The capital cycle has become the story: everyone is spending because missing the platform shift is worse than overbuilding, but there is still no clarity on product shape, moats, or value capture. That creates bubble-like dynamics. May 2026: Provisional thesis: models look likely to become infrastructure, while value probably moves up-stack into apps, workflows, product, proprietary data/context, GTM, and new questions made possible by cheap automation. But he is still explicitly calling this provisional.
- flossly 5mo agoI think that DeepSeek may be important to that. They have a really good model that's open source, raising the bar for all other players: how good your model needs to be so you can make meaningful money on it (better than DeepSeek). Same thing happened on other places the open source offering became popular.
- dist-epoch 5mo agoWhat good is an open-weights DeepSeek model if you have nowhere to run it? OpenAI / Google / Anthropic / XAI also have a ton of compute. That is the real moat.
- amanaplanacanal 5mo agoThat seems pretty temporary if people can just build more compute.
- nmfisher 5mo agoantirez running (quantized) DeepSeek V4 Pro on a Mac Studio M3 Ultra with 512GB of RAM: https://bsky.app/profile/antirez.bsky.social/post/3mlzwmvlov22r https://bsky.app/profile/antirez.bsky.social/post/3mlzwmvlov... It's much closer than you think. We're going to see specialized hardware in the next 24 months capable of running 2025-era frontier models. That's big.
- deleted 5mo ago[deleted]
- brainless 5mo agoIf coding is such a big part of LLM agents' usage at the moment, I do not understand how far the best models will continue to shine and take the largest chunk of revenue. I am far away from tech hubs but I think better harness will utilize smaller models for more constrained, efficient and reliable coding agents. In a way this is like distilling (but it is not) but you can make better harness (tackle more edge cases, better tool/function definitions, sandbox handling, bash management, DB management, deployment management, etc.) but extracting what LLMs know into code. Maybe I am wrong but I would like to see custom software for the last mile (tiny/small businesses) becoming a reality. AI would eat the world of software but costs would go down since you can extract value upstream from the LLMs and spread downstream through tighter coding agents. I am building a coding agent that will not be small - it will be a lot of code, carefully mixed roles (mimic a software dev shop) with separate tools available to different roles. And all this code is generated by other coding agents. https://github.com/brainless/nocodo https://github.com/brainless/nocodo I am a nobody from nowhere with 18 years of software engineering behind me. I do not care about revenue. I just want to see a regular business owner's workflow going live on their own VPS.
- gyb997 5mo agoexcellent work！
- dwa3592 5mo ago>>Companies report ‘annualised’ revenue, defined as sum of previous 4 weeks multiplied by 13. why is it multiplied by 13?
- jaccola 5mo ago52/4 = 13
- Calc13 5mo ago13*4=52 weeks, mostly
- neogodless 5mo ago52 weeks / 4 weeks = 13
- deleted 5mo ago[deleted]
- briodf 5mo ago28*13=364
- deleted 5mo ago[deleted]
- deleted 5mo ago[deleted]
- tedd4u 5mo agoYeah it's weird huh? The "average" month contains 4.35 weeks. (365/7)/12 = 4.3452…
- dwa3592 5mo agothis took a bit of a mathematical turn because of my poor phrasing. what i was actually intrigued by was how does revenue of 4 weeks become "annualized" by just multiplying it with 13.
- ncruces 5mo ago
- dist-epoch 5mo agoIt will literally eat the world. Just like we crowded out wild animals in a few reserved areas, so will AI data centers crowd us out. To quite Ilya Sutskever: > I think it’s pretty likely the entire surface of the earth will be covered with solar panels and data centers.
- zx0r23 5mo agoOr we could not do that... Technology is meant to serve us not drive us into a hellscape lol
- siwatanejo 5mo agoWhat's wrong with that? There are now materials that allow you to have solar panels on a window (so they are not opaque anymore), and we can put data centers under our feet.
- zx0r23 5mo agoDid you read the original comment? > AI data centers crowd us out. > the entire surface of the earth will be covered with solar panels and data centers.
- siwatanejo 5mo agoYes, so our buildings' windows will be solar panels, what's wrong with that? And all our floors can be data centers, what's wrong with that?
- gallerdude 5mo agoI was a baby when the Internet Revolution happened. I was in high school and college when the Mobile Revolution steamrolled everything. It’s been interesting to see this one, as an adult working in the world. I wonder how far it will go.
- stego-tech 5mo agoFurther than the doomers think, but not enough to pay off the investors of the original boom. I say that as someone who has been an early believer in the internet (first website in the 90s), mobile data (slurping down the 'net, IRC, and IMs via EDGE data), smartphones (N80ie), streaming media (RIP Windows MCE), the list goes on. Models were always going to be the commodity, just like the most popular and viable use cases at present are less job-replacement than "let's analyze huge data sets for patterns we're missing, and adjust accordingly" or "probabilistically generate deterministic software for me for X function/task". One-offs simply aren't profitable when models are interchangeable commodities, hence that brief attempt to pivot to "pay by outcome" before giddily embracing the classic consumption-based-billing playbook.
- pjc50 5mo ago> Further than the doomers think, but not enough to pay off the investors of the original boom Not an uncommon event - not only did this happen to many companies who were big in the original internet boom (e.g. Sun Microsystems, as well as all the Boo, Pets.com etc), it also happened to the railway boom of the previous century, and even the Channel Tunnel.
- aworks 5mo ago"Chat is a terrible UX General use needs ‘apps’" I'm old so my computer career has gone: punch cards => calculators => command-line => GUI => touch screen => voice => chat. Chat seems to be the best blend of expressiveness and utility, with a dose of magic thrown in.
- Michael666 5mo ago[dead]
- kannanvijayan 5mo agoThis is a reasonably well-examined take of the situation. On the technical side, one of the additional things I've had on my mind is the potential that these mega models are in fact hiding a ton of inefficiency. The approach of simply shoving higher dimensionality and more parameters into largely tweaks to the current models has delivered results, but it feels like "mainframe" era of computing to me. Throwing reams of annotated human content and forcing the machine to globally draw associations from it feels clumsy. Just as people are able to learn structured knowledge via rule-systems that are successively elaborated with extensions and situational contradictions, I feel like there's probably a much more compact representational model that can be reached by adapting the current technical foundations (transformers, attention, etc.) to work well with generated examples from rule-systems, that then gets used as a base layer to augment the "high level" models that process unstructured data. The risk for the behemoth datacenter might be similar to the risk in the early computing era of building compute centers right before the PC revolution took off. If it turns out that there exists some more compact and efficient representation for this intelligence (which IMHO is likely given that we are still in the first generation of this technology), the datacenters may end up decaying mausoleums of old tech that has no relevance to a distributed intelligence future. That's the big technical unknown unknown for me. How much efficiency juice is there left to squeeze, and what does that mean for a distributed landscape vs a centralized datacenter based landscape.
- jkhdigital 5mo agoRight, the crazy thing is that much of the groundwork for the “rules-and-heuristics” mode of AI was laid down in the 70s and 80s, long before we had the raw compute power to reliably extract patterns from reality-scale inputs. Those early efforts failed miserably mostly because the rules had to be populated manually and in a ridiculously space-inefficient format (compared to the density of information in model weights). So yeah, the next stage is models that basically do what humans do: encode causal models of the world in a composable, symbolic form that can be falsified and refined through interventional experiments.
- kannanvijayan 5mo ago
- ocimbote 5mo agotl;dr; > "What happened the last time that everything changed?" Honestly, I'm glad we hear more of the commoditization of AI, and I hope that the comparison of AI with water or electricity will become mainstream and that the states (as in nation states) will understand that sooner rather than later and act accordingly.
- turtlesdown11 5mo agoLots of quotes from Mark Zuckerberg, not a lot of Zuckerberg quotes on the $80b invested in the metaverse
- benedictevans 5mo agoI had that exact chart in a previous presentation.
- Mithriil 5mo agoThe ratio of AI startups at YC surprised me... (slide 48). This is a clear trend.
- aurareturn 5mo agoIn slide 22, it compares LLM labs (OpenAI/Anthropic) to mobile data telecoms (AT&T, Verizon, TMobile) in 2010s. The difference is that mobile telecoms follow a standard (3G, 4G LTE, 5G) and there is little to no differentiation. It's virtually the same no matter which company you choose or which country you travel to. A better comparison is actually AWS/Azure/Google Cloud/NeoClouds to AT&T and Verizon. The data centers follow a standard (CUDA/PyTorch/etc.) while OpenAI and Anthropic are becoming more like iOS and Android. Both the clouds and telecoms had to spend a ton of capex to build out infrastructure first. Because of what I think is a poor comparison, the the next few slides make the wrong conclusions. For example, it thinks that models will be a commodity like 5G data. I disagree. I think frontier models are a classic duopoly/monopoly scenario. The smarter the model, the more it gets used, the more revenue it generates, the more compute the company can buy, the smarter the next model and so on. It's a flywheel effect. This is similar to advanced chip nodes like TSMC where your current node has to make enough money to pay for the next node. TSMC owns something like 95%+ of all of the most advanced node market. Back in the 80s and 90s, you had dozens of chip fab companies. Today, there are only 3. There should only be 1 but national security saved Intel and Samsung fabs. There is evidence that the Chinese models are falling further behind, not gaining. Consolidation will likely happen soon because many unprofitable open source labs will have to merge and focus on revenue generation.
- benedictevans 5mo agoI've made the semi comparison myself, but the amount of capital required to build a SOTA model today is clearly nowhere near enough to lead to a monopoly. I'm aware that telecoms networks are standardised (I was once a telecoms analyst), but that isn't a precondition for a commodity.
- aurareturn 5mo agoJust like how starting a chip fab was relatively easy back in the 80s and 90s. There were dozens of chip fab companies in the 80s. It turns out that fabs follow Rock's Law which is that the capital cost to build a new fab doubles every 4 years. This means it will quickly get rid of the less competitive players. This is not dissimilar to the LLM scaling laws where you need a magnitude more compute to get unlock a new tier of intelligence. Today, Anthropic and OpenAI are clearly in the lead for models and then there is everyone else. Google is a close 3rd. No one else is challenging them anymore in SOTA models. Some models might beat them in one or two benchmarks but none can compete overall. I expect this gap to grow bigger as models cost more and more to train.
- camillomiller 5mo agoWhat in the AI slop is the Yogi Berra “AI predications” duplicate slide?
- benedictevans 5mo agoThere are no duplicate slides.
- littlexsparkee 5mo agoI believe they're referring to the quote showing up on 50 and 51 but that appears to be intentional (transition adds question below)
- camillomiller 5mo agoBen, I follow you and think you’re brilliant, but boy you can’t take feedback like ever. Duplicate slide is there clearly to add the question, but it has a glaring typo—- “predications” instead of “predictions”. A random internet stranger read to page 51, which is already a rare occurrence, and helped you find a typo that you can now edit. But sure, the answer to that is “your comment is wrong”.
- benedictevans 5mo agoAh, that’s different. Your question confused me because you said they were duplicate slides and they are not, and your word ‘predication’ looked like a typo by you.
- paigerank 5mo agoThe slides say "predication" and not "prediction." Just a typo :) I think the AI slop comment was excessive, and I liked your deck, but there is indeed a typo you could fix!
- arexxbifs 5mo ago> Imagine asking “What will be changed by the internet?” in 1997 Pretty much all of the stuff that was suggested back then or earlier: Shopping, advertising, video conferencing, collaboration, software distribution, media consumption, banking, finance and of course communication overall. Most of these ideas weren't exactly new in 1997, but go back to services like CompuServe and even Douglas Engelbart's Mother of All Demos. The bottlenecks were bandwidth and personal computer performance (both of which were then predictably following Moore's law), not human imagination. A few examples that a lot of people correctly extrapolated from: NLS (1968), PictureTel (1987) and later LiveShare, IndyCam (1993), CUSeeMee (1995), RealAudio (1995), RealVideo (1997). Perhaps the core business problem with LLM:s isn't finding a product-market fit, but that our imaginations have been running wild with expectations on "AI" since at least the 1950s, and now we have something that quacks - but doesn't quite walk - like a duck.
- thrance 5mo agoKnowing why we're trying to build something is a good smell test to segregate promising tech from snake oil, in my experience. Take quantum computers for example, a lot of the time people will compare that to the dawn of classical computing, with claims such as "we can't know yet what we'll be able to achieve, we have to build it first!". Except that even the first classical computers were built with goals and applications in mind. Turing's was to decrypt Nazi codes, for example. Instead, when asking a quantum computing company what they're trying to achieve, they'll gesture vaguely at "chemistry, finance, ecology".
- aleph_minus_one 5mo ago> Except that even the first classical computers were built with goals and applications in mind. [...] Instead, when asking a quantum computing company what they're trying to achieve, they'll gesture vaguely at "chemistry, finance, ecology". I think the problem is a little bit more subtle: To finance a lot of innovations, better also some intermediate step towards the far goal should already be very useful, otherwise the company that builds it will go bankrupt. If this is not the case, it's typically not commercially viable, some product category is typically basic research (which is very important, but it typically means that the commercial potential will only come up in some future). There do exist problems where a quantum computer gives an extreme advantage in the sense that we have no idea how a fast classical algorithm could look like. So, the only viable approaches for these problems are: 1. work on a huge algorithmic breakthrough (to be able to solve these problems fast on a classical computer) 2. build a quantum computer What are these problems? They are basically all special cases of the abelian hidden subgroup problem: > https://en.wikipedia.org/w/index.php?title=Hidden_subgroup_problem&oldid=1352151693 https://en.wikipedia.org/w/index.php?title=Hidden_subgroup_p... In particular cf. the table at the end of this Wikipedia article: > https://en.wikipedia.org/w/index.php?title=Hidden_subgroup_problem&oldid=1352151693#Instances https://en.wikipedia.org/w/index.php?title=Hidden_subgroup_p... If you do have such a problem to solve, 1 and 2 are the only viable approaches. So, there do exist goals and applications for which a quantum computer is insanely useful (assuming no huge algorithmic breakthrough happens). The questions are thus: - Is the abelian hidden subgroup problem sufficient for being able to carry a whole potential industry? - (To come back to my introduction) What use does a quantum computer that is only capable of solving very small instances of this problem have for the user?
- pards 5mo ago> Automation takes a lot of manual labour That's a great quote. https://xkcd.com/1205/ https://xkcd.com/1205/
- jp57 5mo agoWait. There were 10000 elevator attendants in the USA in 1990?
- rootsudo 5mo agoThis is excellent all around.
- seesawtron 5mo ago>"We see a future where intelligence is autility like electricity or water and people buy it from us on a meter” Sam Altman I always considered jokingly that I am "selling" my intelligence when I work for a company. This clarifies that my perception wasn't far off.
- Viliam1234 5mo agoHopefully you are renting your intelligence to the company, not selling it. (Unless the job gets you burned out, in which case it was selling indeed.)
- reserve 5mo agothank you for sharing. pdf really great!