You can find the 4 versions of Benedict's deck here: 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.
I 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.
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.
So long as there is demand, there are always going to be providers competing to offer it at a low cost. My understanding is that the median price on there is in the ballpark of what it costs to run the inference. This is very different from e.g. Opus, which you can basically only buy from Anthropic at the price they set.
I think the original DeepSeek moment seemed important. And yes, the more recent model is good, but there are multiple. This commodification trend spans many different companies, including Kimi 2.5/2.6 and GLM5.1, and even Google itself with its Gemma models. There are a dozen models that exist at roughly the frontier from 6 months ago at 1/10th the cost.
I appreciate Evans’ work and wrote an “antithesis” (1) to the Nov 2024 iteration of this. Given the pivot to “models look likely to become infrastructure” I might want to update my take.
I didn’t know there were a sequence of these decks; thanks — it’s helpful to think of them as updating snapshots in time.
The main thing that stands out to me on these graphs is just . how . early we still are - looking at industries like legal which in my mind are certainly going to be massively disrupted, and seeing the very low usage rates vs. tech (which still shows less than a quarter of tech people using AI daily) — we are in for a lot more change than we’ve seen so far.
This 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.
Right, 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.
I feel like the talk about "world models" is trying to reach at that, but cast it in different terminology. World model is just domain model, and once you're at domain model, there are multitudes of domains.
Unsupervised learning over domain rulesystems has the potential to let us define really well-defined, scoped models that behave a lot more deterministically and don't colour outside the lines, and reserve their weights for cleanly modeling the domain associations and relationships that matter.
I just asked codex the following question in the middle of my coding prompt:
What are you thoughts on the relative strengths of ewoks vs jawans?
Answer:
• Ewoks are stronger in direct conflict. They are organized fighters, good at
ambushes, traps, terrain control, and coordinated attacks. On Endor, the beat
a technologically superior force by using preparation and local knowledge.
....
As amusing as this may be, I really have no need or desire for my coding model to understand or be aware of ewoks and their relative strengths compared to jawans. Nor do I need it to understand the nuances of the races of middle earth. And prompt response of "I have no idea what you are talking about" to all of these would feel reassuringly scoped.
Mixture-of-Experts seems like an attempt to do this - the domain structure being extracted into specific sub-models that are presumably trained on particular domain-associated content - but it feels like this is once again the beginnings of what is possible.
Nice 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.
? 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.
this 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.
Usually startups like to talk about Adjusted Annual Revenue in fundraising and other hype materials. There’s no regulation around this metric so whatever their investors are willing to accept is what they use. One way to measure it is to take the past 4 weeks revenue and multiply by 13.
There’s a bunch of fuzzy metrics here, which is one reason I turned it back into a monthly number.
The other issue (as you’ll see on the chart) is that Anthropic and openAI are recognising revenue in completely different ways.
I 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.
Further 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.
If 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
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.
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.
And then, if there is any data that you think is incorrect, or arguments that you disagree with, you should explain why. All of the charts are sourced, and none of them are 'fantasies'.
No it isn't. The implication is that pro-AI people can take revenge. He knows he is secure with his opinions. He even paraphrases Andreesen's title "software will eat the world". He has repeatedly appeared at a16z.
It is very secure to be pro-AI while the rest has to resort to unregistered typewriters like in the Soviet Union.
not to be too pedantic but sourced doesn't usually mean accurate. sourced can very well be fantasy. it will be a 'sourced fantasy' in that case or hallucination if you used a LLM.
Yeah, beyond this mumbo-jumbo non-answer of yours, did you or did you not push crypto? Because if you did...it could kind of not speak for your analytical competence.
It is an indisputable fact that you spent years shilling crypto. Why even deny that or threaten(!) someone pointing it out? It was/is a huge, verifiable chunk of your public output.
Unflagged as I don't feel this comment deserves to be flagged. The call for reposting with a real name is unnecessary though - if an internet comment is incorrect or overstates the case, just reply to correct it or ignore it.
> "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.
You can find the 4 versions of Benedict's deck here: 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.
I 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.
What 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.
That seems pretty temporary if people can just build more compute.
antirez running (quantized) DeepSeek V4 Pro on a Mac Studio M3 Ultra with 512GB of RAM:
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.
That specialized hardware will be scooped up by AI data-centers, just like RAM is today.
No more than Mac Studios. Datacenters need different hardware.
The 512 GB ram studio can't even be purchased anymore. It's been delisted
https://www.apple.com/shop/buy-mac/mac-studio
Same with the Mac mini. entirely removed from all store references
I just got into self hosting Deepseek v4 Flash on a single DGX Spark via antirez’s DwarfStar 4 project
It feels great to finally have access to something local.
It's quite expensive to self-host but you have many places to run it. OpenRouter alone lists a dozen different providers for DeepSeek 4 Pro. https://openrouter.ai/deepseek/deepseek-v4-pro/providers.
So long as there is demand, there are always going to be providers competing to offer it at a low cost. My understanding is that the median price on there is in the ballpark of what it costs to run the inference. This is very different from e.g. Opus, which you can basically only buy from Anthropic at the price they set.
I think the original DeepSeek moment seemed important. And yes, the more recent model is good, but there are multiple. This commodification trend spans many different companies, including Kimi 2.5/2.6 and GLM5.1, and even Google itself with its Gemma models. There are a dozen models that exist at roughly the frontier from 6 months ago at 1/10th the cost.
Well, yes. Anyone who tells you they know how this is going to work is an idiot.
I appreciate Evans’ work and wrote an “antithesis” (1) to the Nov 2024 iteration of this. Given the pivot to “models look likely to become infrastructure” I might want to update my take.
(1)https://philippdubach.com/posts/is-ai-really-eating-the-worl...
Didn't you mean Claude take? It's ai written after all...
I didn’t know there were a sequence of these decks; thanks — it’s helpful to think of them as updating snapshots in time.
The main thing that stands out to me on these graphs is just . how . early we still are - looking at industries like legal which in my mind are certainly going to be massively disrupted, and seeing the very low usage rates vs. tech (which still shows less than a quarter of tech people using AI daily) — we are in for a lot more change than we’ve seen so far.
This 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.
Right, 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.
I feel like the talk about "world models" is trying to reach at that, but cast it in different terminology. World model is just domain model, and once you're at domain model, there are multitudes of domains.
Unsupervised learning over domain rulesystems has the potential to let us define really well-defined, scoped models that behave a lot more deterministically and don't colour outside the lines, and reserve their weights for cleanly modeling the domain associations and relationships that matter.
I just asked codex the following question in the middle of my coding prompt:
Answer:
As amusing as this may be, I really have no need or desire for my coding model to understand or be aware of ewoks and their relative strengths compared to jawans. Nor do I need it to understand the nuances of the races of middle earth. And prompt response of "I have no idea what you are talking about" to all of these would feel reassuringly scoped.
Mixture-of-Experts seems like an attempt to do this - the domain structure being extracted into specific sub-models that are presumably trained on particular domain-associated content - but it feels like this is once again the beginnings of what is possible.
> 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
Nice 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.
? 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.
> 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.
With each platform shift, some of the old players disappear and some of them become irrelevant - IBM is still with us but no one cares
What 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
>>Companies report ‘annualised’ revenue, defined as sum of previous 4 weeks multiplied by 13.
why is it multiplied by 13?
52/4 = 13
13*4=52 weeks, mostly
52 weeks / 4 weeks = 13
28*13=364
Yeah it's weird huh? The "average" month contains 4.35 weeks.
(365/7)/12 = 4.3452…
this 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.
Because that's 364 days.
Usually startups like to talk about Adjusted Annual Revenue in fundraising and other hype materials. There’s no regulation around this metric so whatever their investors are willing to accept is what they use. One way to measure it is to take the past 4 weeks revenue and multiply by 13.
There’s a bunch of fuzzy metrics here, which is one reason I turned it back into a monthly number. The other issue (as you’ll see on the chart) is that Anthropic and openAI are recognising revenue in completely different ways.
I 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.
Further 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.
If 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
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.
Lots of quotes from Mark Zuckerberg, not a lot of Zuckerberg quotes on the $80b invested in the metaverse
I had that exact chart in a previous presentation.
"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.
Didn'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.
You should repost this using your name.
And then, if there is any data that you think is incorrect, or arguments that you disagree with, you should explain why. All of the charts are sourced, and none of them are 'fantasies'.
Why do you need this persons name?
The implication is that it's a bot saying this, not a person.
Doesn't seem like a bot, and even if it were, the critique is germane. Calling for a name is a little threatening.
No it isn't. The implication is that pro-AI people can take revenge. He knows he is secure with his opinions. He even paraphrases Andreesen's title "software will eat the world". He has repeatedly appeared at a16z.
It is very secure to be pro-AI while the rest has to resort to unregistered typewriters like in the Soviet Union.
not to be too pedantic but sourced doesn't usually mean accurate. sourced can very well be fantasy. it will be a 'sourced fantasy' in that case or hallucination if you used a LLM.
Yeah, beyond this mumbo-jumbo non-answer of yours, did you or did you not push crypto? Because if you did...it could kind of not speak for your analytical competence.
Hah wow, what a way to confirm what he posted.
It is an indisputable fact that you spent years shilling crypto. Why even deny that or threaten(!) someone pointing it out? It was/is a huge, verifiable chunk of your public output.
Unflagged as I don't feel this comment deserves to be flagged. The call for reposting with a real name is unnecessary though - if an internet comment is incorrect or overstates the case, just reply to correct it or ignore it.
tl;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.
It 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.
Or we could not do that...
Technology is meant to serve us not drive us into a hellscape lol