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.
Thanks for the summary. I do love Benedict‘s work; I find he’s one of the few commentators who consistently strikes a balance between taking the transformative potential of AI seriously while not falling over into hype.
Some things that stand out:
* He’s really good with his historical analogies, especially looking at previous transformations like the early Internet and mobile; no surprise given that he has a history degree.
* he emphasizes over and over how we have still have no idea how all of this is going to work when the dust settles. I think that’s kind of a historian’s move as well. When you look at what people were saying during the early days of the web, for example, almost all of their predictions weren’t just wrong… in hindsight, given how the future played out, they were asking the wrong questions. The implication is that we are probably asking the wrong questions about AI too.
* Nonetheless his thesis about the commoditization of models is actually a fairly strong concrete prediction. i’m not sure if I agree with it entirely, but I do keep it in mind every time I look at the valuation of leading AI labs.
* he continually makes the point that a chat bot is barely a product and that AI labs have so far had very little success in delivering products above that layer… with the exception of coding agents, of course.
> for example, almost all of their predictions weren’t just wrong… in hindsight, given how the future played out, they were asking the wrong question
Do you have an example of this? My (poor) memory remembers "it's going to change how people buy things", was the big deal at the time, and it seems like it was a great prediction.
That's a very vague prediction that took decades to bear fruit. The concrete predictions behind the investments into companies like Pets.com and Webvan failed. It took the survivors like Ebay/Paypal and Amazon to build the digital payment and shipping infrastructure over decades until cultural acceptance hit critical mass.
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.
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.
no disagree, specifics matter.. There are a dozen well-defined LLM application subject areas that are regularly tested.. one overall grade IMO lacks important detail.. To go a bit abstract, it is ironic that "oversimplification" in the discussion of these complex machines mirrors the effects on information of the automations themselves.. constantly simplifying, substituting and diluting real meaning
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.
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.
It's big because it may take a big swath of people who will actually pay for LLMs out of the market. But for the average consumer they're going to primarily use their phone/tablet and we're far away from that being possible.
Even if it were possible the LLMs are such a gold mine of user data. It's really hard to see that opportunity be passed up.
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.
I appreciate Evans’ work and wrote an “antithesis” to the Nov 2024 iteration of this. Given the pivot to “models look likely to become infrastructure” I might want to update my take.
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.
I've been having similar thoughts, regarding the gigantic trillion parameter models. I'm starting to believe the future will be very specialized focused models thant can be run on modest hardware (locally) but that can scale in performance (latency, speed) in the cloud, much like any other software of today.
If you need to do programming do we really need trillions sized models? Other domains might be large or smaller, but there's no need for a model to 'know' everything and datacenter levels of hardware to run.
General chatbots might work better as larger models since you really don't know what people will also for, or alternatively we find a way to route the initial question to the appropriate model. Like MoE but without needing to load a gigantic model into memory first.
> As amusing as this may be, I really have no need or desire for my coding model to understand or be aware of ewoks
You'll think otherwise the first time you're a victim of a zero-day ewok.
Seriously though, while coding models may not need to know about ewoks, their contextual knowledge of things beyond just writing code almost certainly makes them better coding models.
It could be difficult to constrain the training corpus "just right" so that you eliminate all the irrelevant subjects like ewoks but retain enough so that the model doesn't turn into an idiot savant capable of churning out correct code but incapable of understanding what you really want.
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.
Lets say you work at a startup that is growing insanely fast and you want to report financial metrics to investors, media etc. You can't use annual recurring revenue because 2026 is not over yet, and your company is so young it doesn't make sense to look back to last year. You can't use YoY because it would be some obscene figure (100000%) that definitely won't hold.
So the two best metrics are annualized recurring revenue (take last month * 12 or last 4 weeks * 13) and QoQ growth %.
There are two caveats:
- If the revenue is high quality (e.g. annual enterprise contracts, good NRR), then last 4 weeks * 13 is actually a conservative estimate as your company will likely continue to grow.
- But if the revenue is more volatile (e.g. consumption, token usage, bad NRR) then annualized recurring revenue can be used to hide worse performance because companies will juice revenue one month and report high "ARR"
I see but it's still predicted annual revenue. I can understand people not liking the word 'predicted' here because it is more grounding but that's what it is in the end? I guess i understand it now. thanks.
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.
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.
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.
> "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.
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'.
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.
'these charts are fantasies' is a non-criticism from an anonymous moron. If there is an actual criticism of an actual point, make it.
Back when people were interested in Blockchains, I explained why people in tech were interested. I'm happy to explain that again now, if anyone cared. If someone thinks that's bad, they're a fool.
Blockchain as a technology was something a lot of people were interested in. I am curious about Bitcoin - the monopoly money of the real world. The questions was simple: did you, or did you not, promote Bitcoin? Anyone who would promote a fake, un-regulated "currency" would not be someone whose opinion I care about.
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.
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.
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.
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.
No, it's a really stupid lie. You can look at all my essays and presentations online.
I've spent some time discussing why people are interested in blockchains as software platforms, and what would be good and bad arguments around that. But I've never suggested anyone buy a token - indeed, I was pretty vocal in pointing to speculative bubbles and silly ideas, like NFTs.
Crypto today has a lot in common with both the internet in 1993 and the internet in 1999. Huge potential with few of the use cases invented yet, combined with froth, scams and delusion. This makes it easier to dismiss (“useless AND a scam!”).
But dismissing crypto as a useless scam is much like looking at Usenet, Cuecat and Boo .com and dismissing the internet. It mistakes applications for the enabling layer.
Looking at crypto and only seeing the scams is like looking at the internet in 1999 and only seeing the bubble.
Looking at crypto and seeing no use cases is like looking at the internet in 1993, when the web was 3% of traffic
Another parallel:
1993 - people complaining the term should be internets, not internet
2018 - people complaining 'that's not what crypto means'
You could argue I was wrong and blockchain's potential never turned into anything much. It actually has become a huge deal as plumbing in the finance industry, but not much else. But so what? This was an interesting tech that hasn't really worked out. Welcome to the tech industry.
I dunno... but those self-quotes sound a lot like at least implying that "missing out on crypto" would be like missing out on early days of Internet. You also seem to try and retrofit "blockchain" instead of "crypto", but these are two different things - blockchain is a technology, crypto is a form of play-money based on that technology, just like it's older brother, the monopoly-money, is based on the technology of paper (but not much else).
No, I was using crypto and blockchain as interchangeable terms. I'm aware some people don't, but that's always how any person the space I've spoken to uses them
In the current system technology is meant to serve the shareholders. That might end up serving us, if you believe the standard narrative. But maybe the shareholders will just carve out a few nature reserves for themselves and just wait for the rest of us to die off.
What'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.
Who made Ilya Sutskever, or any other LLM-bonehead the Grand Prophet of Humanity? Why the fuck is his opinion on that relevant? Of course he will shill for data centers.
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.
Thanks for the summary. I do love Benedict‘s work; I find he’s one of the few commentators who consistently strikes a balance between taking the transformative potential of AI seriously while not falling over into hype.
Some things that stand out:
* He’s really good with his historical analogies, especially looking at previous transformations like the early Internet and mobile; no surprise given that he has a history degree.
* he emphasizes over and over how we have still have no idea how all of this is going to work when the dust settles. I think that’s kind of a historian’s move as well. When you look at what people were saying during the early days of the web, for example, almost all of their predictions weren’t just wrong… in hindsight, given how the future played out, they were asking the wrong questions. The implication is that we are probably asking the wrong questions about AI too.
* Nonetheless his thesis about the commoditization of models is actually a fairly strong concrete prediction. i’m not sure if I agree with it entirely, but I do keep it in mind every time I look at the valuation of leading AI labs.
* he continually makes the point that a chat bot is barely a product and that AI labs have so far had very little success in delivering products above that layer… with the exception of coding agents, of course.
> for example, almost all of their predictions weren’t just wrong… in hindsight, given how the future played out, they were asking the wrong question
Do you have an example of this? My (poor) memory remembers "it's going to change how people buy things", was the big deal at the time, and it seems like it was a great prediction.
That's a very vague prediction that took decades to bear fruit. The concrete predictions behind the investments into companies like Pets.com and Webvan failed. It took the survivors like Ebay/Paypal and Amazon to build the digital payment and shipping infrastructure over decades until cultural acceptance hit critical mass.
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.
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.
> that exist at roughly the frontier
no disagree, specifics matter.. There are a dozen well-defined LLM application subject areas that are regularly tested.. one overall grade IMO lacks important detail.. To go a bit abstract, it is ironic that "oversimplification" in the discussion of these complex machines mirrors the effects on information of the automations themselves.. constantly simplifying, substituting and diluting real meaning
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.
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.
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.
It's big because it may take a big swath of people who will actually pay for LLMs out of the market. But for the average consumer they're going to primarily use their phone/tablet and we're far away from that being possible.
Even if it were possible the LLMs are such a gold mine of user data. It's really hard to see that opportunity be passed up.
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.
That seems pretty temporary if people can just build more compute.
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.
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” to the Nov 2024 iteration of this. Given the pivot to “models look likely to become infrastructure” I might want to update my take.
Didn't you mean Claude take? It's ai written after all...
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.
I've been having similar thoughts, regarding the gigantic trillion parameter models. I'm starting to believe the future will be very specialized focused models thant can be run on modest hardware (locally) but that can scale in performance (latency, speed) in the cloud, much like any other software of today.
If you need to do programming do we really need trillions sized models? Other domains might be large or smaller, but there's no need for a model to 'know' everything and datacenter levels of hardware to run.
General chatbots might work better as larger models since you really don't know what people will also for, or alternatively we find a way to route the initial question to the appropriate model. Like MoE but without needing to load a gigantic model into memory first.
> As amusing as this may be, I really have no need or desire for my coding model to understand or be aware of ewoks
You'll think otherwise the first time you're a victim of a zero-day ewok.
Seriously though, while coding models may not need to know about ewoks, their contextual knowledge of things beyond just writing code almost certainly makes them better coding models.
It could be difficult to constrain the training corpus "just right" so that you eliminate all the irrelevant subjects like ewoks but retain enough so that the model doesn't turn into an idiot savant capable of churning out correct code but incapable of understanding what you really want.
> 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.
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
> 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
>>Companies report ‘annualised’ revenue, defined as sum of previous 4 weeks multiplied by 13.
why is it multiplied by 13?
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.
Lets say you work at a startup that is growing insanely fast and you want to report financial metrics to investors, media etc. You can't use annual recurring revenue because 2026 is not over yet, and your company is so young it doesn't make sense to look back to last year. You can't use YoY because it would be some obscene figure (100000%) that definitely won't hold.
So the two best metrics are annualized recurring revenue (take last month * 12 or last 4 weeks * 13) and QoQ growth %.
There are two caveats:
- If the revenue is high quality (e.g. annual enterprise contracts, good NRR), then last 4 weeks * 13 is actually a conservative estimate as your company will likely continue to grow.
- But if the revenue is more volatile (e.g. consumption, token usage, bad NRR) then annualized recurring revenue can be used to hide worse performance because companies will juice revenue one month and report high "ARR"
I see but it's still predicted annual revenue. I can understand people not liking the word 'predicted' here because it is more grounding but that's what it is in the end? I guess i understand it now. thanks.
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.
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.
Because that's 364 days.
52 weeks / 4 weeks = 13
13*4=52 weeks, mostly
28*13=364
Yeah it's weird huh? The "average" month contains 4.35 weeks.
(365/7)/12 = 4.3452…
52/4 = 13
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.
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.
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'.
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.
'these charts are fantasies' is a non-criticism from an anonymous moron. If there is an actual criticism of an actual point, make it.
Back when people were interested in Blockchains, I explained why people in tech were interested. I'm happy to explain that again now, if anyone cared. If someone thinks that's bad, they're a fool.
Blockchain as a technology was something a lot of people were interested in. I am curious about Bitcoin - the monopoly money of the real world. The questions was simple: did you, or did you not, promote Bitcoin? Anyone who would promote a fake, un-regulated "currency" would not be someone whose opinion I care about.
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.
Sure. So which chart does our anonymous coward think is a fantasy, and why?
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.
Hah wow, what a way to confirm what he posted.
Why do you need this persons name?
The implication is that it's a bot saying this, not a person.
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.
Doesn't seem like a bot, and even if it were, the critique is germane. Calling for a name is a little threatening.
Looking at a 80 slide deck and saying that the charts are 'fantasies' is not a germane criticism at all. it's handwaving.
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.
No, it's a really stupid lie. You can look at all my essays and presentations online.
I've spent some time discussing why people are interested in blockchains as software platforms, and what would be good and bad arguments around that. But I've never suggested anyone buy a token - indeed, I was pretty vocal in pointing to speculative bubbles and silly ideas, like NFTs.
Here you go:
https://en.cryptonomist.ch/2018/11/01/benedict-evans-cryptoc...
Nope. This is the exact quote.
Crypto today has a lot in common with both the internet in 1993 and the internet in 1999. Huge potential with few of the use cases invented yet, combined with froth, scams and delusion. This makes it easier to dismiss (“useless AND a scam!”).
But dismissing crypto as a useless scam is much like looking at Usenet, Cuecat and Boo .com and dismissing the internet. It mistakes applications for the enabling layer.
Looking at crypto and only seeing the scams is like looking at the internet in 1999 and only seeing the bubble.
Looking at crypto and seeing no use cases is like looking at the internet in 1993, when the web was 3% of traffic
Another parallel: 1993 - people complaining the term should be internets, not internet 2018 - people complaining 'that's not what crypto means'
You could argue I was wrong and blockchain's potential never turned into anything much. It actually has become a huge deal as plumbing in the finance industry, but not much else. But so what? This was an interesting tech that hasn't really worked out. Welcome to the tech industry.
I dunno... but those self-quotes sound a lot like at least implying that "missing out on crypto" would be like missing out on early days of Internet. You also seem to try and retrofit "blockchain" instead of "crypto", but these are two different things - blockchain is a technology, crypto is a form of play-money based on that technology, just like it's older brother, the monopoly-money, is based on the technology of paper (but not much else).
No, I was using crypto and blockchain as interchangeable terms. I'm aware some people don't, but that's always how any person the space I've spoken to uses them
Indeed you did, but that does not make it right. They are not interchangeable. Now again, did you, or did you not promote Bitcoin?
No, you have a different opinion about terminology, which ironically is one of the things I noted as pointless in the thread I posted.
No, answer my question - it's not that hard!
Nope what? You just confirmed the OP’s point. Glad we’re all in agreement.
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
In the current system technology is meant to serve the shareholders. That might end up serving us, if you believe the standard narrative. But maybe the shareholders will just carve out a few nature reserves for themselves and just wait for the rest of us to die off.
What'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.
Did 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.
"we"? Are we all now billionaires who decide what gets built and who gets laid off?
None of "us" gets to decide this. Only the very wealthy get to decide.
There's a way for us to have a say in the matter. It's happened before.
Okay, what way? What happened before?
Don't be vague.
> To quite Ilya Sutskever:
Who made Ilya Sutskever, or any other LLM-bonehead the Grand Prophet of Humanity? Why the fuck is his opinion on that relevant? Of course he will shill for data centers.
when someone gives their opinion about AI, one typical retort is "are you an AI/LLM expert? we should let the experts talk"
The so-called experts are bought and paid for.