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You 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 v

by btucker 5mo ago

You 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 ago
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.
- dist-epoch 5mo ago
  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.
  - amanaplanacanal 5mo ago
    That seems pretty temporary if people can just build more compute.
  - nmfisher 5mo ago
    antirez 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.
    - dist-epoch 5mo ago
      That specialized hardware will be scooped up by AI data-centers, just like RAM is today.
      - nine_k 5mo ago
        No more than Mac Studios. Datacenters need different hardware.
      - ffsm8 5mo ago
        The 512 GB ram studio can't even be purchased anymore. It's been delisted https://www.apple.com/shop/buy-mac/mac-studio https://www.apple.com/shop/buy-mac/mac-studio Same with the Mac mini. entirely removed from all store references
    - treis 5mo ago
      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.
    - deleted 5mo ago
      [deleted]
    - menaerus 5mo ago
      2-bit quantization? That's a lot of signal being removed. Considering how quickly the AI models are progressing in their capabilities (still exponential curve), I will not want to use the 2025 model in two years time. Similarly, how I don't want to use llama-3 or old Anthropic model from 2023 or 2024. Newer models are so much better that it makes it very difficult to ignore. Once and if the advancements with the AI models slow down, only then IMHO it will become feasible to design the specialized HW for general-purpose consumption and general-purpose workloads.
  - wolttam 5mo ago
    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.
  - eli 5mo ago
    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 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.
  - danny_codes 5mo ago
    There are myriad compute providers. I suspect the inference market is hard to monopolize. But given our anti-trust track record the past 40 years I suppose it’s possible.
- mchusma 5mo ago
  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.
  - mistrial9 5mo ago
    > 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
benedictevans 5mo ago
Well, yes. Anyone who tells you they know how this is going to work is an idiot.
7777777phil 5mo ago
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.
- ffsm8 5mo ago
  Didn't you mean Claude take? It's ai written after all...
vessenes 5mo ago
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.
- cman1444 5mo ago
  Legal has lots of institutional inertia behind it though. I think AI will be very very useful for lawyers..... at their desk in private. But I don't see it replacing them. The legal system is heavily personal and relies a lot on reputation and tradition. I think you'll see courts, bar organizations, etc frowning on using AI too heavily, and certainly not using it to automate "official" processes.
libraryofbabel 5mo ago
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.
- zetsurin 5mo ago
  > 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.
  - akiselev 5mo ago
    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.
  - libraryofbabel 5mo ago
    Well, yes, but as the other commenter says, that’s a very broad general statement akin to something like “AI will change knowledge work“. That’s certainly true, but how? What are the details? What kind of companies are going to be the winners and what kind will be losers, or end up with commodity margins, like the telcos did after the mobile revolution? What is the pricing structure going to look like? I suppose a concrete example in 1997 would be that a lot of companies thought the future of e-commerce was setting up a store on AOL, that people would use while sitting down at a desktop PC. Obviously it didn’t turn out quite that way. Furthermore, the Internet enabled new kinds of ways to buy things that weren’t even envisioned in the pre-Internet pre-smartphone world: think Airbnb and Uber. Predictions are hard, especially about the future. Most predictions reflect the worldview and biases of the time in which they are made: think about all the vintage sci-fi from the 60s 70s and 80s that actually reads or looks kind of retro now. Similarly, our predictions of the future will look kind of retro and strange to someone living in the 2030s or 2040s. If studying history has any lesson to teach us, it’s really just this: that the past is an alien world with alien moods of thinking, and that our moment in time will look similarly alien to people in the future who choose to look back and analyze it closely. This isn’t an argument that we should stop trying to make predictions. We need to, but it is an argument for humility, and also for questioning all your assumptions that you might be importing.

by btucker 5mo ago

flossly 5mo ago

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.

dist-epoch 5mo ago
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.
- amanaplanacanal 5mo ago
  That seems pretty temporary if people can just build more compute.
- nmfisher 5mo ago
  antirez 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.
  - dist-epoch 5mo ago
    That specialized hardware will be scooped up by AI data-centers, just like RAM is today.
    - nine_k 5mo ago
      No more than Mac Studios. Datacenters need different hardware.
    - ffsm8 5mo ago
      The 512 GB ram studio can't even be purchased anymore. It's been delisted https://www.apple.com/shop/buy-mac/mac-studio https://www.apple.com/shop/buy-mac/mac-studio Same with the Mac mini. entirely removed from all store references
  - treis 5mo ago
    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.
  - deleted 5mo ago
    [deleted]
  - menaerus 5mo ago
    2-bit quantization? That's a lot of signal being removed. Considering how quickly the AI models are progressing in their capabilities (still exponential curve), I will not want to use the 2025 model in two years time. Similarly, how I don't want to use llama-3 or old Anthropic model from 2023 or 2024. Newer models are so much better that it makes it very difficult to ignore. Once and if the advancements with the AI models slow down, only then IMHO it will become feasible to design the specialized HW for general-purpose consumption and general-purpose workloads.
- wolttam 5mo ago
  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.
- eli 5mo ago
  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 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.
- danny_codes 5mo ago
  There are myriad compute providers. I suspect the inference market is hard to monopolize. But given our anti-trust track record the past 40 years I suppose it’s possible.
mchusma 5mo ago
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.
- mistrial9 5mo ago
  > 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

benedictevans 5mo ago

Well, yes. Anyone who tells you they know how this is going to work is an idiot.

7777777phil 5mo ago

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.

ffsm8 5mo ago
Didn't you mean Claude take? It's ai written after all...

vessenes 5mo ago

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.

cman1444 5mo ago
Legal has lots of institutional inertia behind it though. I think AI will be very very useful for lawyers..... at their desk in private. But I don't see it replacing them. The legal system is heavily personal and relies a lot on reputation and tradition. I think you'll see courts, bar organizations, etc frowning on using AI too heavily, and certainly not using it to automate "official" processes.

libraryofbabel 5mo ago

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.

zetsurin 5mo ago
> 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.
- akiselev 5mo ago
  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.
- libraryofbabel 5mo ago
  Well, yes, but as the other commenter says, that’s a very broad general statement akin to something like “AI will change knowledge work“. That’s certainly true, but how? What are the details? What kind of companies are going to be the winners and what kind will be losers, or end up with commodity margins, like the telcos did after the mobile revolution? What is the pricing structure going to look like? I suppose a concrete example in 1997 would be that a lot of companies thought the future of e-commerce was setting up a store on AOL, that people would use while sitting down at a desktop PC. Obviously it didn’t turn out quite that way. Furthermore, the Internet enabled new kinds of ways to buy things that weren’t even envisioned in the pre-Internet pre-smartphone world: think Airbnb and Uber. Predictions are hard, especially about the future. Most predictions reflect the worldview and biases of the time in which they are made: think about all the vintage sci-fi from the 60s 70s and 80s that actually reads or looks kind of retro now. Similarly, our predictions of the future will look kind of retro and strange to someone living in the 2030s or 2040s. If studying history has any lesson to teach us, it’s really just this: that the past is an alien world with alien moods of thinking, and that our moment in time will look similarly alien to people in the future who choose to look back and analyze it closely. This isn’t an argument that we should stop trying to make predictions. We need to, but it is an argument for humility, and also for questioning all your assumptions that you might be importing.