A rational conversation on where AI is actually going | Benedict Evans

Summary

Podcast Summary: Lenny’s Podcast – “A Rational Conversation on Where AI Is Actually Going”

Guest: Benedict Evans
Host: Lenny Rachitsky
Date: May 31, 2026

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Overview

In this episode, Lenny Rachitsky hosts Benedict Evans—a well-known independent tech analyst previously with a16z (Andreessen Horowitz)—to unpack the true scope, hype, risks, and future impact of AI. Benedict argues for a measured view: AI is transformational, on the scale of the Internet or mobile, but not necessarily more so, and history’s lessons from earlier technology shifts still apply. Together, they explore job impacts, value capture, industry disruptions, the rise of anti-AI sentiment, predictions about utility models and pricing, distribution moats, and career advice for adapting to an uncertain future.

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Key Themes and Discussion Points

1. AI’s Place in Tech History

• Benedict’s Core Thesis:
  “AI is as big a deal as the Internet or mobile and only as big a deal as the Internet or mobile.” (00:00)

  • AI is a major platform shift, but calls comparing it to the Industrial Revolution are overblown; the Internet and smartphones were already huge transformations.
  • Most of the transformative uses of AI haven’t been built yet; we are in an “Internet 1997” moment.

  Quote:
  “If you’re going to make the Internet comparison, it’s like we’re in 1997. Most stuff kind of doesn’t work yet. Most of the stuff that people are going to do hasn’t been built yet.” (03:23)

2. On Jobs, Automation & Economic Impact

• Every platform shift automates tasks and creates new categories of jobs, but the new jobs aren’t predictable in advance:
  • “Every time we have a new technology, it automates away a bunch of jobs and then… unlocks a bunch of new jobs and you don’t know the new job because it doesn’t exist yet…” (00:09, 19:19, 66:49)
• The history of accounting, software, and law illustrates that major productivity leaps do not automatically shrink job markets; often, demand or expectations rise instead.
• Anxieties about AI causing a "job apocalypse" are mostly misplaced; Benedict criticizes oversimplified takes:
  • “You can’t predict which things are going to be exposed… You can’t look at a senior partner at a law firm and say, well, 17% of their work could be automated. This is horseshit.” (00:31, 63:38)

3. Professional Services & AI

• Ironically, leading AI labs are massively hiring professional services and consultants:
  • “You would think AI is going [to kill consulting], but the most cutting edge AI labs are the ones most investing in these folks.” (12:13)
  • Most tasks involve complex social, political, or situational context that’s very hard to automate.
• Analogies to prior industry shifts: record companies and newspapers didn’t understand their true business until tech decoupled value from their physical operations.

4. Adoption Cycles & the Maturity of AI

• Adoption is uneven—while tech insiders “live in the future,” the general public lags far behind:
  • Even among 13 to 18-year-olds, only about 15–20% are daily AI users. (08:52)
• The tech industry overestimates how quickly and thoroughly AI will be adopted; actual enterprise software transitions take years.
• Many “AI for X” companies could have been started years ago, except nobody noticed the need or opportunity before (23:01).

5. Value Capture: Where Will the Money Flow?

• The foundational model companies (OpenAI, Anthropic, etc.) may not be as dominant or as profitable as expected:
  • “Over time, the foundational model companies… their margins will get squeezed... the bigger opportunity is in the application layer, the people building on the models, the wrappers.” (38:09)
• Benedict draws analogies to telcos and electricity: even if a business is essential infrastructure, value often accrues to whoever sits atop the value chain, not the commodity provider (33:06–34:49).
  • “If the chatbot isn’t the UX and it needs to be apps… the value should go out [to the app layer].” (36:03)

6. Distribution as Moat

• As foundational models become commoditized, distribution and brand become key moats.
• Benedict likens the situation to the browser wars: Microsoft’s win on distribution didn’t deliver lasting value, because value moved further up the stack (44:49–44:57).
• The power of incumbents:
  • “If the product is a commodity, then the distribution is what matters.” (43:53)

7. Anti-AI Sentiment and Societal Concerns

• Rising backlash towards AI: concerns over energy, water, jobs, and cultural changes.
  • Many specific fears (e.g., data center water use) are greatly exaggerated:
    “Data centers use water for cooling… but the number of data centers relative to the total… water use… is tiny.” (49:03)
• Lack of good data: Even for core questions like usage and real-world employment effects, robust data is missing (50:35).
• Echoes of prior social media backlashes; this cycle is faster and more politically charged. (52:07)

8. The Unknowability of Transformative Tech Shifts

• Embracing radical uncertainty is essential: “Presume radical uncertainty.” (66:20)

• Most predictions about which industries will be automated, or which jobs will be most/least affected are almost always wrong—unexpected sectors get transformed.

  • E.g., “The last profession you’d think would be automated is engineering and coding… now it’s the most transformed role of any role.” (62:01)

• Favorite Recurring Motif:
  “It depends.” (66:09, 75:51)

9. Advice for Navigating the Future

• Don’t ignore or resist new technology:
  • “Don’t stick your head in the sand and say I hate all this stuff… What helps is you diving into this, completely submerging yourself in it and coming out, understanding what you can do with it…” (66:49, 67:47)
• Be ready for ongoing, sometimes turbulent change—don’t count on stability in career paths or business models.

10. Personal and Career Insights

• For parents: If your kids are heading into the job market soon, things feel uncertain for a few years; for younger children, normal adaptability works (53:35–54:06).
• On steering children toward/away from certain jobs: The future is too uncertain to give prescriptive guidance beyond adaptability, skills development, and openness to change (58:39).
• Adaptability, curiosity, and willingness to engage with change outweigh specific technical training.

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Notable Quotes & Moments

• On Foundation Models Becoming Utilities:
  “If the chatbot isn’t the UX and it needs to be apps and the model companies aren’t going to build that… then why would the model companies have pricing power?” (36:03)
• On the Limits of Automation Panic:
  “There’s a chart in the presentation of the number of people employed as accountants, which… has gone up again since the beginning of the 21st century… why is that? Well, it’s more complicated than automation.” (17:22)
• On Mistaken Early Predictions:
  “Imagine having this conversation about the Internet in 1997… you would have missed almost all of it.” (39:09)
• On Vague AGI Definitions:
  “People are redefining AGI to mean the stuff that works now… Once machines can do it, people just say, ‘well, that’s just software’.” (27:26)
• On Professional Value:
  “What you actually pay Bain to do is to go and walk all over your company and work out, ‘yes, but why is it that you didn’t do that?’... The PowerPoint is just the task, but that’s not what you hired them for.” (15:58)
• On Career Resilience:
  “You have to dive into this and absorb it and internalize it and think about what it means, just as… we did with mobile and the Internet.” (68:24)
• On the Paradox of AI Use:
  “AI is good at stuff that computers are bad at and bad at stuff that computers are good at.” (69:44)
• On Technology and Human Impact:
  “Every wave of technology comes with ways that you can ruin people’s lives, either deliberately or by accident.” (58:10)
• On Parenting and Uncertainty:
  “The days when you kind of knew what your career was going to be are over…” (58:57)
• On Life Philosophy:
  “It depends. It’ll probably be okay.” (75:56)

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Timestamps for Key Segments

• Opening Controversial Opinion: 00:00–00:40
• Tech Transitions & AI’s Parallels with the Internet: 02:25–05:15
• 1997 Moment & State of Adoption: 06:25–09:44
• Professional Services and Consulting in the AI Era: 10:06–17:45
• Jobs, Automation, Friction, and Elasticity: 17:22–22:20
• AGI Hype & Moving Definitions: 26:02–29:46
• Value Capture, Applications vs. Infrastructure, Industry Parallels: 30:23–38:28
• Distribution as Moat, Browser Wars Analogy: 43:15–47:42
• Anti-AI Sentiment, Water Usage, Real Impacts: 48:38–52:55
• Advice for Parents and Career-Steering: 53:12–59:03
• Presume Radical Uncertainty & Embracing Change: 66:18–68:24
• AI in Daily Life—Personal Uses: 69:00–70:56
• Lightning Round (Books, Movies, Gadgets, Life Mottos): 72:24–77:41
• Final Reflections, Where to Find Benedict Evans: 78:12–79:24

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Final Thoughts and Takeaways

• AI is monumental—but not magical. We’re early and should expect surprises on timeline, impact, and business models.
• The real, durable value will accrue at the application/distribution/cultural layer—not in AI infrastructure itself.
• Predicting the future of jobs or industries is fraught: unexpected roles will be automated, new ones will be created, and historic patterns of creative destruction will repeat.
• To thrive: stay curious, deeply engage with new technologies, and be ready to adapt—don’t retreat or rely on comfort from past expertise.
• For those worried about AI: “It depends—it’ll probably be okay.” But only if you get involved.

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Find Benedict Evans’ newsletter, presentations, and more:
ben-evans.com

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This summary covers all major arguments, analogies, and memorable lines from the main discussion, omitting ads and promotional content. It is intended to give you both the spirit and substance of Benedict Evans’ perspective on AI’s real trajectory.

Transcript

[Benedict Evans]

My most controversial opinion is that I think that AI is as big a deal as the Internet or mobile and only as big a deal as the Internet or mobile.

[Lenny Rachitsky]

What? You're just on the coming job Pocalypse.

[Benedict Evans]

Every time we have a new technology,

it automates away a bunch of jobs and then that automation unlocks a bunch

of new jobs and you don't know the new job because it doesn't exist yet. We've had that process over and over again.

[Lenny Rachitsky]

Even just looking at the most advanced AI companies throughout big OpenAI everyone's increasing headcount.

[Benedict Evans]

You talk to these dumas on Twitter and they would act like every big

company is going to buy ChatGPT tomorrow

and then in two weeks time they'll fire all their stuff.

These people are morons.

You can't predict which things are going to be exposed. You can't look at a senior partner

at a law firm and say, well, 17% of their work could be automated. This is horseshit.

[Lenny Rachitsky]

I'm curious if you're following the anti AI sentiment.

[Benedict Evans]

It's a big fuzzy mass.

Yes, this will change a bunch of stuff and we'll need to worry about it, but that's kind of a constant. We've always had that.

[Lenny Rachitsky]

What would be a couple things you recommend people do to be more successful in this future?

[Benedict Evans]

Don't stick your head in the sand

and say I hate all of this stuff.

That gives you a great feeling of

moral superiority and you can go on Blue sky and shout at everybody about how evil AI is. Like great, I'm happy for you, but

that's not going to help.

What helps is you diving into this and coming out, understanding what you can do with.

[Lenny Rachitsky]

Today my guest is Benedict Evans. Benedict was a longtime partner at a 16z as their in house analyst and resident thinker. Before that he was a longtime equity researcher and for the past six years he's been an independent analyst tracking the most important tech trends and sharing what he's learning. Most recently, as you'd expect, he's spending all his time on how AI is changing our lives. And in his words, AI is eating the world. In this conversation we go deep on what we're still not pricing in on the impact that AI is going to have on our lives and our work. The rise of anti AI sentiment, the impact on jobs where in the value chain most of the value will accrue and tons more. If you are worried about AI or just confused about where things are heading, this conversation will teach you a lot and also make you feel Better. Before we get into it, don't forget to check out lennys productpass.com for a year. Free of some of the most amazing, hottest, most well crafted AI products in the world. Available exclusively to Lenny's newsletter subscribers. With that, I bring you Benedict Evans. Benedict, thank you so much for, for being here.

[Podcast Host/Announcer]

Welcome to the podcast.

[Benedict Evans]

Thank you for inviting me.

[Lenny Rachitsky]

You just put out this deck called AI is Eating the World. I want to ask you kind of the flip side of this, of we all know it's a big deal, like knowing that what do you think people are still not fully pricing in when they think about the change that they're going to experience to their lives and their work.

[Benedict Evans]

An interesting way of thinking about it. I did a podcast last year with

someone where I said, you know, I. My most controversial opinion is that I think that AI is as big a deal as the Internet or mobile and only as big a deal as the Internet or mobile. Because clearly there's a bunch of people in tech who think, no, this is more like the Industrial revolution or something. And there are a whole bunch of people underneath saying, well, he thinks this is just as big as. Does he not understand how big this is?

And I'm like, smartphones were quite a big deal. The Internet was quite a big deal.

We wouldn't be doing this if it wasn't for the Internet.

So there's like one layer of. But then if you dig into that, like, if you're going to make the

Internet comparison, it's like we're in 1997. Like, it's very exciting.

Most stuff kind of doesn't work yet. Most of the stuff that people are

going to do hasn't been built yet.

And it's not really clear how any

of it's going to work when it

does work and the people who have already got it, who have already taken

whichever pill it is, I forget which

sort of imagine that everybody in the

world is already there.

And the truth is you've got this

kind of very wide distribution.

So there's people in tech who bought

their cluster of Mac Minis and, you know, don't use Google anymore.

And then you look out sidetech and

setting aside the idiots who think that

this isn't real, you know, most people

are using, who are using this, are using this every week or two maybe.

So you've got that kind of spread

of adoption and that spread of maturity of how well this works.

And then within that you can make sort of specific points about, well, how

are the models going to work? And do the model labs have pricing

power and where's the value going to be? And has OpenAI won the whole thing

or is Anthropic got it this week?

And so then you can kind of get into calling those races where again,

it's like being in 1997 and saying, well, is it going to be Excite or Yahoo? And the answer was no generally.

So there's a sort of a fractal point here. There's like the sort of the super high level that like this is going to change absolutely everything. I don't think it's particularly productive to

say well is it 20% bigger than the Internet or 100%? Those aren't productive conversations.

But it's one of those fundamental changes. But then you don't know how any

of it's going to work. In fact, I just published it.

I do a presentation every six months

and I just published one yesterday and

one of the comments was Benedict.

This is 80 slides of saying we don't know, which is like slightly facetious but also kind of true.

[Podcast Host/Announcer]

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[Lenny Rachitsky]

So if we're in this 1997 timeline for AI, I know so much of your messages, we don't know where it's going exactly yet. I don't know, do you have a sense of just like the timeline to okay, now things are going to be radically changing. Like where are we in that cycle, you talk about all these different cycles we've been through, like how far are we from just like, wow, it's all different here.

[Benedict Evans]

Well, unquestionably we're already in that moment in software.

And then there's a conversation about, well, what does Agentic and AI software development, two separate things that merge together mean

for the future of software industry?

You know, there's one extreme which is no one really believes, which is, you know, hey, you'll just like five code your own stripe. And no one actually believes that, although very few don't believe that.

But clearly there's a whole bunch of

questions about what this means for the software industry and how much stuff you'll

be able to do yourself or how much more software there will be.

And that's one whole conversation.

The other extreme is if you're in a law firm. This is all very interesting, but how exactly do we use this and how do we work out how not to

be the next story that we've submitted something with hallucinations in it and how

many associates are we going to hire next year? What does this mean for us? One of the analogies I used in the presentation is imagine you're an accountant

seeing the first software spreadsheets in the late 70s. This is mind blowing.

You change the interest rate here and all the other numbers change and it does a week of work for you in like 30 seconds.

And we can talk about what that

meant for the accounting industry.

But clearly if you're an accountant, this is obviously mind blowing.

But if you were a lawyer looking

at that or journalist looking at that,

you'd think, well that's very clever and my accountant should see this. But that's not what I do. I might use it for my timesheet next week if it didn't cost 10

or $15,000 to get the Apple II and the monitor and the printer to

run it, which is what it costs

if you adjust replacement.

But that's not what I do.

And you need a word processor, which actually came like very shortly afterwards.

And so that's sort of the moment that we're in of there's some people

like software development are develop software developers are the accountant seeing VisiCalc like, oh my God, this changes everything. Like before VisiCalc and after VisiCalc before, before Claude code and after Claude code,

a lot of other people are picking it up, using it to varying degrees, but slightly puzzled.

So you know, there's a bunch of survey data that I put in the

presentation that even if you look at like 13 to 18 year olds or something. It's still like kind of 15, 20% of people are daily active users and another 20% are weekly active users. And then the other 60% of those people in that demographic on you say

they are not using this.

So there's a sort of very wide spread of who gets it and a very wide, which I think also maps,

this is kind of almost a separate

point maps to this sort of jagged frontier question of where does this work?

Where does it not work?

Can you tell where it's going to work? Is it intuitive to know where it would work? Can you tell after it worked? Can you, you, can you, can you work out for yourself what you would do with this? And all of those intersect. If you're a software developer, there's a lot of other people were like people having a moment or they're not or we're in again, we're in that kind

of 1997 moment of okay, what is this?

[Lenny Rachitsky]

Along those lines, something you've been writing a bit about. Is this like unexpected investment in professional services, slash consulting services, slash forward deployed engineers. All the AI labs, at least the two big ones open anthropic, are like investing in buying massive like consultancies and firms. Talk about just what is happening there. Why, why that's happening.

[Benedict Evans]

Well, I just, I was kind of groping for a joke last night when

I wrote my newsletter and couldn't quite

get to land it. But as you know, something like, you know, we know the joke that a machine learning scientist is a statistician who lives in San Francisco and there's something in there of like a forward deployed engineer is like an Accenture outsourced software

developer who lives in San Francisco or works in San Francisco.

I mean, you know, joking apart, if

you have any experience of professional services, like companies do not have lots of people sitting around waiting to do a build a big new project or do a big new piece of analysis or build a big new piece of technology or a new product or work out how they're going to redesign their stores or you know, work out where the

stores should be or try and work out why the churn is too high. All of those kinds of questions are

reasons why you hire Bain BCG, McKinsey on one side or Accenture Infosys, whoever on the other.

Or you hire a branding agency or

you hire an firm of architects or whatever.

And it's always like, well we could hire some architects, but why on earth would we want to have 15 architects on staff. When we would just go and hire

an architecture firm, we just go and hire an ad agency.

And so you're supposed to like completely reimagine all of the internal workflows of your company and work out which of

them could be automated really quickly.

With AI, that's a project. That's a project that needs like five or 10 people to sit down and spend a month or two working it out, and then actually doing it is another project. Okay, so you need to plug these

three vertical systems into these two horizontal systems and build a bunch of new

workflows and train people to do that.

Well, guess what? Who's going to do that? Because you don't have a bunch of people sitting around not doing anything. So on the one side, this is part of the model of some PE firms, which is that they provide support to their portfolio companies to do stuff. And on the other side, that's why you hire. Depending on what you're trying to do, you hire Bain or you hire Accenture, or you hire publicists to help you work that out.

[Lenny Rachitsky]

What's really just funny about this trend is you would think AI is going like consultants were going to be gone. No, we don't need all these people anymore. AI is going to do their work instead. Like the most cunning edge AI labs are the ones most investing in these folks. I think it's pretty surprising.

[Benedict Evans]

Well, one of the strands in my presentation. So I split the presentation into three sections. There's a section on capital, which is

basically, where is all this capex going? And all the model labs going to have differentiation.

And then there's a section on deployment,

which is basically, what does it mean for the software industry?

And then the third section is how does this change stuff? And one of the strands I tried

to pull together in the section on

change is what's the hard part of the job? Is the hard part of the job writing the code line by line is the hard part of the job. Like giving you the SKU or making the PowerPoint. Or is the hard part of the job something else? Is it the task or the job? And, you know, pulling that apart, Sometimes the task is the job. Like the classic example is like an elevator attendant. I live in a building that has an attended elevator.

We have a manual elevator. There's no button, there's a lever, and

the doorman drives you to your floor.

It's a vertical speedcut. It's like one of those trams in San Francisco.

They drive you to the store, to your floor, and then those all got

automated after the 50s and now you get and you press a button and pressing the button is a job.

So there were some things where the

button, the job was a task and the task got automated.

What happens? Much more. And this is why people talked about like the Jevons paradox.

Is this price elasticity? Because Jevons paradox is just price elasticity. Applied price elasticity.

If you make it cheaper to do something, what happens? Do you do the same for less

money or do you do more for the same amount of money?

Or do you do more for more money because you've got a new roi? And if you look at something like the history of accounting or indeed professional services, like, this is a joke I made on Twitter.

Back when it was Twitter was like,

young people won't believe this, but before

Excel, junior investment bankers worked really long hours.

And now, thanks to Excel, Goldman's associates, all the work at lunchtime on Fridays. It's like, well, why is that not what happened? You could make the same point in software development. Before, IDEs and libraries and operating systems developers had to write all the code. Now if you write an iPhone app, 90% of the code is written for you by Apple. Like Apple wrote the modem driver and

the graphics drivers and you know, the file system.

You don't need to write any of that. So we've got like a tenth as many engineers now.

Well, no.

And so then you kind of have to look at an industry and work

out, well, which is it and what is the hard part?

One of the analogies that occurred to

me here is to look at the

history of E commerce, which is that what Amazon does is it gets you the sku.

If you know what the SKU is,

if you know what SKU you want,

you want that microphone stand, you know this part number, you can go to Amazon and get it.

If you don't know what microphone to get, probably shouldn't start on Amazon, multiply that by many, many, many product categories. And so what Amazon does is get you the sku, but knowing what SKU you want is another job. You know, Claude, co can write you the code, but what code do you want? It can make you the features. Sure, but what features do you want? Who's your customer? What's the right product for that customer?

How are you going to take it to market? And long way of answering a question. Why do you hire McKinsey?

Are you hiring them to get a 75 slide deck? Well, narrowly, Claude, Cowork will make a

really, really crappy version of that. And you get all these kind of AI grifters on LinkedIn and Twitter and

so on, saying, hey, I made a McKinsey deck with Claude.

And you look at it and you think, yeah, that's a bunch of dog crap. That's not what you'd get if you from McKinsey.

But even if it was, that's not what you paid them for. What you actually pay Bain to do

is to go and walk all over your enterprise, your company and work out, yes, but why is it that you didn't do that?

And how do the politics of this work? And what do you actually need to do? And let's go and talk to your customers and work out what they actually

think as opposed to what's on the

first page of Google is all the other stuff. And the PowerPoint is just like the task, but that's not what you hired them for.

The same with Amazon versus the retailer. The same with software development.

So you've got that kind of split. The other analogy that occurred to me here is looking at the sort of class of industry that got steam loaded by the Internet because they had those two things and you could split apart. So you had the physical manufacturing or

physical distribution and then you had the other the thing, what was the actual thing. Like classic examples would be newspapers and recorded music.

So record companies do not think of

themselves as being in the business of manufacturing small pieces of plastic. But that was what they actually did

and when that went away, they were screwed. Same thing for newspapers.

Newspapers did not think of themselves as like manufacturing and trucking companies.

When you decouple that, then that becomes a problem. But often you kind of can't decouple that or that wasn't really the problem or you make that thing cheap and then all this other stuff happens as well. And so all of this is just vastly more complicated than saying, well, hey,

you know, we're just going to automate the accountants or we're going to automate the consultants. I mean there's two charts in the

presentation of the number of people employed as accountants, which went up right the way through the 20th century and has

gone up again since the beginning of the 21st century. So you have adding machines and punch

cards and mainframes and databases and ERP

and cloud with freight eats and PCs and the number of accountants keep going up.

And so why is that? Well, it's not, it must be more,

it's more complicated than automation even.

[Lenny Rachitsky]

Just looking at the most advanced AI companies. Anthropic OpenAI I just had Dan Shipper from every on the podcast. Everyone's just increasing headcount like the companies you would think would be least likely to add humans are adding many, many humans. And since your point, it's really complicated. What's your just kind of just on the job, the coming job apocalypse, you know, like Dario's talking about all the entry level people are no more jobs.

[Benedict Evans]

Just like, yeah, I mean there's a narrat point here which is that I would place I don't like argument from authority and I don't think the fact that you run an AI lab suddenly gives you or rather and if you're going to use argument from authority, then it should be relevant to the field. So like I'm interested in Dario's opinions

on where models are going to go in the next six to 12 months.

Not particularly interested in his opinions on

theories of labor and market value and competitive comparative advantage.

Like, yeah, maybe he had a course

on that at university.

So did I. So I think one needs to be a little bit cautious on like, well, Dario says, and that's setting aside like

the cynical view that he's just doing that department stock, which I don't believe at all.

So this kind of comes back to

my point about platform shifts.

Every time we have a new technology,

it automates away a bunch of jobs. And then that automation, whether it's price elasticity and the enablement of the fact that they became automated, unlocks a bunch of new jobs. And so you go back to 1800, like 90% of us were peasants and our major concern was are the crops going to fail? Because then we'll all go hungry or worse. And so ever since then we've been automating jobs and creating new jobs.

And you can always see the job that's going to go away and you don't know the new job because it doesn't exist yet.

And it's like something that sounds dumb anyway, like railway engineer. What's a railway? Why would that be a thing? Who would want to go that fast?

And so we've had that process over and over again.

This is what any first year economic student would tell you. We've had this process over and over again since 1800 and each time you

go through it, you get a bunch of frictional pain and dislocation and a

bunch of people lose their jobs and

a bunch of towns get hollowed out and it all sucks. But when you come through on the other side, we're all richer and we're

not worried about the crops failing anymore. And this is the process of the last 200 years.

So then the question is, is there some a priori reason why this would

be different to those?

Because like the Internet removed a bunch of jobs. PCs removed a bunch of jobs. There aren't many people working as typesetters

anymore, or telephone operators or typists. The Internet removed a bunch of jobs

and generally the jobs that go away

are crap jobs seen retrospectively. And the new jobs are better because GDP keeps giving up.

So is AI different? And so then there's kind of a couple of answers to this. One theory is, well, this is going to be way quicker and certainly the adoption of AI is quicker than previous technologies. But this is kind of because you're

standing on the shoulders of giants.

So you don't need to wait for

everyone to buy a piece of expensive hardware to buy a phone or a

PC or wait for the telco to deploy broadband.

It's already there. So of course ChatGPT can get 900 million WeChat users because there's already 900 million people on the Internet. Like in, like when Mark had recently launched Netscape in what was it, 93,

94, there were like 50 to 100

million PCs on Earth. So no, you didn't have 900 million users then.

But the point is then he didn't need to wait for like phone networks or microchips and before that you didn't

need to wait for electricity and you didn't need to wait for mass production.

So you're always kind of standing on the shoulders of giants.

There's always a compounding effect.

So yeah, this is faster, but the

Internet was faster too.

I think the other answer to this,

and this kind of comes back to

the professional services point is you talk to these Dimas on Twitter and they would act like every big company is

going to buy ChatGPT tomorrow and then

in two weeks time they'll fire all their stuff. And these people are more on something

is one of many reasons why the humans were more on complete failure to understand the way the world works.

And that was like the starting point,

why they then didn't understand anything else.

You know, typical big company, you know,

enterprise software sales cycle, you'll know this better than me. Enterprise software sales cycle is like 18 months if you're lucky.

You know, this is always the problem.

The enterprise sales cycle is shorter than the venture backed software funding cycle. Longer. Longer rather longer.

Like it takes you longer to get an enterprise deal than it takes you

to go between ramps. And this was always support, particularly for Sectors like aerospace or healthcare or something.

So I know people aren't going to

tear out SAP and replace it with

XYZ maybe in like 3, 5, 10 years.

Yes, that whole estate will look radically different and all those jobs will have changed.

But it will take, you know, 2,

3, 4, 5, 10 years and it will take time, sector by sector, and it will take time for people to work out. Oh, you could do that thing with this.

One of the companies I always remember that we looked at when I was

at Andreessen Horowitz is a company called Frame IO, which is video editing, video collaboration.

And there's nothing new there that you couldn't have done at least five years earlier and maybe 10 years earlier. And actually that's kind of a bad example because that relies on a bunch

of like, a bunch of stuff like cutting edge web technologies.

But if you go out and like pick, pick 10 random SaaS, companies that

were started the day before ChatGPT launched,

how many of them could have been founded at any point in the previous 15 years? The delay was somebody realizing, oh, that

problem exists inside that industry and oh, this is the way that we would solve it.

It didn't all happen the day after Google Docs.

It took like 10, 15, 20 years for people to invent all that stuff and work out that you could do that with this.

And so all of that is like the way of saying, well, yes, it is going to be quick, but actually no, it will kind of take a

while for people to work out how to completely change how their business works.

[Lenny Rachitsky]

Your view is so comforting because basically it's like, okay, this is a huge deal, but we've been through many transformations before and it's going to be okay.

[Benedict Evans]

Well, I have a slide towards the end of the presentation which the title is something like, this is going to

be completely different from everything else. Just like everything else.

And then the next slide is an IBM ad from the 50s which has

got this sea of white men holding up with in white shirts and ties all holding up flood rules.

And the ad it said, the slogan and the title of the ad is

it's an IBM ad. It says an IBM electronic calculator. This is before it was called a computer. It's an electronic calculator. It's the size of a fridge. Is like having 150 extra engineers.

How many people listening to this comfort list?

Like their company slogan is basically, we'll give you 150 expert engineers.

I mean, isn't that like the whole Picture called code 150 extra engineers for

free or not free. That's like a lot of money.

And yeah, that's what it gave you. And so, yes, we keep going through

this over and over and over again

just to kind of make that tangible.

I mean, obviously we couldn't be doing this without the Internet.

So there's a slide in my presentation

which is we could maybe talk about,

but it's a slide or chart showing

how many products are stocked in supermarkets in America since the 50s.

And the point of the slide is

to say that barcodes allowed supermarkets to stock way more stuff because they could keep track of it.

But making that chart, I had to know there was a thing called the Food Marketing Institute. And I had to have found out that they published a number for how

many schools there were in supermarkets every year.

And then I had to realize they'd been around since the 50s. And if I dug long enough, I might be able to bake a whole time series and I could make whole chart. Now imagine doing that in 1994. First of all, you would have no idea that exists. You really need to go and find a library where they publish and they publish that number and that the number's in that report. You'd have no idea. Then you need to find a library that had them. So you're going to spend like three

days on the phone and spend like $50 on like long distance phone calls

to find a library that has these. Or maybe you call the Food Marketing Institute and they say, yeah, sure, if

you buy a, you know, we'll sell

them to you for $500 each. So then, you know, you know, going

to get on a trip, maybe you live in New York or find that someone that has this and you, two

weeks later you've got the chart and you look at it. And then the other side of this

is the life of an analyst is you spend all day making a chart and you look at it and go, oh, that's not very interesting.

You spend two weeks to make the chart and then you look at it

and go, yeah, I'm not going to use that.

And for me, this was like two hours in Google. And so we forget how big a

deal the Internet was. That's a long way of saying it.

But we forget we've had these absolutely enormous changes and then we don't see it because it's like, that's the world.

[Lenny Rachitsky]

The world's always been what's different potentially this time. Just even though your code is, it's different, this is, everything's going to change. Like, just like last time, like, the big difference obviously is AGI might emerge and super intelligence, where that is, could, you know, does the work of humans, can do a lot of this stuff for us, can actually replace jobs. Just like thoughts on that element of this transformation we're going through?

[Benedict Evans]

I don't know. This is one of the ways I struggle to write about AI is certainly in 2023, early 24, all the questions were questions you could have asked in December 2022. The questions didn't really change and the

strategies didn't really change.

And I think the AGI question is kind of the same. I mean, the thing that the observation one can make, we have no theory

of what human intelligence is. We have no theory of why these

models work so well.

We have no theory of how much

better they will get.

So we're all just kind of vibe forecasting as to what will happen.

And then you can have the 2am doped out.

Philosophy students talking about, hey man, is this consciousness?

Maybe we aren't conscious either. We just think we are.

Yeah, great, thank you.

I think the one thing one can observe today is so we have no idea.

We don't know.

We can guess, but we don't really

know where this is going to end up.

What I think you can say today is that there's a lot of kind

of redefinition of terms.

So I think a quote I used

in my presentation late last year was

an AI scientist called Larry Tesla who

said, AI is whatever machines can't do yet, because once machines can do it, people say, well, that's your software.

And so certainly, I mean, I did do a poll on social media every

now and then asking is machine learning still AI?

Because I've certainly heard people say, oh, that's not AI.

That's just image recognition.

That's not AI.

That's your sentiment analysis.

So AI, it's a bit like the word technology. It's like if it's new, then it's technology. But in the 60s, airliners, jet airliners were technology.

Now a jet airliner is in tech.

And so there's a sort of sense of AI is like a moving target,

is whatever just started working.

And I think the point here is now clearly you can see people redefining

AGI to mean the stuff that works now.

So is aei. What's the definition now? It's like it can do a certain percentage of economically valuable work. Well, that's a very different thing to it has a soul and it's fucking alive. Because a Database can do that. Like an IBM mainframe in 1975 could

do a meaningful percentage of economically valuable work that was previously done by people.

And it turned out there was a whole bunch of other stuff that it couldn't do that we didn't do then, we didn't know existed. So there's a lot of creative redefinition here. Super intelligence, I'm not sure is super intelligence more than AGI or less than AGI? Because last year I thought superintelligence was

really good, but not as good, not actual AGI.

And now it's like, oh no, no, we've already got AGI.

But super intelligence, that's really hard.

So all these terms are like what even. It's funny, I was having an argument on Hacker News this morning. You remember the argument, which is never a good use of time, but you remember the argument of like people would argue about whether crypto is blockchain or

whether blockchain is crypto.

There isn't a right answer to that.

Let's just be sure.

It's important to understand what you mean when you say that, but there isn't

a correct answer to this.

Are we going to get to something that has human level intelligence? We don't know. I don't think we have any way

of answering that question.

Maybe, maybe not. You can make arguments either way. Meantime, in the meanwhile we've got this thing that's clearly kind of a completely transformative technology. And maybe the serious point here is you don't have to believe even if the model stopped, you're getting better tomorrow. If this is it and we hit a brick wall tomorrow, this is an incredibly useful technology that's going to change

the world and get world out over the next 10 years.

So you don't have to believe in

any of that stuff to believe that this is a giant deal.

[Lenny Rachitsky]

Something that's definitely changed. I had your former boss, Mark Andreessen on the podcast and we didn't actually talk about this during the conversation and he brought it up before we started recording and I never got to it is he had this insight that the opportunity set for companies now is so much larger. We used to have no trillion dollar companies. Now we're going to have dozens of trillion dollar companies. Just like the size companies can grow to is going up so much. Evaluations also go up along with that. And his point is just people haven't really groked just how large companies can get now like everyone's hitting 100 million ARR in like 5, 5 months, 6 months. Just thoughts on that.

[Benedict Evans]

Yeah, I mean, this was his whole

software eating the world thesis from 15

years ago, whenever it was. Yeah, you know, the TAM gets progressively

bigger because you can address larger and larger parts of the economy. And so, you know, if you think about the kind of the classic platform

shift framing that, you know, mainframes are, I think peak mainframe install base was

something like 70, 80,000 units. I mean, slightly fuzzy term. What exactly is a mainframe and what's the difference? At what point does it become two mainframes as one? But something like that, that order of magnitude.

And then when the Internet kicks off,

there are, As I said, 50 to 100 million PCs on Earth. Maybe today there are something over a billion, one to one and a half billion, but obviously a lot of those are corporate. It's like 7,800 million consumer PCs in the world. There's about 5 and a half, 6 billion mobile smartphones in the world, which is why you can have 900 million weekly activities on ChatGPT.

And so there was this narrative like five years ago, well, we've run out of people, so the next thing can't

be an order of magnitude bigger.

Which was true up to a point, but that was like the wrong model because clearly what's happening now is you're

moving in another direction, is you're just branching out and automating big new swathes of the economy. Now back to your job point. You could argue, well, we're just going to replace all the people with AI and all the money will go to Sam Altman and Mark can buy himself another gulf stream.

I think the add to the fleet. I think the kind of.

The other answer is it's back to the lump of labor fallacy and the last 200 years that each of these technologies removes a bunch of jobs, creates a bunch of new jobs, creates a bunch of new value, unlocks prosperity for all of us. And that's painful as you go through it, but it always creates more value.

And so here you could certainly make an analog to, you know, the useful analog to the electricity industry is just

saying how that electricity became part of absolutely everything.

And software has been kind of slowly

working its way out. You know, the analogy would be electricity in factories and then electricity sort of slowly spreads out. And so that would be the point again that, you know, it slowly spreads out to do more and more things. And so you knew more and more value and a bigger and bigger contribution to the economy.

It also, of course, disappears inside things.

And the other side, the point of My capital section in the presentation is there's this sick quote from Sam Altman where he said, we're going to be selling electricity, we're going to be selling

AI intelligence on a meter like water or electricity.

And you look at this and think,

my dear, sweet child, you need me

to explain the margin structure of the utility industry to you

because guess what? When you watch television, the TV company isn't paying a percentage of your monthly

bill to the electricity company.

When you wash your clothes, Bosch isn't paying a percentage of the price of the washing machine. And clearly this is the much more specific tactical question at the moment is do we even end up with three

giant models or does it become hundreds of models and open models and local models and so on?

And even if we do end up

with, say, pick a number, three to six to ten giant foundation models that cost hundreds of billions of dollars a year, fine. Do they get all the value from that now? I started my career as a telecoms analyst and so still pay attention to it a bit.

Global mobile industry has revenue of about

a trillion dollars a year, maybe a bit more now, and it spends about $200 billion a year on capex every year. Total telecoms is about 300. Mobile is about 200. It's about 15 to 20% of revenue every year. And if you look at a chart of mobile data consumption, it's an exponential curve, like perfect curve going straight up.

And the number now I think it's

about, you know, 1500 to 2000 times what it was in 2010 globally.

And the stocks have gone nowhere in

25 years because it's an ex growth, low margin commodity utility where they're selling

this objectively amazing piece of global technology infrastructure that has enormous complexity and enormous sophistication. But all the cool stuff is made by you, it's made by the people listening to this podcast.

It's made by somebody else.

This was that kind of pivotal moment where the telcos thought that they would

do all the stuff that you did on your iPhone.

And not only do they not do it, but Apple doesn't do it either.

It's all further upstack.

And so this is kind of the elemental question right now around foundation models is does the model do the whole thing? Do you just go to the chatbot and get the chatbot to do the whole thing? Can the model companies keep building these,

like Claude for X, claude for Y

things which to me look very much like what you see if you hit

file new in Excel, like the templates, but like all of those are actually billion dollar companies as well.

And if not, no. Does it all have to be apps?

Quote unquote, whatever app means.

And if it all has to be apps, who builds those? Well they can't all get built by

the model labs, just as they didn't all get built by Microsoft.

And so if they're all built by other companies, does the models foundation models have leverage up the stack the way Windows did? Or is this more like AWS where

like if you're a, I don't know, an engineering company or a law firm buying a piece of software, you don't care which cloud it runs on and

you don't have to like standardize on

AWS because that's where all the software is. And like the developers all standardize on AWS because all the customers use AWS. That's not how it works. That's how Windows O iOS works, but that's not how code works.

And so it does sort of seem to me that like if the chatbot

isn't the UX and it needs to

be apps and the model companies aren't going to build that and the models themselves are basically commodities, at least as

you can see them as users, then

why would the model companies have pricing power and wouldn't all the value be further up the stack? Aren't you basically have you got like

three to six companies selling a commodity

at marginal cost now obviously the semi

analyst guys are like no, no, no, no, no.

There's going to be infinite pricing power forever.

I'm sorry, I'm exaggerating but I think

you have to really important to kind of draw a distinction between where are we now where you have radical price disequilibrium and you've got these.

What's the guy, the open claw guy spent one and a half million dollars on tokens last month.

But that's like somebody getting a 50 grand mobile data bill in 2010. That's temporary. What is the steady state equilibrium point where all of these lines, the liners

on the chart kind of get lined up and we don't have this kind of weird crazy stuff going on and

then will you have pricing power or have you got like three or four

or five companies kind of all selling

the same thing and so then you

should have a pricing price, you should have lower pricing and lower margins and the value should go out.

[Lenny Rachitsky]

Stack.

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[Lenny Rachitsky]

That's vanta.com Lenny A really interesting takeaway here is that your sense is over time the foundational model companies anthropic OpenAI others will their margins will get squeezed, they will not be as successful as they are today. And the bigger opportunities in the application layer, the people building on the models, the wrappers.

[Benedict Evans]

Yeah, I mean this is a very sort of deterministic thesis which is the models companies. Crucially what I said is the models don't seem to have network effects, so there doesn't seem to be a winner

takes all effect where one of these will run away ahead of the other.

So you should have competition indefinitely. If you have competition indefinitely, you don't

have different primary radical differentiation of what

the product is, then why would you have pricing power? And meanwhile, if you need to have thousands of applications that are all different built by different people, those can't all

be built by the model people.

So it should end up looking more

like cloud than it looks like Windows.

Now that may be completely wrong. And you know, one of the points I make in the presentation is like imagine having this conversation about the Internet in 1997, like what would you have got right? And or indeed having it about mobile in 2000. You know, you would not, you know

most, you would have missed almost all of it. You certainly would have said that like a has been PC company from cuppuccino would win the whole thing. Like no one would have said that. And a search company with like a weird logo like search what's that got to do with mobile? Like no, forget it. You're an idiot.

So like we should presume we don't know, but they're all, you know, these sort of basic building blocks of like,

well, but why would they have writing power?

I don't know. When I was a baby analyst in

like 99, we went to see a

dot com company in the UK that

was trying to do online selling computer cards, components online. And like they had this whole model and this whole story and the brand and buy the whole thing. And we went up to see them and we're on the train back from Birmingham and this sort of, sort of senior banker called David Tate, we're all

sitting talking about it and Tati says it's a low margin reseller.

One time sales,

you can say dot

com all you like, it's a low margin reseller.

And I think that's the kind of, the crux of this is they're undifferentiated commodity infrastructure providers.

There's a lot of science to it,

but there's a lot of science in mobile. I mean what do you pay for flat panel screen?

Like there's Nobel prizes in flat panel screens. They're still a low margin commodity.

I look forward to be proving wrong. Proven wrong, but like, hey, that's what it looks like now. This is great.

[Lenny Rachitsky]

So I know, I know you're not an investor, I know you didn't actually do investing at a 16z even though you work for a 16z partner, partner just sit around and pontificate. Partner. Would you, are there companies you would invest in? Like if there are a couple companies you'd invest in now, is there some on that list or categories even?

[Benedict Evans]

You know, I mentioned briefly that I was an analyst. I was a, I was a sell side equity analyst. I was not a very good sell side equity analyst. But partly because I was not interested in talking to clients, partly because I was not interested in share prices which would seem to be like a disqualification to be an equity analyst.

And you know, I don't, you know, there's, there's, there's like a huge difference

between being right and being early. And there's a huge difference between the right company and the right price. Now, you know, deterministically you can look across the market and say, well you

know, it's like, you know, like the bell curve Iqmium and the guy with

50 and the guy with 200 are both saying, Jeff Bezos, smart guy, I buy stock.

And you can certainly overthink all of this.

And you can look at Google, Apple, Facebook, Amazon and say hard to see a problem for them really with all of this.

You can certainly see questions for all

of them, and one of them may drop the ball.

But it's worth kind of remembering what happened in mobile.

The Internet was just like a big, obvious platform shift.

The funny thing about mobile is that

some companies missed it completely.

And for some of them, it really didn't change anything for Google, it didn't change anything for Meta.

This was great.

This is a way better way to

do social than on PC because you've

got a camera, notifications, and it's on your phone all the time with you Amazon. Right?

What does this change? Doesn't change anything. I mean, I'm massively oversimplifying here, but

the point is now, meanwhile, Yahoo Mail

fails to make the jump.

There are companies that were already kind

of dying that failed to make the jump. Maybe ebay, you could argue about individual names.

The point is that we went through that shift and it didn't change anything

for half the industry, half the Internet industry.

And so I think that you could kind of propose a little bit of that here.

While Steven Sinowski at a 16z who used to run Windows would always say, incumbents always try and make the new thing a feature.

And sometimes they're right, sometimes it's a feature.

[Lenny Rachitsky]

Actually, along those lines, something I wanted to get your take on. There's this thread that's been happening across a bunch of guests which is around distribution becoming a bigger and bigger moat because as software is easier to build, everyone's launching products, everyone's trying to compete for attention. It's getting harder and harder.

[Podcast Host/Announcer]

It's always been hard to get people's

[Lenny Rachitsky]

attention, but it's just like the noise in the market is just going up like crazy. And to me, that tells me distribution is becoming a more and more valuable skill and asset. And it also tells me incumbents are going to be a lot more successful because they already have distribution versus a startup that's trying to break through.

[Benedict Evans]

Yeah, I mean, there's like a version of, you know, the Drake meme of,

like he says, I don't like that. I do like this. It's like, you know, I don't like seeing GPT wrappers. I do like harnesses.

So, yeah, I did spend some time talking about this in the presentation I

did at the end of last year,

that if the product is a commodity,

then the distribution is what matters. And I wrote a thing about ChatGPT earlier this year, OpenAI earlier this year. How do they compete?

Well, there's an obvious comparison here that

a lot of people made is with

web browsers that fundamentally web browser and There's a distinction here, I think, between the web browser as product and web

browser rendering engine, in that the rendering engine can be better or worse, but

the browser product is just like a really thin wrapper for a rendering engine.

There's an input box in an output box.

And what else? Which is like, what's the last innovation

in browser design, like tab browsing just 20 years ago, 25 years ago.

And every now and then somebody tries

to innovate in browser design, and it never works because you found the platonic ideal. It's like trying to innovate in smartphone design.

It's a glass rectangle.

There's nothing you can do there.

And so what happened, of course, is

that Microsoft uses distribution to break their work, to break in. Then of course, what also happens is setting aside the lawsuit is that it

turns out that winning browsers doesn't matter

anyway because the value is further upstack. And so Microsoft wins browsers for like five, six years, and it doesn't matter. It doesn't get them anything.

And so clearly what's happening now is

that Google is using distribution to drive Gemini.

And like, what's the difference between Gemini and fraud? And if you're using this stuff all

day, then, you know, but like normal person, there's no difference.

And the same thing with meta, you look at survey data on which LLMs people use. Even before the new thing, like the llama thing meta was behind, it was up there between ChatGPT and Gemini, which if you're in tech, people have completely

written it off, but it was like they'd sprayed it on every surface and

it wasn't that bad. It was fine. So distribution of an adequate product when

the field is basically commodity distribution on

brand become a big deal. You can see that in, you could

see that in the, like the strategy, OpenAI strategy late last year was, you know, people called it, you know, everything everywhere yesterday. And so they were just kind of trying everything to kind of work out how they would get that, like, how can we get a flywheel? How can we get distribution? How can we get something that sticks? How can we get people something, something that people uses before Google and Meta and Amazon spray it everywhere and get everybody using that one. And then you've got like the inertia and the power of the default and like, why would you switch?

Obviously meta.

Apple is kind of the last penny to drop here. That was this sort of slightly weird opening ideal. And now there's even weirder story that open a, I want to sue Apple. Good Luck with that.

The funny thing about the Apple deal

thing is just not to go off

on a tangent, but like if you go back and watch the WWDC from 2024, the whole second half of it is Apple Intelligence that was like the most compelling vision of a personal AI assistant.

I'm still still the most compelling vision I've seen.

They then couldn't ship it, but then neither is anybody else. And you watch it again and you're like, okay, so you want tool using

agentic on device AI with no prompt injection and no hallucinations and a completely

standardized API system across 10,000 apps with

intents that all work perfectly.

And like, well that sounds good to me. But I'm not surprised they couldn't ship it.

But nobody else has shipped that.

But that vision was great.

You know, I really want to see what happens at WWDC in a month. Do they actually ship that now? Powered by Gemini.

But that's also another point, is like, okay, there's going to be the AI intelligence, whatever we call it Gemini Intelligence on Android and then there's going to be Apple Intelligence on iOS which is powered by Gemini. But it's not going to be the same set of products. The model is just like the dumb

thing underneath the funny way of putting it, the dumb thing underneath that powers the feature. The model is the commodity that powers different decisions about what the feature should be and what different distribution.

And in that situation, of course, Apple's

got like a billion devices that can run this on edge. And Google has this wonderful marketing slogan coming soon to our most powerful devices,

meaning it won't work on most Androids.

So again, distribution questions.

[Lenny Rachitsky]

Interesting. Google iOS next week. So we'll see what they launch.

[Benedict Evans]

Oh no, they launched Android.

It just shows how like how Mozart was it today.

Well, no, they launched it last week. I mean, which is like, it just illustrates how much we, we stopped paying attention to Android and iPhone. Like Google did a whole big thing last week.

They've got, they're replacing Chromebooks with Google

Books and they've got a new Android intelligence powered by Gemini that will roll

out to like the five people who bought a Pixel phone. You don't work for Google.

[Lenny Rachitsky]

Yeah, I want to go in a slightly different direction. Something that I'm curious if you're following is just the anti AI sentiment that feels like is growing. Feels like if you've seen these surveys, AI is like less popular than ice. People are trying to stop data centers from being built. I think Eric Schmidt just did a commencement speech and People were booing him every time he mentioned AI. Just like, where do you think, what do you think is going on? Where do you think this goes over time?

[Benedict Evans]

It's interesting and it's a big sort of fuzzy mess of different stuff. I think there is like tangible, like

my electricity bill went up, which applies it actually in a very small number of places objectively.

But it did.

And this is a question.

The water thing is weird because it's just like completely fake and I should qualify.

Explain what I mean here. Data centers use water for cooling. It's mostly closed loop, but the number

of data centers relative to the total

amount of water use in the USA is tiny.

I actually went and dug into this at the Livermore Lab, did a study

at the end of 2024 where they estimated US data center water consumption and it came out at about 0.017% of US water consumption. Now obviously if you live in a small town and you've got one well and like they capped the well and gave all the water to the data center, then you're really pissed off.

But like that's like, that's a planning

problem, that's not a data center problem. In generality, yes, this is, you know, data centers are what, like 5% of U.S. energy and might grow 1% a year for the next, next five years, 1 percentage point a year. But the water stuff is just nonsense.

And then you get into more tangible

like, well, what is happening with this?

Is it taking jobs away where you

can watch a bunch of three hour podcasts of economic economists talking to each other and main answer is we really don't know yet.

There's a bunch of charts that kind of say yes and a bunch of charts that kind of say no. And clearly there's a slowdown in employment

of, you know, 18 to 24 year olds. But that seems to be the same for people who do and don't have degrees and the same for people in fields that look exposed to AI and

fields that don't look exposed to AI. So there's a lot of like econometric argument about this. And I mean, there's a border point

here in fact, which is different point

here that like we have very little

data on what's going on in AI from anyone.

The model labs don't tell us anything. They don't give us any meaningful usage information.

They give us these weird studies of how many people use this for this and that.

They don't give us a daily active use number. We do not have a daily active

user number for ChatGPT.

It's crazy. And all the data comes from academic economists trying to back stuff out of BLS surveys or consultancies and marketing agencies,

like spending a whole bunch of Money to survey 20,000 people and saying, what are you doing with this stuff?

We don't have good data on what's

going on and how many people are

really using this, the employment question. Hence there's a lot of people looking

through all the stuff that the U.S.

census collects and trying to work out,

well, where can we see this? Can we see productivity? What can we see?

And the answer right now, I think,

is there's no clear consensus that we're seeing an impact on jobs. But of course, politically, that doesn't matter. If you're a student and you can't get a job, and that clearly is

an issue, whether it's because of AI

or whether it's because of Trump and tariffs is a different question. Then you get niche things like people who draw book covers for young adult romance novels are very upset that now you can get a picture of a naked woman on the back of a dragon flying through over a volcano without paying them. So I'm sorry, I'm being deliberately unkind, but there's a little. Particularly like novelists, people who write ebooks, there's a huge culture war over whether it's okay to use AI. There's this whole sort of AI slot question, and if you saw the number that 30, 40% of new podcasts generated by AI.

So there's a big fuzzy mass of questions. Some of this, I think it's a

little bit the backlash we had around social, but much more compressed and like social.

Some of the backlash around social was true, and some of it was sort

of true, and some of it wasn't, you know, always like, exemplified in the whole, like, Facebook sells your data thing, which is just A, not true, and

B, the people who believe it are absolutely adamant that of course it's true.

And you're, you're obviously a lunatic for suggesting otherwise. You know, it's like the line from, from Jonathan Swift that you can't reason somebody out of an idea. They weren't reasonably deep.

So you get this kind of wide, it was a long way answering question, but you got this kind of wide kind of spread of ideas, just as you kind of did with social. There's like 20 different things, some of which are really real and some of which are really not real, and a lot of which are kind of a

fuzzy mess in the middle.

All of which means that meanwhile, you've

got Trump saying he wants a new

executive order on Dangerous Models, which I

actually don't think is the thing that drives the backlash. They're worrying about myth or cyber. I don't feel like that's a Main Street America conversation, but that's the thing that got Trump interested in this stuff.

[Lenny Rachitsky]

Again, let me go kind of in a tangential direction, something that I like to ask folks that have kids that come on the podcast, especially people that are thinking so deeply about where things are going, knowing what you know, about just where the world is heading, what AI is going to do to the future, how are you changing the way you raise your kids? Just what are you teaching them differently potentially that might help them in the future?

[Benedict Evans]

I don't know.

I think there's a curve here in

that if you've got kids who are going onto the job market in the next year or two, then everything is up in the air and no one knows how this is going to work.

If you've got kids who are going

onto the job market in like five years, then who knows? But staff will have settled down a lot by then in probably unpredictable ways. So I could be a lot more worried if I had a 21 year old. You know, I don't, I've got, you know, a kid in his sort of early teens. So it's a different, those, those questions vary.

Then you've got a lot of the

questions that were the same before ChatGPT around, you know, the collapse of gatekeepers, the, you know, you know, should you really believe what that influencer on TikTok says? And, you know, where exactly are you getting your understanding of what's going on in Israel and all of those kinds of social media, internety media consumption kinds of questions.

I don't know. There are people who are like super,

super intentional about, you know, every minute of their child's life. I'm not.

I kind of recall, you know, the

George Carlin line, you know, that anyone who drives faster than you is a maniac and anyone who drives slower is an idiot. And that certainly applies to parenting. So I'm, you know, like, everybody thinks they're somewhere in the middle, but, you know, I don't have, you know, a deeply systematic and widespread and coherent, like, plan for this is what my child is going to be doing in 3, 6, 12, 18 months time. I'd settle for him not breaking his Chromebook again.

[Lenny Rachitsky]

I like that you're just. General vibe is, it's going to be okay, guys. It's going to be okay. Yeah.

[Benedict Evans]

I don't know if you, I think

if you, you know, maybe this is because I'm British and we haven't had political violence in 500 years. And I think, you know, maybe if I came from Iran, I'd have a different attitude to being calm about the future.

I think there's a layer of like,

yes, this will change a bunch of stuff and we'll need to worry about it. But that's kind of a constant. We've always had that.

I remember in the whole wave of

the panic around social media, I dug up so a whole bunch of books in the late 70s about databases. There was a whole panic about databases.

And again, half of it was true. Like, you know, if everybody's like police records and arrest. If all police records and all government records are online, then that's different. If you think about, for example, the

deep nudes, deepfake nudes issue, for example,

there's like a dumb reaction to this,

which is to say, haven't you heard of Photoshop?

Which is true, that a 15 year old kid couldn't use Photoshop to make hardcore pornographic nudes of every girl in

their high school and send them to the whole school in one afternoon and

[Lenny Rachitsky]

turn them into video.

[Benedict Evans]

Exactly.

Even. Well, yeah, even more. And now they can. So like, that is different. It's kind of like, you know, the challenge of social. You know, the thing people would say

in the 90s is it's great, you can be, you know, the only gay kid in your village and you, you

can find other gay people and you can find your tribe.

And guess what? It turned out you could also be the only Nazi in your village or the only pedophile in your village or the only someone who wanted to look at child porn.

And like, yeah, now you can find

the other people who like looking at child porn and they'll tell you it's great.

So, oops, we connected everybody.

And unfortunately that meant we connected all the bad people and all of our own worst instincts and every problem in society.

And so that will happen again with AI. You know, we can.

Deep fake news are like the obvious thing. We can see now there will be a whole bunch more of this stuff. Stuff.

But there's also, and you know, something

a kind of technical audience should know about.

Have you heard, do you know about the post office scandal in the uk?

[Lenny Rachitsky]

No.

[Benedict Evans]

Okay, so sidebar here.

So in the uk, post offices are

mostly franchises run by small business people. So they're run by like pharmacies, classically very often Indian immigrants, second generation Indian people.

And the post office, like 15 years

ago, rolled out a new point of sale computer system. So they have a separate counter in the back, that's the post office.

And so the post office rolled out

this new computer system built by Fujitsu that had a bunch of bugs in it that showed shortfalls and cash. The post office looks at this and says, aha, we knew these people were stealing from us. Hundreds of people get prison, bunch of suicides, bunch of bankruptcies, people lose their homes.

Meanwhile, people from the post office and

people from Fujitsu are going to court and swearing there's no bugs in the system. And nobody else has had this problem.

This is 1970s technology. That's really the point that every wave of technology comes with ways that you

can ruin people's lives, either deliberately or by accident. This is the whole thing of Chinese mass surveillance is deliberate. This is maybe people should go to prison, maybe not.

But we have this. With every technology, we have a bunch

of ways that you can ruin people's lives. And you have to be conscious of that and also kind of not panic about it.

[Lenny Rachitsky]

So maybe following that thread and coming back to the kids thing and the jobs thing, is there like a job you are steering your kid away from, and is there a job you kind of think you want to steer them towards?

[Benedict Evans]

I don't know about that. It's probably a little bit early yet.

He's not quite at the like, I want to be a fireman stage, but that might be a great job. Yeah.

And certainly, you know, if I look

at my career, you know, I started as an equity analyst and then I went and worked in an industry and then I was a consultant.

Like, you know, the days when you

kind of knew what your career was

going to be or over, you know,

there were certainly some people, well, you want to be an architect, you want to be a software engineer, you know, you want to be X or Y.

I don't know, I think, you know, the only, the only kind of thinking

I have here is that you have, like, you slowly work out. There's a bunch of skills that you have, and there's a bunch of, like, jobs that make, that makes you good at. And then there's a bunch of stuff that people will pay you for, and you want to get at least two of those and preferably all three.

[Lenny Rachitsky]

Okay, so zooming out a little bit, let me ask you a meta question. What's a question about AI that you think nobody's asking yet or not enough people are asking that we should be asking ourselves.

[Benedict Evans]

Sure. I mean, we talked about like value capture.

Like obviously this is a whole. Everyone is asking, like, I'm not sure how many people are asking whether model labs have pricing power. I think a lot of people are just presuming that the situation today will continue or that of course they will. So I think that's maybe a question that not enough people ask.

I think the question I pose towards

the end of my presentation, which we talked about earlier, is like, what's the task and what's the job? What is just the thing that becomes a button or make the SKU versus what are people actually hiring you for? Is that kind of a useful way of thinking about this?

And clearly there are going to be

some jobs where no, that is just a task and that job gets automated away. But there's a bunch where that kind of isn't the question.

The way I actually pulled that together. At the end of the deck was

a chart of global recorded music revenue, which as you may know, is kind of a U shaped curve, more or less. So it's dropped by about half from 2000 to 2015 or so, and since then has come back about to about 75% of the peak, adjusted for inflation.

And the way that I look at

this is to say, and that's driven by streaming.

And I kind of looked at this

and said, well, the first half of this chart is saying what happens if I don't have to pay $15 to get a CD to get that track?

The second half of the chart is saying what happens if $15 a month

gets you all the music that there is? So it's kind of a completely different sort of question.

And you could, you know, that's a

way that you could look at Uber or the way you could look at Airbnb, all these kinds of companies is

that to begin with, you do the

old thing, but more with every new technology, you do the old thing, but more of it on the new place. So, you know, you put Flickr on mobile, you print out your emails, and

then you make new things that are

only possible with a new thing.

And then maybe you go a bit further and you kind of completely redefine the question and you make something that

isn't that at all. You know, Spotify is not an online music store. It's something else.

And right now, you know those questions, you only even know what the question

is after it's been asked. And you built a billion dollar thing that lots of people use because like, obviously Spotify look crazy and people look crazy and you look crazy.

But that's the sort of. I think the way to get at what this means is you have to get past. We do the old stuff, but more. And you have to get to.

What do you do that's different because of this. What does this change? What wasn't possible before? What gets unlocked as opposed to just doing the old thing, but more of it.

[Lenny Rachitsky]

Yeah. Just to support this kind of general theme you have of. It's like we don't know what is going to happen. Like, this is unprecedented. If you, if you were to zoom out like a few years ago, maybe three years ago, four years ago, the last profession you think would be automated is engineering and coding. It's like that feels like the hardest thing. That's like, we're going to need people to build these things now. It's like the most transformed role of any role. Like you went from writing all your code to 0% of your code is AI.

[Benedict Evans]

It's almost like you didn't realize, you

didn't realize it was boring manual labor that could be automated. You thought it was something else. It's funny, I mean, I was looking at this. There's a sort of US government called data set, called ONET or something like

that, which tries to kind of analyze every single job.

And then people try and kind of score it and they try and say, well, you know, this profession is x or Y percent exposed to AI and AI can do z percent of it today.

I think this is just the most

ridiculous bunch of deluded horseshit.

And there's two reasons for this. The first reason is that this is like, ironically, this is the logical systems

problem, the expert systems problem. The problem with expert systems is like, for anyone who doesn't know, you try to recognize a picture of a cat. And so you start building up logical steps. So you make an edge detector and then you make a fur detector and you make an eye detector and you make an ear detector. And 15 years later you've got 700 steps and it doesn't work.

And this is what happens when you try and look at a profession and

sort of break it down by which bits can be automated and which can't. You, you can't describe a profession like that. Well, at any rate, we can't.

You can't kind of look at a senior partner at a law firm and

say, well, 17% of their work could be automated. Like, this is horseshit.

You can't do that. I think the other side of the

fallacy though, is to talk about taxi drivers.

So, you know, if we'd been having

this conversation in 1997, it's like the Uber test. Imagine we're in 1997. What will be crossed by the Internet?

Well, newspapers will be fine because they'll

save money on the printing bills.

This is like a joke, but people

said that newspaper, the Internet will be great for newspapers. Their printing bills will go down. Well, yes, but.

No, but the other side is, well,

obviously, like taxi drivers, you couldn't automate that with the Internet. It's got nothing to do with the Internet. Maybe you'd have Internet booking, but like, no, that's not going to change anything. And of course it completely changes the whole thing.

And so, like, the example I saw

the other day was like, things that won't be affected by AI personal trainers.

Okay, So I take my iPhone and

I balance it on the metal piece

with the camera pointed at me and

I ask an AI to build me a training routine and watch me and tell me if I'm doing it right.

Why do I need a personal trainer? Now, that might be complete nonsense, but that's how these things work. Like the stuff that you don't think is you can't predict which things are

going to be exposed necessarily, or a

lot of the big companies are things

that didn't look like that would work and didn't look like that was exposed. The other side of this, of course, is this is one of the charts at the end of my presentation is comparing Uber and Airbnb, because this is like the cliche from Mark and recent that Uber doesn't sell software to taxi companies. Airbnb doesn't sell software to hotels. Okay, now let's go and look at the market impact. Well, a whole bunch of cities work either demolished taxi business and made it much bigger as well. The TAM became much bigger and everyone switched.

Airbnb's impact hotels, if you actually go

and look at the numbers, is pretty marginal.

They carved out this whole other business and maybe they slowed down the growth

of hotels a bit. But, you know, my wife flies to Milwaukee next week. She's going to land at 8 o' clock at night. She wants to go to a hotel, she wants to have room service, she needs a bathroom bath. She needs, you know, needs a gym at 6 in the morning and then she gets 7 in the morning. She's going to drive to the client site. She's not going to stay in an Airbnb. Like, absolutely zero chance she's going to stay in an Airbnb and half of the hotel business is travel, is business travel.

And as soon as you actually get into anything, then it gets complicated. I remember somebody on social media said

a problem with Benedict is his answer to everything is it depends. It's like, yeah, it does.

It depends. So it's back to my 1997 point. You can say some of this,

but

you have to have that humility.

[Lenny Rachitsky]

Yeah, I'm coming back to this phrase you use. Presume radical uncertainty is a nice core thesis here. So knowing all this, just, it's hard to tell. We don't know exactly where it's going. Things are going to change a lot, but it'll probably be okay. Broadly. Just a lot of people listening are pretty worried about their jobs and their careers and how much the world changes. What would be a couple things you recommend people listen do, knowing what you know, to be more successful in this future?

[Benedict Evans]

Well, I should just kind of wind

back on what you just said. It's like, as Keynes tells us, in the long run we're all dead.

So, you know, it's all, you know, like, on average, you know, on average, nobody died in World War I, great. But if, you know, if you're, if you're a 19 year old in 1914,

you've got a 1 in 3 chance of not coming back.

So, yes, you know, clearly there's a

bunch of professions where this is a major question, and particularly if you're an associate or would have been thinking about being an associate, this is a major question.

And it's very unclear how those professions

are going to play out. It's very unclear what happens to the pyramid structure of professional services. The only answer I think one can have is

don't stick your head in the sand and say, I hate all

of this stuff, because that gives you a great feeling of moral superiority. And you can go on Blue sky and shout at everybody, shout at each other about how evil AI is, like, great, I'm happy for you, but that's

not going to help.

What helps is you diving into this, completely submerging yourself in it and coming out, understanding what you can do with it, how this changes things, how can you, how you can be a great

hire and that may still not help. But you know, if you're going into a law firm and they're like, well, we hired 100 associates last year and

this year we're only going to hire

50, going to the interview and say,

well, I think AI is bullshit and I'm never going to use it, is probably not the Right mood.

So, you know, you can, that, that, that may not be particularly comforting, but I don't think there's, there's an alternative

is, you know, you have to dive into this and absorb it and internalize it and think about what it means, just as, you know, you and I did with mobile and with, with the Internet.

[Lenny Rachitsky]

I think that is actually very actionable and, and very consistent advice on the podcast is just, just do stuff, build it, don't sit around and pontificate and be pissed at what's happening. To close us out, I'm going to take us to AI corner, a recurring

[Podcast Host/Announcer]

corner of the podcast.

[Lenny Rachitsky]

And the question to you is just what's one way you used AI and use AI in your work or life that is really interesting, something that other people might be inspired by.

[Benedict Evans]

I don't know, I struggle with this

question because I'm sort of the lawyer looking at ChatGPT. So, you know, the stuff that I would do, that I would automate are sort of precise information retrieval task, which is precisely the thing that this is kind of worst at.

And you know, that's not a criticism,

it's just an observation that kind of the kind of stuff that I would want a machine to do for me is the stuff that AI kind of can't do for me very, very, very well at the moment. I use it for proofreading, I use it, you know, for images. I used it redecorating my apartment. That worked fantastic.

You're well at that.

Here's a picture of this room. Repaint it, add this light and this table and this rug. No, change the color of the rug. There's a kind of class of stuff where it works.

But I mean, a couple of years

ago somebody said AI is good at stuff that computers are bad at and bad at stuff that computers are good

at, and that

I struggle to find many examples of those where I need it.

But then I'm a kind of a unique, weird job.

You know, I sit at my desk all day, you know, trying to synthesize a whole bunch of other stuff into a whole bunch of new ideas. That's not particularly common way for people to spend their time. I struggle to find AI use cases. I am the accountant looking at the spreadsheet and thinking, well, that's very clever and this is clearly going to completely transform everything. But I actually don't make spreadsheets every day.

[Lenny Rachitsky]

I went to a stand up comedy show with Pete Holmes, I don't know if you know him, and he made this joke that we Want AI to do, like, clean the poop off the street and do all these hard things that nobody wants to do, but instead it's like, oh, let me help you write. Let me help you create imagery. It's like this bohemian. It's like, no, I don't want to. I don't want to do all these ugly things. I want to be creative, make art.

[Benedict Evans]

Yeah, well, I mean, there's variations of all of this.

It's like, I don't want the AI to do the stuff I do for fun.

I want to do the stuff, the boring stuff that I don't do for fun. And finding that mesh. I mean, joking apart, this kind of

comes back to kind of of my chatbot point, that the chatbot is a blank screen in a jagged edge. What am I supposed to do and what will work? And that's a big problem.

And the solution to that problem is

to wrap it in use cases.

Part of it is also like, AI just disappears. So most of what I write now I dictate.

I dictate is a voice memo, and that's automatically transcribed. Is that still AI or is that just voice recognition?

Probably an LLM. There's probably an LLM in there.

Okay, so maybe that's AI well, okay, so what? At a certain point, it's just automation.

[Lenny Rachitsky]

What do you use for that? For voice transcription?

[Benedict Evans]

So I actually find Apple Notes. The app, or the one built into the iPhone works fine.

I mean, I'm conscious of people want others, but, like, I mean, I dictate it. There it is.

They work. So I'm happy with that.

[Lenny Rachitsky]

All right, final question before we get to our very exciting Lightning Round. Is there anything else that you wanted to share? Anything else you want to leave listeners with?

[Benedict Evans]

No, I think, you know, I've monologued

plenty and I've gone through a bunch of stuff in the deck.

Go read the deck and sign up to my newsletter.

And then you will get many more mags of brilliant Benedict Evans wisdom, some of which may even be useful.

Someone unsubscribed from my newsletter, and they said, you didn't give me any actionable stock ideas. And I'm like, well, on one level, that's completely true. On the other level, maybe not.

[Lenny Rachitsky]

Well, with that, Benedict, we've reached our very exciting Lightning Round. I've got five questions for you. Are you ready?

[Benedict Evans]

Sure.

[Podcast Host/Announcer]

First question.

[Lenny Rachitsky]

What are two or three books that you find yourself recommending most to other people?

[Benedict Evans]

Tough one for me because I just read an enormous amount of books and

then I Can't remember which ones I've read.

I sometimes often joke that there's a

classic British comedy from the late 19th century called three men in a Boat, which is like my I Ching. Like, we're having trouble hanging a picture. Well, there's a section about that. You know, we're having trouble doing this our world. There's a story about that. All of which are hilarious.

So Three Men in a Boat is my I Ching. There's a book by, I think, William Cronin about the economic history of Chicago,

which is fascinating and actually very relevant to technology because it's talking basically about standardization and packetization and logistics and channel conflict and network dynamics and network neutrality. So, like, when the meat packers of Chicago reach the point that it's cheaper to ship a cow from New York to Chicago, kill it, pack it, and then ship it back to New York than to kill it in New York and the pricing of refrigerator cars, and it's exactly like reading about broadband. It's all the same kind of business issues, which is fascinating. What else have I read? I don't know. Read books. Read different books. Generally read books for grownups.

Please read something other than Lord of

the Rings if you're going to name another company. Like, I saw this and what was the latest? Like, Peter Thiel Company.

I was like, read another book. Everything is named after a character from this one book. There is more than one book in the world. There is more than one book, and

all about science fiction. Read about different things. Read about things you don't know about.

[Lenny Rachitsky]

Kind of along those lines. You have a favorite recent movie or TV show you've really enjoyed.

[Benedict Evans]

I don't know. I've dropped so badly off the current

media treadmill, and I just spend most of my time watching classics, which are like, always the ones that you're supposed to have seen. And that all seem intimidating. And then you watch them and you're like, oh, that was actually really good. I watched the Seventh Seal recently, which is like one of those Jake, Woody Allen, terrifying, boring movies, and it was brilliant. It's really interesting. And it's like, it's only like an hour. So go. Go watch one of those movies that you are supposed to have seen or hadn't seen.

[Lenny Rachitsky]

Favorite recent product you recently discovered that you really love. Could be a gadget, could be an app.

[Benedict Evans]

I was speaking at a partner meeting for a company earlier this. This week. What's today?

Monday?

No, last week. And met the founder of the company who has a very famous network, the CEO of the Company has a very famous name and admired his shoes and didn't say anything, but then went and Googled like half an hour later. Yeah, okay, I'll buy a pair of this.

[Lenny Rachitsky]

Do you want to share the brand or you want to keep it. Keep it secret?

[Benedict Evans]

Okay, we'll keep it secret.

I don't know. I think one comes in waves of new products and you know, you get

into waves of new things and like, like when's the last time there was a cool app, like iPhone apps that was, you know, all that white space went. I mean, it's partly a function of product chips, a platform chips. Like all the white space went for cool new apps.

And now we haven't quite got.

Actually this is a.

To the earlier point, we don't have

breakout a consumer AI app yet because I think because of marginal cost more than anything else, you can't make it free and get 50 million users and then have a revenue model. But we don't have those breakout things yet.

[Lenny Rachitsky]

For consumer.

[Benedict Evans]

Yeah, for consumer.

No, I just.

Weird. I keep getting these ads for voice recorders. Like somebody's selling like a business card size, like hardware voice recorder.

But like, I don't get it.

Like I've got the voice recorder on my phone.

[Lenny Rachitsky]

Yeah, all kinds of cool stuff coming. Okay, two more questions. Do you have a favorite life motto that you find yourself coming back to often in work or in life?

[Benedict Evans]

I suppose I've mentioned earlier, apparently I mostly say it depends on.

[Lenny Rachitsky]

That's going to be the title.

[Benedict Evans]

It'll probably be okay.

[Lenny Rachitsky]

Yeah. Okay. That's the vibe I get. I like that. I like that. It's probably going to be okay. Not for sure. Okay, final question. I saw somewhere that you own a lot of old phones. Is that true?

[Benedict Evans]

It is, yes. As a. I kept, I mean, I

was a telecoms analyst and mobile analyst and I kept all my phones up to a point. Now they're kind of uninteresting.

But as you may remember, before the

iPhone, particularly outside the usa, there was this huge creativity and expansion in what phones looked like because everyone was basically innovating around a little teeny tiny gray square. So everyone was trying to differentiate from everything else before it kind of result. It's kind of like cars, actually.

It's like cars before street, before wind tunnels.

Cars all look different. And everyone's trying to innovate around because you've got the same four wheels and the same engine. Everyone's trying to differentiate based on the shape, shape. And then everything converges on one shape and it's kind of the same with phones. Like everyone, everything converged on one shape. Before that there was all this innovation. So yeah, I have a whole bunch of PDAs and smartphones.

[Lenny Rachitsky]

How many phones are we talking about?

[Benedict Evans]

I don't know, like 20 or 30.

[Lenny Rachitsky]

Okay, okay, okay. It's not so crazy. What's like the oldest one? What's the oldest one you got?

[Benedict Evans]

So I have one of those. I should have.

You told me. I'd have got the box down. I have one of those Ericsson Shark Fin Flip phones from like 98 or something, which is very, again, like hardware design, visual design, trying to differentiate.

I've got an imode phone from 2001 and a J phone phone from 2001

that has a camera. So I came back from Japan in 2001 and I phone had a color screen and a camera.

And like, I just had like endless client meetings and people just wanted to

see the phone with a color screen. Like, it's mind blowing. It didn't work outside Japan.

I plugged it in the other day.

It still charges up. I mean, clearly I can't do anything with it.

And like, I mean, there's a little bit of an analogy in there as well.

And like, we thought there'd be all these different shapes and sizes. And before the iPhone, people kind of imagined like, well, some people will have like a little pocket PC and some

people have a keyboard and you have like folding.

All these different ideas for what it would look like. And we didn't realize it was all going to converge on one device.

[Lenny Rachitsky]

Benedict, this was amazing. I learned a ton. I feel better after this conversation. Two final questions. Where can folks find you online? Where do they find this presentation? And how can listeners be useful to

[Benedict Evans]

you if you can Google me?

As I always say, my parents had good SEO, so Google Benedict Evans. And so there's a website where there's. I publish all the presentations that I've done and sign up for my newsletter, which comes out every week.

Otherwise, how can they be useful to me?

Like, I'm always trying to understand stuff and I'm always trying to ask different questions. The worst thing in tech is to like, carry on talking about the same stuff. Stuff. It's like, you know, the moment you really understand something is the moment you have to push onto something else.

And so I'm always trying to think

like, no, am I just talking about the same thing over and over again? Like last year I just spent probably too much time saying, but these models still hallucinate. Stop telling me. They don't hallucinate.

And they do. They still hallucinate. You push them a little bit further, any question and you'll still get like, nope, that's not true. But that doesn't mean they're not useful.

So you have to kind of keep pushing myself. So that's always the challenge for me is how do I push?

And then yes, if you want me

to come and present to your board in the Caribbean, then let me know.

[Lenny Rachitsky]

And by the way, the domain is ben-evans.com if folks want to check you out and E v a n s.com benedict thank you so much for being here.

[Benedict Evans]

Thanks a lot.

[Lenny Rachitsky]

Bye everyone.

[Podcast Host/Announcer]

Thank you so much for listening. If you found this valuable, you can subscribe to this channel show on Apple Podcasts, Spotify or your favorite podcast app. Also, please consider giving us a rating

[Lenny Rachitsky]

or leaving a review as that really

[Podcast Host/Announcer]

helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.

[Benedict Evans]

Com.

[Podcast Host/Announcer]

See you in the next episode.