The Speed of Now

Speaker 3

Have you ever felt like the world isn't just moving fast, but, I don't know, accelerating at a pace that almost defies logic, especially with AI?

Speaker 2

Oh, absolutely. It's like things are speeding up exponentially.

Speaker 3

Yeah, it feels like entire decades of progress are now getting squeezed into just months. What does that feeling actually mean for how we live and work day to day?

Speaker 2

It's a really profound question. And it's pretty much what we're digging into today, looking through the lens of Keith Tears editorial. He calls it the great convergence.

Speaker 3

The great convergence.

Speaker 2

Yeah. It's basically about this fundamental collision between, you know, the exponential progress of technology, AI in particular, and the much slower, more, let's say, linear pace of human adaptation and our institutions, too.

Speaker 3

Right, our established ways of doing things. Okay, let's unpack this then. Our mission here is to really try and grasp the implications of this convergence. We're going to look at the incredible speed of AI innovation right alongside the very real friction we see in traditional systems. You mentioned institutions like venture capital or even massive global supply chains.

Speaker 2

Exactly. Those things don't change overnight. And it's all about identifying the critical choices this moment is forcing on all of us right now.

Speaker 3

So what have we got to work with?

Speaker 2

Well, we've pulled from a stack of really interesting sources to bring this to life for you. Obviously, Terror's Core Editorial, but also hard data from the cutting edge of AI development and trends from the VC world, plus some really thought-provoking essays that kind of challenge our basic assumptions about how things work.

Speaker 3

Sounds good. So Terror's Core Idea, the anchor for this conversation, is that stark statement, AI is fast, humans are slow.

Speaker 2

That's it. It just perfectly captures the essence of this great convergence, this point where almost unimaginable AI-driven abundance crashes right into that deep-seated institutional inertia.

Speaker 3

It's more than just a catchy phrase, though, isn't it?

Speaker 2

Oh, much more. Tara's editorial really digs into how the numbers coming out of the AI space aren't just big numbers. They represent what he calls the speed of now.

Speaker 3

The speed of now. I like that. It really gets at the dynamic here. It's about exponential growth, essentially.

Speaker 2

Yeah. Totally. We're talking about AI compressing what might have taken, say, a decade of development into just months.

Speaker 3

And our sources have some wild examples, like chat GPT.

Speaker 2

Right. I mean, think about it. It basically compressed what took Google over a decade to achieve in terms of daily searches, user adoption, into just 300 days.

Speaker 3

300 days. That's not just an improvement. It's a total redefinition of how we interact with information.

Speaker 2

Exactly. And it's not like it's just one company. Look at Cursor. Our sources flag it as potentially the fastest growing sauce company ever.

Speaker 3

Okay. What are the numbers?

Speaker 2

They went from $1 million to $100 million in annual recurring revenue ARR in just 12 months.

Speaker 3

$100 million ARR in a year.

Speaker 2

Yep. And here's the kicker. Almost zero marketing spend. They hit 360,000 paying customers, mostly through word of mouth.

Speaker 3

That kind of scale with no marketing. That's astonishing. That must be what Terry means by AI native economics. It just changes the whole game.

Speaker 2

It completely redefines value creation. And you see similar patterns elsewhere. Anthropics Claude, another big AI player, hit $3 billion in annualized revenue back in May. That was 200% growth in just five months.

Speaker 3

Wow.

Speaker 2

And then there's Midjourney, the AI image generator. Wow. $300 million revenue in 2024 with just 131 employees. And again, zero marketing.

Speaker 3

Zero marketing. It feels like the network effects and the scalability of AI are just making traditional sales and marketing models almost irrelevant for some categories.

Speaker 2

It certainly seems that way. And it's forcing established players to adapt fast or risk being left behind. Look at Databricks.

Speaker 3

The data and AI company.

Speaker 2

Yeah. They recently caught Snowflake, the cloud data warehousing giant, hitting $3.7 billion in ARR. That's 50% year-over-year growth, mostly driven by AI adoption on their platform. It's like an acceleration across the entire tech landscape, pulling even the giants into this new speed.

Speaker 3

It's even reshaping really fundamental concepts, isn't it?

Speaker 2

Yeah.

Speaker 3

Like Andrej Karpathy's idea of software 3.0.

Speaker 2

Right. His idea that English is becoming the hottest new programming language.

Speaker 3

Which sounds counterintuitive, but it makes sense when you think about it.

Speaker 2

It does. The core idea is that AI platforms are just going to absorb entire categories of B2B software, change the whole landscape.

Speaker 3

And chat GPT is kind of the prime example here, becoming this ultimate mega app.

Speaker 2

Exactly. It's not just plugging into other software. It's threatening to actually replace traditional B2B tools by becoming the main interface for workflows.

Speaker 3

So you could connect it to your Google Drive, your Dropbox, SharePoint, pull data right through ChatGPT.

Speaker 2

Or even as the sources suggest with HubSpot, it could absorb core CRM functions. The key insight is that AI isn't just another tool. It's like a new operating system.

Speaker 3

Shifting the value away from the software features themselves.

Speaker 2

towards these intelligent, adaptable interfaces that learn from your specific data. It sort of commoditizes the how and elevates the what.

Speaker 3

So instead of logging into HubSpot to find customer insights, you could just ask ChatGPT, show me trends for customers in X sector, and it pulls the data, maybe even suggests marketing ideas.

Speaker 2

Potentially, yeah.

Speaker 3

Which makes you question what you're paying for in that traditional SaaS subscription if ChatGPT can do, say, 80% of the job.

Speaker 2

That's exactly Karpathy's point when he says software 3.0 is eating 1.0, 2.0. It's a huge shift.

Speaker 3

It really is. So are there any areas of traditional software you think are truly safe from this absorption? Or is it all heading towards commoditization?

Speaker 2

Well, that's the billion dollar question, isn't it? You'll probably always need highly specialized niche tools with deep domain expertise. But the general trend, like our sources point towards, is definitely commoditization for general business functions.

Speaker 3

So the resistance isn't really technological.

Speaker 2

Not primarily. It's more about that friction of the old, the human systems, the institutions that just struggle to keep pace.

Speaker 3

And that friction of the old really stands out against AI speed. Venture capital is a great example. Our sources are painting a picture of a pretty serious liquidity crisis there.

Speaker 2

Yeah, it's quite a contrast to the boom years. The numbers are sobering. Only 37 percent of the VC funds started in 2019 have actually returned any capital to their investors, their LPs, after five years.

Speaker 3

Only 37 percent. How does that compare?

Speaker 2

Well, compare that to 81 percent of 2017 funds at the same stage. And the median IRR, the internal rate of return for those 2019 funds is just 5.4 percent.

Speaker 3

Wow. It's low.

Speaker 2

It's dramatically slower returns for the LPs, which puts huge pressure on the whole VC model.

Speaker 3

And then there's this thing people are calling the great unicorn backlog.

Speaker 2

Ah, yes. Over 1,400 unicorns, private companies valued over a billion dollars worth of collective $4.8 trillion.

Speaker 3

And the problem is getting them public or acquired, right? The exits.

Speaker 2

Exactly. At the current rate of exits, it would take something like 49 years to clear that backlog. It essentially locks up a massive amount of capital.

Speaker 3

Which must be forcing VCs to change how they operate.

Speaker 2

Absolutely. Seed investors, the earliest ones, are now looking to sell their winning investments much earlier or trying to find buyers in the secondary market. LPs just want their money back quicker.

Speaker 3

Are there risks with that? Selling early.

Speaker 2

sure and there are risks with the secondary market too like adverse selection you know maybe the deals hitting the secondary market are the ones that couldn't raise

Speaker 3

money elsewhere easily yeah it's tricky and this inertia this friction it's not just in finance we see it in huge physical systems too like global supply chains

Speaker 2

apple in china is the classic example definitely it took them decades to build up that incredibly complex iphone manufacturing system in china But now, because of geopolitics, they're scrambling to diversify, moving a chunk to India by 2026.

Speaker 3

That sounds like a massive, slow and expensive undertaking.

Speaker 2

It is. Apple suppliers have reportedly spent around 16 billion dollars just since 2018 on this shift. Our sources talk about the messy reality that infrastructure and capability don't materialize overnight.

Speaker 3

It's a perfect illustration of how the physical world and huge corporations just operate on a completely different timescale than AI development.

Speaker 2

Totally different pace. And this friction even hits the workforce. Amazon CEO Andy Jassy warned that AI will likely mean fewer corporate type jobs, linking that AI efficiency directly to potential workforce cuts.

Speaker 3

So these examples, struggling VCs, supply chains in upheaval, potential job impacts, they really highlight the pressure on the old ways of doing things. This friction, as Tierra says, forces a choice.

Speaker 2

Exactly right. Tierra's argument boils down to this. Every person, every company, every institution faces a decision. You either embrace the speed of the new or you get stuck managing the decline of the old.

Speaker 3

No middle ground.

Speaker 2

Not really. Not in an exponential world, he argues.

Speaker 3

So what does this mean practically? The companies winning seem to be the ones choosing speed, like that AI law firm example, Crosby.

Speaker 2

Yeah, Crosby is a good one. Completing contract reviews in under an hour, it shows what's possible when you actually build for this new AI native world instead of just tweaking the old model.

Speaker 3

Because Tyra's point is that if you're still optimizing for the old ways,

Speaker 2

you're just going to get left behind. In exponential curve, moving too slowly is actually the biggest risk you can take. It's not just a business issue. It's societal. We all need to shift our thinking.

Speaker 3

But embracing speed doesn't mean just blindly jumping on the bandwagon, right? Kara himself says this isn't naive tech triumphalism.

Speaker 2

No, definitely not. The challenges are real. We talked about the VC liquidity crisis. And there's also the huge concentration of AI power in just a few big platforms. Those are serious issues.

Speaker 3

And tackling those requires clear, critical thinking, which brings us to those essays you mentioned.

Speaker 2

Exactly. This is where someone like Kyle Harrison in his essay on the burden of proof becomes really relevant. He basically warns us, don't just buy into compelling stories, especially around new tech or how resilient old systems are, without demanding real proof.

Speaker 3

Resist the narrative if the evidence isn't there. Avoid that in-group thinking.

Speaker 2

Right. We need to apply the same critical lens to narratives about AI's impact, both positive and negative, as we would to anything else. Seek truth, not just validation.

Speaker 3

That makes sense. And Paul Kedrosky's essay, Who's Weird? Maybe We're Weird, pushes that even further, doesn't it? Questioning our fundamental assumptions.

Speaker 2

Yeah, he challenges us to ask if the things we consider normal or inevitable, like maybe our current free market setup, are actually just historical blips. Are we the weird ones?

Speaker 3

He uses that Douglas Adams analogy, the sentient puddle.

Speaker 2

That's the one. The puddle thinks the hole fits it perfectly, designed just for it right before it evaporates. We risk that kind of blindness if we assume our current systems are the only way things can be, especially when faced with change this fast.

Speaker 3

A powerful metaphor for potential blind spots.

Speaker 2

It is. But Tyr adds a crucial point here. Skepticism is important, but skepticism without action is just, as he puts it, intellectual tourism. Meaning? The AI acceleration, the capital shifts, the supply chain issues, they aren't just happening to us. They're the result of millions of human decisions. And they demand a response, not just observation.

Speaker 3

Okay. So that points towards the path forward. The potential is huge, right? This abundance economy idea.

Speaker 2

The potential is extraordinary. Universal access to intelligence, huge cost reductions, maybe even new ways for people to flourish. But just building the tech isn't enough to get us there. We need more. We need to build better institutions, better policies. We need better ways of thinking about how progress and fairness can actually coexist. Ultimately, it requires us humans to really lean into change and adapt thoughtfully.

Speaker 3

Because the future isn't set in stone.

Speaker 2

Yeah.

Speaker 3

Just because AI can compress decades into months doesn't automatically mean everyone benefits.

Speaker 2

Exactly. And just because capital flows towards these abundance creating technologies doesn't guarantee that abundance gets shared widely.

Speaker 3

It comes down to the choices we make.

Speaker 2

Thier argues it depends entirely on the choices and actions taken by individuals, entrepreneurs, investors, citizens, all of us. The great convergence is happening. The real question is, can we match the speed of the tech with the speed of our own adaptation, our institutional reforms, and maybe most importantly, our collective wisdom?

Speaker 3

So wrapping up our deep dive today, we're in the midst of this great convergence. It's this stark clash between AI's incredible speed and the slower pace of human adaptation and our traditional systems.

Speaker 2

Right. And the key thing for you, the listener, is that this isn't just abstract tech talk. It's fundamentally changing how value gets created and how society needs to evolve. You're not just watching this. You're part of it, influencing it.

Speaker 3

Which leads us to a final thought for you to consider. Given this incredible acceleration and all the friction we discussed, what specific linear system, maybe in your own life, maybe in your work or organization, feels the most out of step with this exponential reality?

Speaker 2

And perhaps more importantly, what's one small step you could realistically take to start aligning yourself or that system just a little bit more with this speed of now?