I view one of the things that went off the rail was when it started to be run by business people as opposed to technical, the bean counters, the finance people. And you know when I became uh CEO uh in 2001 that was the first technical leader in essentially 15 years

Gelsinger identifies the root cause of Intel's decline as a loss of technical leadership — a cautionary tale for any technology company about who should be in the driver's seat.

if you have a business leader who does he promote business leaders and you know right you know so I think one of the fundamental things is and you know as you look at the great technology companies uh today you know they're deeply technical and even if they're not you know Satcha is not a founder no right? You know, Sundar is not a founder as well, but they're deeply technical individuals. And when you're making these hardcore technical, you know, decisions that affect billions of dollars, you don't do that through a spreadsheet.

Gelsinger articulates why technical depth in leadership is non-negotiable for major technology investment decisions — spreadsheets can't capture technology trajectories.

Intel gave $100 billion to shareholders. and stock buybacks. it hadn't built a new factory in a decade when I got there. It's like, you know, how can you not be building? How could you not buy EUV machines? You know, there's just all of these things, you know, that you would only do as a technologist because the economics behind them by themselves were not good.

This reveals how financial engineering can hollow out a technology company — investments in frontier equipment like EUV only make sense with a long-term technical vision, not a quarterly earnings lens.

they were putting extraordinary demands on Intel. You know, make the chip smaller, drive lower power. They're demanding uh customer. And when he was no longer convinced that we could continue to do that, you know, he started the project. And if you remember what was it, you know, you know, uh, P semi, you know, they acquired some small companies, started to build some competency, but, you know, they did a few little chips internally. It wasn't a big deal and then the little chips got a little bit bigger, you know, and Steve was a master of this, you know, just starting, you know, these small efforts to build core competence inside the company.

Jobs' quiet, incremental buildup of silicon expertise inside Apple is a masterclass in strategic optionality — building capability before you need it.

Steve said, I've been working on that for the last four releases. He had been preparing the core technologies inside of Apple for something that might happen uh in the future, you know, and he was already, you know, to me, I just remember I was just shocked. I've ported the last four releases to the x86. I think we got this.

Jobs secretly prepared Apple's OS for a processor transition years before it was announced — a stunning example of strategic patience and long-horizon planning.

Jensen, he was just building high performance computers, you know, throughput machines. You know, when we were at the height of our strength on CPUs, uh, at Intel, we sort of scoffed at his machines. Yeah. Right. You like, oh, that's a graphic machine. there's some gamers who want to use that kind of stuff. But when they started to build a real software stack with it, right? You know, sort of, okay, this CUDA thing and SIMT as a technology, you know, uh, you know, multi-threading and so on. And it just sort of kept getting a little bit better and a little bit better.

Intel's dismissal of Nvidia's GPU as a 'gamer toy' is a textbook innovator's dilemma moment — the disruptive technology always looks trivial from the incumbent's vantage point.

it wasn't just about doing graphics anymore. This was a more computationally dense platform to start attacking some of the world's most interesting workloads. And I think Jensen would agree that was a defining moment and them sort of saying, Oh, these aren't just graphics cards anymore. You know, these are generalpurpose computing devices that can start applying to these other uh workloads. And you know AI was you know had gone through what its fifth nuclear winter by that point.

The pivot from graphics to general-purpose computing — enabled by persistent software investment — is what made Nvidia the most valuable company in the world, not luck alone.

the thing with TSMC was they started with a vision of foundry right you know they were going to become the factory for the industry and again these factories are so expensive 20 billion 30 billion and uh the engineering and the continuous investment required to do it and you know it was a stunning you know vision uh at that point in time Intel was IDM as we called it the integrated design and manufacturing we Never worked to make our process and our factories available for third parties

TSMC's foundry model — dismissed by Intel as a trivial niche — became the dominant architecture of the entire semiconductor industry, illustrating how business model innovation can be as powerful as technical innovation.

the island of Taiwan has less than 3 weeks, a big article in the Wall Street Journal two weeks ago on this, less than 3 weeks of energy reserves. Okay, that should just put a chill in everybody's spine, right? Because the blockade after 3 weeks, the island browns out. When you turn off a fab, it doesn't come back on for 90 days, right? The economic impact of a brown out of Taiwan is greater than the Great Depression, right?

The Taiwan semiconductor risk is not hypothetical — a mere blockade without a single shot fired could trigger the worst global economic catastrophe in modern history within weeks.

there is a silver lining here that guarantees we don't get too far ahead of oursel in terms of bubble, you know, and that is energy capacity. Right. You know, energy capacity in the world is expanding four 5%. nobody's going to build and buy GPUs and build data centers if they don't have energy. So essentially, you have an upper bound on how aggressive and how hyped and bubbled that we get.

Energy infrastructure is the invisible ceiling on the AI buildout — a physical constraint that acts as a natural bubble limiter in a way that purely financial assets don't have.

I have to make AI 10,000x better, right? You know, it's way too expensive today. you know, we want to drop, you know, by five orders of magnitude the cost per token, you know, the energy, you know, per token so that we really do have Jevans law that we just explode the access to AI. There has not been a time in human history where it's been better to be a technologist than the one we're in right now. We will solve chemistry. We will solve language. We will, you know, invent new materials, re, you know, new forms of, you know, uh, interaction, you know, uh, killing cancer, right? Lifting people out of poverty.

Gelsinger's vision of 10,000x cost reduction in AI compute and a multi-decade buildout reframes current AI investments as early infrastructure, not a speculative bubble.

quantum's been about 5 years away for 25 years um when is it actually going to do anything meaningful. this decade this decade. So by 2030, it'll be meaningful. you're going to be able to start doing things that cannot be computed today. You know, chemistry, you know, biology, there will be things that can't be computed today.

Gelsinger makes a firm, falsifiable prediction that quantum computing will achieve meaningful real-world results by 2030, staking his credibility on a specific timeline.

this decade we will see quantum supremacy uh results across multiple industries you know we know how to build cubits we know how to error correct cubits we now have algorithmics right against uh quantum and you know now it's just about engineering scale. The thing that you're seeing is that you now have like four, five, six modalities of quantum that are demonstrating pretty good results, right? You know, across trapped ions, across, you know, photonic uh approaches, spin uh approaches. So, you now say modality is not an issue.

The convergence of multiple viable quantum hardware approaches signals that the field has matured past theoretical debate into an engineering execution race.

we're seeing people who are first-time founders. We're seeing enterprise leaders move much faster together with their teams on this platform that has a lot of opinionated pieces in how you should uh create software and how to operate that software and how the different applications in your company connect to each other over time. So that's what why we're seeing so much growth also on the enterprise side which where where we actually growing fastest right now.

Lovable's fastest growth segment is enterprise — suggesting that AI-assisted software building is not just a hobbyist tool but is reshaping how large organizations build and operate software.

If we were sitting here last year, people would look at it and say it's a great way to make a mockup. like you said, a great way to think about product and maybe create wireframes or a workable prototype. All of that's out the window now. The whole concept of building wireframes and building a mockup, well, you can just go right to building the product in a day or two days.

The wireframe and prototype phase of product development is being collapsed into the same act as building the actual product — a fundamental shift in how software gets created.

many engineers they don't look at the code they don't write code anymore and that means that you don't need to be an engineer to create software right um but the the thing that lovable does for any anyone also the non-technical people is that it it um takes uh creates a structure for the architecture of the software that you build and it makes sure that you don't go off a cliff

The shift where even engineers stop reading their own code represents a profound change in what software development actually means — and raises serious questions about accountability and understanding.

we're working with some of our customers in pre-release to give them access to a co-founder that works for you even when you're sleeping and comes back to you in the morning and says like, Here are some strategic directions you could go. here's some optimizations you can do go in terms of growing your business faster serving your customers better uh faster uh and and and that's um that evolution towards operation and intelligence for towards driving towards outcome for your business

The vision of an AI co-founder that proactively generates strategic recommendations overnight represents a step-change from AI as a tool to AI as an autonomous business partner.

Is software going to become 100% bespoke even like the internal tools. I was looking at Slack and our bill for Slack even on the highest version is maybe $10,000 a year. It's well worth it. But I was starting to think, well, maybe I should vibe code my own Slack so it's integrated into everything we do at a deeper level.

The question of whether mass-market SaaS tools will be replaced by custom-built bespoke software is one of the most consequential business model questions of the next decade.

they've now replaced more than 10 tools that they had bespoke applications and um I think in terms of your question you can do that for multiple reasons. In their case they're saving more than a million dollars per year, right? So that's that's huge, right? But it's also the case that in some cases you have specific requirements where the tools that you've been using to date they aren't suited for those requirements exactly

A real enterprise replacing 10 software tools and saving $1M/year with AI-built custom software is a concrete data point that SaaS disruption is already happening, not just theoretical.

I'm actually a huge fan of very rapid experimentation and I I have a story where for a while I worked at a a place called CERN where they do particle physics. and that's where I was introduced to this concept of co-opetition where they have two actually quite isolated teams working on the same um particle accelerator but different places on it and then they don't share the results until they publish and that way they uh they can kind of over time learn what's working best in the different organizations but you don't get stuck in a local minimum

Osika draws a brilliant analogy between CERN's parallel team structure and vibe-coding multiple competing solutions — when building is cheap, parallel experimentation beats serial optimization.