Still Human after All These Years

From Caring Deeply to Not Knowing How to Care

Lisa Broderick — Sun, 05 Jul 2026 14:53:09 GMT

There was a time, early in Silicon Valley, when the goal on everyone’s mind wasn’t building the most powerful anything. It was to make computers accessible, even fun, for people who used them. The tech was primitive by today’s standards, but the experience we wanted for users with those unbelievably early computers was anything but random. It was well considered, painstakingly crafted, by developers who believed that the interface between people and computers mattered as much or more than the capability. You see it in the OG’s that defined that era, with the Lisa and the Mac, where the graphical interface wasn’t just a feature, it was the point. As a kid just out of college, my first company developed the graphics software that was shipped with the original Microsoft Mouse, and we endlessly tinkered with the interface, caring maybe too much about possible friction between a user and the software. We were driven to make the interaction as second-nature-natural as possible. Peripherals like the KoalaPad touchpad and Ray Kurzweil’s Reading Machine for speech were crafted with the same earnest intention: that the human side of the interaction deserved as much thoughtfulness as the device itself.

The original Macintosh team. They weren't just building software. They were painstakingly crafting the relationship between human beings and computers, believing that the experience was every bit as important as the technology itself.

Over time, that commitment faded like a distant memory. As the tech industry grew, the center of gravity moved away from the interface and aimed itself squarely at the next big breakthrough. More processing power, more features, more capability. Innovation was defined less by how well something worked for a human and more by what it could do in absolute terms. The assumption, usually unspoken, was that using the thing would be obvious to any remotely intelligent human. That if the underlying tech was advanced enough, knowing how to use it would somehow take care of itself. In this new world, the interface is something to be tolerated instead of something to master. (Note that the iPhone seems to be the perennial exception.)

You see the impact of that in the gradual move toward greater complexity. Systems became more powerful, and also more complicated. Consistency was thrown out in favor of features that didn’t always matter or even make sense to anyone but the developers. The experience of using tech now requires more effort, not less. What once felt like a careful, thoughtful marrying of person and tech has begun to feel like a series of feature-driven compromises. Even platforms that originally set the standard for great design, like the Apple Mac, now show signs of dullness, inexplicably moving in the direction of the same kind of complexity that early software was so criticized for, including Microsoft Windows. The difference wasn’t just how these systems looked, but in how they felt. The sense of familiarity and ease that once endeared users has become almost impossible to find.

At first, it was easy to explain this as just the market exerting itself. Companies stopped caring about the interface because they didn’t have to. Market dominance, switching costs, and the overpowering ubiquitousness of the industry made it possible to deprioritize the user without affecting the bottom line. That still holds true to some degree, but it’s not the whole reason. What’s obvious now is a lot more disturbing. It’s not just that the tech industry chose not to care. It’s that it doesn’t even know how.

That caring about the quality of human interaction with tech shows up in small ways. Design decisions that used to be seen through a rational set of rules now feel random. Interfaces reflect internal constraints instead of external intuitiveness. The developers building these systems aren’t less talented. They’re working in companies with incentives that reward speed, scale, and novelty over elegance in human interaction. The art of creating a fun human experience has been trampled by a relentless need to ship, iterate, and publish the next-greatest thing. The once-lofty idea of the human touch is a nuisance, a secondary concern, and over time, the reason for doing it all goes bye-bye.

This is where the conversation becomes less about nostalgia and more about capability. If an industry moves far enough away from a practice, it doesn’t just stop valuing it. It risks losing the ability to even consider it. The idea of thoughtfully considering an interface, of holding a system together with an intuitive human-based reasonableness, begins to feel foreign. Not because it’s impossible, but because it’s no longer part of the flow of how work gets done. The shift from caring to not caring was just the halfway point. The more profound shift is from not caring to not even knowing how to care.

That matters now much more than it did before, because the systems we’re building are indisputably more complex, not less. As AI and other advanced tech gets more embedded into daily life, the quality of the interplay between human and machine is even more crucial. It’s the layer where adoption is either created or lost. If that layer is forgotten, then the chasm between what tech can do and what people can actually use tech for grows ever wider.

There’s still an opportunity to get back what was once routinely part of the workflow of tech development. Not by turning back the clock, but by appreciating why it was valuable in the first place. The early tech artists in the Valley weren’t working with better tools. Their tools might as well have been sticks and fire compared to today. But they were working with a clearer vision for the future. What drove them was the belief that the experience of the user wasn’t an afterthought; it was the point. Returning to that may be as impossible as going back in time, not just for the industry, but for anyone trying to build tech that seeks to be, in any meaningful sense, still human.

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That AI Is Too Good to Check

Lisa Broderick — Sun, 28 Jun 2026 14:30:38 GMT

Here’s something I heard while living in New York that sums up the whole problem with tabloid journalism: “That story’s too good to check.” It’s a scathing indictment of the moment when editors realize that something they’ve stumbled upon and can print is so compelling, so juicy, so irresistible that the responsible step of actually verifying it goes right out the window. It’s such an obviously dangerous instinct that common sense would have normal people avoid it. Still, that instinct to “just run with it” has gotten a lot of people fired, or worse. And now, in a very real way, we’re facing that same atomic-era choice, not about a headline, but about intelligence itself.

Anthropic’s decision to initially pull back the full release of its new Mythos Preview AI model, and then go ahead and re-release it with caveats and restrictions, is one of those moments. By its own account, this system is capable of identifying vulnerabilities in software that survived decades of human scrutiny and millions of automated tests. That’s not just an incremental improvement. That’s a quantum slipstream-quality leap. And like many such leaps in technology, it carries enormous promise and cataclysmic risk at the same time.

Like this risk. Imagine a world where hugely consequential systems, from financial infrastructure to energy grids to transportation networks, are finally hardened against threats at a level we know are coming but still are unable to do anything about. Mythos can deliver that kind of future, a positive one where we’re kept safe and powerful technology is used for the greater good. Now imagine the exact same capability used in reverse, for nakedly power-hungry, malicious ends. The once-in-a-generation AI tool that can find the needle-in-the-haystack flaw can also exploit it. And when that tool is everywhere for anyone to use, unimaginably destructive cyber attacks await an unprepared world.

And that’s why the uproar over Mythos has been so loud.

As writer Tom Friedman recently pointed out, this isn’t just about a fancy new piece of software. It’s a huge red flag. A stark warning that we are entering a phase where the capabilities of AI are being rolled out faster than our systems, institutions, and norms can possibly keep up. We’re not just building tools anymore. We’re creating a new form of intelligence that we’ll have to learn to live with, guide, and constrain. Where that AI is too good to check.

The temptation isn’t just to race to market to get a competitive edge. It’s the siren song of capability itself. If something can do what Mythos can do, the competitive pressure to deploy it for profit will be unbearable. Companies want it. Governments want it. Competitors assume others are already using it. And that’s the race to the bottom where restraint looks like risk.

But restraint may be the only thing that keeps our world intact.

What makes this moment different from earlier tech revolutions is the level of interdependence. Our infrastructure, financial systems, and communication networks are no longer isolated. They’re all inextricably interconnected across companies and among countries. A vulnerability in one place sends out shockwaves almost instantly. A tool that lowers the cost of finding and exploiting those vulnerabilities doesn’t just change one system. It makes it necessary to change to all of them.

And that means no single company can do this alone. No single country can either.

We’re in a position where cooperation is no longer optional. It’s structural. The same way climate change forced a global conversation, AI is doing the same for our world. The difference is the speed. Climate change happens over decades. AI is metastasizing in real time.

That’s why sensible regulation isn’t about slowing innovation. It’s about making sure innovation doesn’t outpace our ability to deal with its consequences. Regulation in this case would be less about restriction and more about alignment. It would be about setting boundaries that allow both humans and AI to integrate into one another without devouring one another.

Anthropic’s decisions around Mythos are early examples of what possible alignment might look like. It’s not great. It’s not comprehensive. It’s weak and partial. But it at least acknowledges something we need to face. Just because we can do something doesn’t mean we should.

The real question is whether that kind of restraint can withstand the mounting pressure.

Because the next model will likely be even more powerful. And the one after, more powerful still. And governments will want it, and bad guys will too.

At some point, every organization in this space will face the same tabloid-journalism decision: whether to move forward because they can or hold back because they should.

The difference is this time, “that AI is too good to check” isn’t just a sarcastic line. Keeping AI in check will be the only thing standing between progress and whatever happens when bad guys start doing their worst.

I Said "Please" to ChatGPT

Lisa Broderick — Sun, 21 Jun 2026 14:01:33 GMT

There’s a way of talking about AI these days that keeps it safely contained. We call it a tool, a system, a model, a thing we built, and so something we control. Thinking about it that way has been useful, and in a lot of ways it’s accurate. But it’s starting to feel, well, superficial. Not because AI has suddenly become truly intelligent in any real sense, but because the role it’s starting to play is no longer well described by the language we use. For the first time yesterday, I did a query using ChatGPT and asked it to please do something, and then thanked it, whatever it is.

That felt polite in the moment, and then it struck me. What’s emerging with AI looks less like a tool we use and more like a new kind of participant in society. Not human, not biological, but more and more capable of generating what looks to us to be insight. It’s shaping our decisions. It’s influencing our outcomes at a speed and scale that looks scarily like a human counterpart, and I’m not sure how I feel about that.

So I went in search of a different word, and what I discovered was one that would’ve seemed hair-on-fire hyperbolic not long ago. Author Tom Friedman recently wrote about the idea of AI as a new species, or at least an early relative of one. He came to this conclusion not because of biology, obviously. He and others are looking at AI’s function and presence, and what they’re seeing looks like a person. In essence, we’re creating systems that can learn, adapt, reason across specialties, and interact with us in super natural ways. And as AI becomes more capable of doing that, it’s not just completing rote tasks. It’s participating in society in ways that used to be solely the domain of humans. It can write, diagnose, recommend, predict, and decide, blurring the boundary between us and it—not because it has intentions of its own, but because what it spits out when asked is shaping human thought in a continuous improvement loop that’s pretty damn good.

What comes next isn’t just a technical challenge. It’s a social and philosophical one. If we are, in effect, giving birth to a form of intelligence that lives right alongside us and increasingly with us in every conceivable way, then we’re also going to need to do some serious deliberation about how that relationship is structured. Not in the abstract as we have are now. But in in practical, grounded terms. We’re going to need to sit down together, as nations, societies, institutions, and communities, and work out the morals and ethics that govern this new kind of being and how we interact with it. That may seem premature. It’s not. It’s now. History suggests that waiting until new technologies as powerful as AI are fully mature before establishing norms is not a winning strategy. Think nuclear weapons.

I think the hesitation to do this comes from how we often use the words morality and ethics, like they’re interchangeable, even though what they mean are two entirely different things. Morality is about people. It’s our sense of right and wrong as individuals. It’s shaped in individual people by culture, experience, and belief. It’s internal, personal, and deeply felt. Ethics is different. It’s about how groups and systems operate. It’s how societies, professions, and institutions translate moral intuitions into shared standards, rules, and practices that govern behavior at scale. Ethics is what allows a group of people, who may not agree on everything, to get along with some degree of understanding and trust.

That matters for how we think about AI, its place alongside humans, and how it should operate with and for human systems. It’s not just a matter of individual moral views. It’s an ethics question. It demands a collective agreement on boundaries, responsibilities, and acceptable uses. It asks us to clearly spell out what kinds of roles AI should play in decision-making, how its outputs should be used, and where human oversight must be non-negotiable. It also brings up questions around accountability. If AI contributes to an outcome, who is responsible for that outcome, and under what conditions are they accountable for them? Nobody knows.

At the same time, there’s a morality to all of this. As AI becomes has become a part of daily life, people are forming relationships with what they create from it, rely on these personifications, and even give them personal deference. Recall that I just said “please” and “thank you”. That doesn’t mean what we create has consciousness or intent. But it does mean that human responses to AI personas will be shaped by the same moral instincts we apply to interactions with other people. Questions about trust, fairness, harm, and care won’t stay neatly tucket away inside technical or regulatory containers. They’ll show up in how we live, in how we feel about the role these personas play in our lives.

What makes this a watershed isn’t just that we’re discovering something new. It’s that we’re actively shaping it. The ways we think about AI now, both moral and ethical, will influence how it evolves and how it’s integrated into society. And that’s a level of responsibility that’s easy to overlook. It’s not just about preventing harm. It’s about defining our coexistence with something that’s neither fully separate from us nor fully within our control.

The good news is, we’re still super early in the process. Some will overstate while others understate what’s happening. But in between is a more sober realization. We’re building systems that are already functioning as participants in our shared world. Since that’s the case, the work ahead isn’t just technical. It’s foundational for the careful construction of our coexistence with AI, which will shape how we live and work alongside our brand new member of society.

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The Eyes Have It

Lisa Broderick — Sun, 14 Jun 2026 14:01:21 GMT

Before Silicon Valley became the modern, scalable, venture-backed tech ecosystem we think of today, there was a sense that technology was a force for good and progress. Back then, the ridiculous hours we spent creating software and peripherals were because we believed that better tools could spectacularly improve how people live and work. And that felt important and real.

Since then, decades later, the Valley, once driven by childlike enthusiasm and unbounded ideas, has somehow found itself driven by boring optimization, the dull quest for engagement, and the relentless need to scale. We’d become shockingly good at building systems that perform. Now we’re building something even more shocking. We’re building intelligence that generates realistic facsimiles of ourselves.

AI-generated video is moving at a frightening pace in its ability to replicate the human form. Faces look real. Voices sound believable. Movements feel lifelike. And at the same time, there’s something unmistakably weird about it. Most people who see AI-generated videos recognize it almost immediately, even if they can’t quite put their finger on it. And I think it’s all about the eyes. The eyes look weird.

Going way back millennia, cultures have revered human eyes as the “windows to the soul.” Not because we have any concrete idea what a “soul” is, but because there’s something about human eyes that goes way beyond even the most realistic image AI can generate. Looking into someone else’s eyes, we’re not just seeing the eyes; we’re seeing them. We sense presence, awareness, life. And when it’s not there, we definitely notice. There’s something missing we’re wired as humans to detect

For its part, AI can now generate convincing videos of people doing pretty much everything and render eyes that seem to match the humans looking out of them. But the eyes never look alive. It’s like when you’re reading a post online that’s so highly structured that it’s forced into an endless array of one-liner paragraphs. It reads like AI. What these two examples have in common is that, as humans, we’re remarkably attuned to detecting tiny tells that reveal underlying inauthenticity.

This isn’t just a failing of AI (although AI might eventually figure this one out, also, God help us). It’s a signpost for where we are in the evolution of tech. For decades, the tech industry has been incredibly successful at replicating things we can measure in terms of speed, scale, precision, and output. But we’re now up against something different. What it means to be human can’t be reduced to ones and zeroes. Presence. Intention. Consciousness. Whatever it is we sense when we say someone’s alive. When we see someone’s eyes that are alive.

Thankfully, this is where we can find a glimmer of hope. The future of AI won’t just be defined by how real AI-generated human images can eventually look. It’ll be defined by how deeply AI understands what it means to be alive. Not just mimicking a human face with all of its expressions, but able to truly generate whatever underlying patterns of attention, awareness, and connection we sense when someone is actually alive.

If the past forty years have shown us anything, it’s that we can build the most powerful systems the world has ever known without having any idea of the second-order effects. We did it with cell phones, apps, social media, etc. But with AI, we’re facing a completely different and existential problem. We can continue to optimize for realism, or we can ask the deeper question about what it is we’re trying to recreate.

If the eyes are where the illusion of life can either be sensed by those who see them as real or a sham, they may also be where a huge breakthrough can occur. Not because we’ve perfected how eyes look, but because we’ve somehow come closer to understanding what we’re looking at in the first place. Maybe that’s the point. The eyes don’t just help us see. They show us what it means to be human.

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Permanence: The Book

Where Have All the Heroes Gone

Lisa Broderick — Sun, 07 Jun 2026 13:58:30 GMT

Long ago in a galaxy far, far away, Silicon Valley’s early architects were heroes. They weren’t just building machines and peripherals and software. They were handcrafting a future they believed in and wanted to birth for the world. First names like Steve and Woz were synonymous with computing that could be personal, intuitive, and, oh yes, fun. About the same time, Bill Gates and Paul Allen were busily laying the foundation for their software empire that, even then, they sensed would one day power a new economic order. These pioneers weren’t particularly hard to find. They hung out at the same places we all did, standing in line for dinner and a movie on University Ave. Everyone could see what they were doing, argue about it, press all the buttons until we broke it, and know that we were all together in whatever was being birthed.

What made that generation so remarkable wasn’t just raw talent. It was the passion and ethos that drove it. Around the same time, other pioneers like Ed Roberts, with his Altair 8800, swept the cover of Popular Mechanics readers (it was a kit computer that we all had to put together ourselves, named after a planet in Star Trek), and groups like the Homebrew Computer Club. Products were invented and ideas shared as a collective effort. Before them, in the 1960s through the 1970s, true visionaries like Doug Engelbart and Alan Kay believed—and made good on those beliefs—that computers could extend human capability, and not be replaced by it. It was a time when tech and human progress were one and the same, and those forging it were worthy of admiration.

In stark contrast, we have today, which is hard to ignore. It’s not that innovation has gone away, quite the opposite. Machines and software are more amazing than ever. But the relationship between creators, what they create, and all of us who consume what they create has changed in fundamental, not-so-great ways. I’m being polite because it’s Substack. AI isn’t just another tool. It’s closer to an early operating system, like machine language and CP/M. But in the case of AI, whatever it is, it can generate almost anything, decide, and act on its own in ways that feel a lot like agency. This startling truth introduces opacity and complexity into our tech and overall world order that even super-sophisticated users may not fully appreciate. And it’s created a real sense of foreboding among the same people who, way back when, built the early tools, and whose families, along with all the rest of us, will have to live and work with these systems going forward.

This growing pit in the stomachs of so many is now showing up in the data. Recent surveys show that roughly half or more of younger people don’t fully trust artificial intelligence.[1] That’s not a passing statistic. That’s a huge, glaring red flag. It shows a much deeper suspicion about where this is all going and whether the tech giants building it are thinking about the unintended, possible harmful consequences of what they’re building in the same way we are.

And all this matters a lot, because trust is what makes builders heroes.

The early developers earned our trust because what they were doing was out in the open, for all of us to see. PCs were something you could hold, play with, understand, program, and control. It gave us agency. It didn’t take it away. Today’s AI companies are increasingly putting out software that seems to be hell-bent on the opposite direction. They’re black boxes, trained on unimaginably vast amounts of public data that spark all kinds of reasonable questions about who owns the data, who consented that it be used, and who decided to deploy it in ways that influence outcomes with virtually no accountability.

So when young people look at today’s tech leaders, they’re not seeing innovators or someone to look up to. They’re seeing unmitigated greed and risk. They’re innately sensing ethical issues around privacy, bias, labor disruption, and the concentration of power. And instead of being inspired, they’re trying to figure out if this is something to trust or something to ignore.

These suspicions fundamentally change the balance of power. If the current and future generations don’t trust technology, it’s hard to imagine they’ll trust the people building it. Or the institutions which fund it and allow it to flourish.

This isn’t about nostalgia. It’s about where we’re headed. The early pioneers were iconic because what they were building felt right to the people who were building it with them. Progress was out in the open, chronicled, and human. Today, the question isn’t just what technology can do, it’s whether it should do it, and who gets to decide.

That’s where the heroes used to stand. And it raises a harder question than it might seem at first. We haven’t run out of people capable of building the future. We’re drifting away from building it in a way people can believe in.

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New book: Permanence

[1] https://www.washingtontimes.com/news/2026/apr/10/gen-z-hesitant-fully-endorse-ai-study-finds/

Interdependence Is the New Dependence. And It’s a Big Problem for Tech.

Lisa Broderick — Sun, 31 May 2026 14:01:01 GMT

For a long time, we’ve thought about how the world works through the lens of dependence on one another. You depend on people you work with, a company depends on its suppliers, a city depends on its infrastructure. It wasn’t a perfect system, but it was simple. And if there was a problem, if you followed the chain of dependence back to its source, you could usually pick out the cause of the problem. Back when we were doing homework at Stanford in C and Pascal, finding bugs was fun. When the program broke, you knew the answer was in there somewhere. It might take hours and an ungodly amount of pizza, but eventually you could trace the problem back to the exact line where it all started. Software was understandable. Every effect had a cause, and with enough time, you could always find it.

This cause-and-effect thinking has also paved the way for the growth of hugely successful frameworks, like societies and industries, over long spans of time. It’s shaped how tech scaled, how we built big organizations, how we ensured accountability when needed, and how we got our world under control—until now.

So what’s changed? In short, we’ve become more connected. In fact, we’re now so connected that, as author Tom Friedman points out, we’ve crossed the Rubicon into a whole new and different world: a world of complete and total interdependence. And that’s the source of a whole new set of problems for all of us.

In these new interdependent systems, problems are no longer traceable back to the source. Instead, everything is networked, mutual, and constantly changing. A depends on B, but B depends on C, and C loops back to A in ways that work great until, for some reason, they don’t. It’s only at that moment we notice that terrible outcomes aren’t the result of an easily identifiable breakdown, or even a clearly defined chain of breakdowns. They’re co-created in a system where things are so distributed and continuously evolving that we can’t even identify the source of the problem. Yikes.

And at no time has this problem been more visible and obvious than now, where systems are not only interconnected but also constantly adapting. In AI, outputs instantly loop around again to become inputs, triggering the next set of AI-generated decisions in a never-ending cycle of human and machine. No single factor entirely determines any outcome, while at the same time every factor impacts the outcome.

Unfortunately, this disaster isn’t limited to the world of tech: global markets, social systems, cultures, and organizations, where interactions triggering unexpected results are everywhere. This doesn’t mean that control over systems is impossible. But it does say that control is no longer what we thought it was. And that’s because our world is increasingly distributed, partial, and indirect.

What all this means is we need to rethink our most basic assumptions about how the world works. In a dependent world, the key question used to be, What do I rely on to get the outcome I want? In our new, interdependent world, the question becomes: what am I part of setting in motion, and then hoping what I want eventually happens? This has profound implications for leadership, for systems design, and for how we understand our world. It begs us to pay attention not just to inputs and outputs, but to relationships, feedback loops, and all the conditions that now factor into how outcomes happen over time.

And worse, we didn’t intentionally adopt this new way of thinking—in fact, mostly we haven’t even realized it was happening. Until we get our arms around the fact that interdependence is the new dependence, we’ll stumble along with linear ideas about completely nonlinear problems. My guess is that’s why the world around us feels out of control, why things feel more fragile than ever before. It’s not just that the world is more complicated. It’s that the way the world works has fundamentally been transformed into something ridiculously complex while we’re still eating pizza and looking for bugs in C.

The Most Important Tech Company Ever Probably Doesn't Exist Yet

Lisa Broderick — Mon, 25 May 2026 14:03:22 GMT

A while ago, I sat down with a youngster (yup, I’m old enough to say that) who felt like they were already behind. They thought they hadn’t accomplished enough yet, and worse, they believed all the earthshattering breakthroughs had already happened. It’s something I’ve run into a lot, especially these days, with headlines about AI and imminent space travel make it seem like the future has already happened.

So, I told them a story. Back in 1982, I was working at Apple Computer as a marketing assistant. Back then, you had to assemble your own computer which was left in your cubicle, otherwise you couldn’t work there. Steve walked in and asked me who I was. I said, “Lisa, who are you? At the time, the PC was itself the breakthrough. It was hard to imagine what could ever come next. The idea that we’d one day have more power in pockets than was being sold for millions of dollars in the form of supercomputers, or that the internet would be in every facet of daily life, wasn’t just unknown, it was unimaginable.

But it was imagined, and then it was known.

Within a few years, Microsoft shipped its first version of Windows, changing how people interfaced with “PCs”. The “World Wide Web” became a thing that completely changed communication, commerce, and access to information. Wi-Fi was everywhere, and smartphones transformed daily life. Bitcoin blew up how we thought money worked. And now AI is detonating what we think intelligence even is.

But here’s what I told them to keep in mind. None of those were obvious at the time. They weren’t inevitable successes. Whether or not they would leave a lasting mark, or even be remembered, was uncertain and incredibly messy.

There were also the even earlier tech battles. The operating system wars, where Gary Kildall and his CP/M had the early lead until a missed meeting with IBM allowed for Bill Gates and his MS-DOS. That chance event didn’t just seal the fate of a single product. It shaped the future of what would become the entire personal computing industry.

Then there were the hardware and interface wars. In the early 1980s, it wasn’t at all obvious how humans would interface with computers. Maybe through speech, like Ray Kurzweil wanted. Maybe using pads like the early tablets developed by companies like Koala Technologies. Or maybe using a pointing device, like the mouse. At the time, each of these had highly credible claims that they would be the winner, like they would define the future.

The mouse obviously won. But not because it was in any way inevitable. But due to a series of twists of fate, and precipitous decisions, that only look obvious in hindsight.

That’s the part people miss. When you’re living these moments, they’re not yet history. They’re life.

Fast forward to today. The shocking announcements about AI, like Mythos Preview make it seem like there’s no room for anyone else, like no as-yet-unheard-of company could break out to displace a market leader. Their ubiquitousness is seen as a fait accompli, like the game’s already over.

But when you’ve lived through even one of these tech revolutions, you know different. These aren’t discreet moments. They are just a remnant of the very, very long arc of progress.

What looks like a done deal today is often just another shift in a much longer game. The outcomes aren’t clear or apparent for years or even decades. And even then, they’re rarely the result of a single breakthrough. They’re the sum total of many.

I also said something else to them. Around Stanford in the early 1980s, and in and around the Valley, those of us dreaming about new tech weren’t thinking about “winning” the future. We were laser-focused on the immediate problems we faced, experimenting, regenerating, and compiling software and hardware that worked just a little better than before. We didn’t know we were defining anything. And the same is true today.

So, if you’re looking at the current tech landscape and maybe thinking everything important has already been done, I’d say you’re looking at it the wrong way. Every generation feels pressure. Every generation is anxious about what they’ll accomplish to leave their mark on history.

The future doesn’t arrive fully formed. It unfolds as a result of the ideas and efforts of so many who are willing to jump on board with what’s right in front of them. Forty years ago, we couldn’t imagine the world as it is today. And forty years from now, we’ll look back at life today and say the same thing. So no, the most important companies don’t already dominate the world. In fact, they probably don’t even exist yet. And for anyone wondering if there’s still room to matter, there always is.

Lisa is a human systems thinker, technology veteran, and behavior change author who writes about the intersection of human truth and complex systems. She consults with CEOs, boards, and senior executives leading consequential change.

New Book: Permanence

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The Generation That Built Our Digital World

Lisa Broderick — Sun, 17 May 2026 22:06:24 GMT

One of the only remaining photos I have of my father, taken in the 1960s in Phoenix, was of him sitting in the center of his start-up team, all working at his company, Western Data Sciences. He was one of the original computer entrepreneurs, an innovator from New York who saw the future coming before most people even understood what a computer was, and moved to Phoenix. During that period, Phoenix became part of a broader growth trend. It was emerging as a major center for electronics and aerospace, attracting companies like Motorola and General Electric alongside Honeywell.

Back then, there were no formal academic programs in computer science or computer engineering. The people attracted to these companies who wanted to work in these fields were electrical engineers, physicists, and mathematicians who were learning in real time. My dad was one of them. That era was the beginning of electrical engineers being trained in computing, not through structured degrees, but through experimentation, industry work, and sheer curiosity. They were inventing the field as they went.

His start-up didn’t go public, file major patents, or get acquired. The company was among a number of small, founder-led electronics and data storage ventures that left very little formal trace. At the same time, it was one of the earliest companies focused on memory storage. At a time when the concept of storing data itself was still being figured out, his work sat alongside the broader breakthroughs that would define modern computing. The 1950s were also when the first true forms of random access memory emerged, with magnetic core memory allowing data to be accessed directly rather than sequentially. That shift changed everything. It laid the groundwork for the RAM that now powers every device we use.

I still remember, as a child, standing in a room the size of our living room, staring at a tiny 4-inch green computer screen surrounded by punch cards that held what was then considered “vast” amounts of data. It felt like looking into the future, even if none of us could truly understand what that future would hold.

His path took him from Manhattan to Phoenix and later to Silicon Valley, and at the end of his life, he was in senior leadership at a then-young Apple Computer, when it occupied just a handful of buildings on Bandley Drive in Cupertino. He died while I was in my final year at Stanford, but not before he saw me graduate.

I think about him often now, especially as headlines question the future of technology in the U.S. and whether moments like today’s AI breakthroughs signal winners and losers. He lived through similar uncertainty. The interface wars of the 1980s. The early battles between Apple and IBM. The moments that felt consequential at the time but only revealed their full impact decades later.

What I learned from him is this: when you’re inside a technological revolution, it never feels settled. It feels messy, contested, and unclear.

We won’t know what today’s inflection points truly mean for another 40 years. But if history is any guide, we are far from the end of the story.

And I find myself wishing he could see it.

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Still Human After All These Years

Lisa Broderick — Sun, 10 May 2026 21:59:44 GMT

Over the past few weeks, it’s been pretty hard for us to miss the profound nature of headlines. AI models which are more powerful and faster than expected pulled back. Debates around regulation and whether anyone can realistically keep pace finally inspiring talk of regulation. Companies not-so-quietly restructuring as the unavoidable impact automation marches forward into reality.

Some of us see unbounded opportunity. Others see full-on catastrophe. Many feel an uneasy mix of the two extremes.

What’s remarkable isn’t just the speed of change. It’s the gap between what these systems can do and how we humans are keeping up with them.

That’s where I think the real issue is.

After decades in technology, starting back in Silicon Valley when the goal seemed simple: make computers work better to better people’s lives, many of us whose careers go back to the 1980’s see a landscape that’s not as easily navigable as it’s historically been.

What’s unfolding now feels different in scale, while also eerily familiar. We’ve been here before, at moments when capability accelerates faster than understanding, and when the consequences only became clear after the fact.

This feels like one of those moments.

So I’ve been writing about it. A short weekly transmission called Still Human After All These Years. It’s not about tech itself. It’s about what it means for us, how we integrate, where we’re likely to lose our footing, and what to think about all of that.

If you’re watching what’s happening with AI and wondering not just what it’ll ultimately be able to do but what it means to us now and in the future, you’ll likely find it interesting.

More soon.