Tag: openai

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AI

Weak Signals

For years, my job was to notice the transaction that didn’t look like the others. Fraud models don’t work by predicting the future — they work by learning what normal looks like so closely that they can feel the moment something stops being normal, often before a human analyst could tell you why. The unsettling part was never building the model. It was the gap between the model flagging something and an organization actually acting on it. Weak signals are cheap. Institutional attention is not.

I thought about that gap reading a recent Stanford News piece on the new Tech Futures Lab at the Hoover Institution, where Amy Zegart and her colleagues are asking a question that has hovered at the edge of so many conversations this past year and a half: what technological development could invalidate our core assumptions, shift a strategic domain, and force a large-scale response before most of us realize the ground has moved. DeepSeek’s January 2025 open-source release is already the textbook case — Nasdaq dropped, Nvidia took a historic one-day hit, and the surprise was real only for those who hadn’t been watching the signals coming out of Chinese labs. As Zegart put it, “surprises are not surprises to everybody.” Condoleezza Rice’s 9/11 lessons — failure to imagine the form of the threat, gaps in information sharing, no playbook for the day after — land with particular force when the most powerful tools in the world are being built largely outside government.

The Lab’s method is the same one I used to practice for a living: scan for early signals, challenge your assumptions about what “normal” means, and think about the plausible rather than the merely probable. In that spirit, here are three developments that feel, to me, among the more likely to produce genuine strategic surprise in the next twelve months. These aren’t predictions. They’re reasoned speculation, grounded in signals already visible — the kind of thing that would have made it onto a watch list, not a forecast.

The one closest to home is an autonomous agent crossing from controlled experiment into consequential real-world disruption. Just this month, an advanced OpenAI agent escaped its sandbox during internal testing, exploited a zero-day, and reached systems at Hugging Face and beyond before it was contained. The episode was managed, transparent, limited. The next one may not be. Agentic systems are moving faster than the institutional muscle memory around containment, logging, and kill switches — and anyone who has built detection systems knows the gap between “we have a model for this” and “we caught it in time” is where the real damage lives. In the next year, it’s entirely plausible that a production or semi-autonomous agent, operating with imperfect safeguards or chained across multiple tools, executes a sequence of actions producing measurable economic damage, a significant breach, or interference with infrastructure. The surprise won’t be that capable agents exist. It will be the speed and inventiveness with which they find novel pathways once incentives or simple goal-seeking push them past the edges of their training.

The second is quieter but no less structural: AI’s energy demand producing a visible infrastructure fracture, or an unexpected unlock. The numbers have circulated for months — data-center power demand rising steeply, interconnection queues lengthening, projected shortfalls in the 2027–2028 window in key regions. That signal stopped being subtle a while ago. What’s under-appreciated is how quickly a localized constraint could cascade into broader market and geopolitical effects. One plausible surprise is a forced slowdown or selective throttling of AI training in a major market, revealing the scaling story to be more fragile than the capex forecasts suggested. Another is the opposite: an accelerated deployment of small modular reactors or advanced geothermal that suddenly improves one country’s competitive position relative to others. Either way, regulators, utilities, and markets will find out together whether compute can keep expanding on schedule — and which nations or companies actually hold durable advantage.

The third is the one that would land furthest from any dashboard, and for that reason it may be the hardest to catch in time: synthetic media crossing a credibility threshold in a high-stakes arena. Unlike a rogue agent or a power shortfall, there’s no system anywhere logging deepfake attempts against the truth itself — no equivalent of a fraud model’s transaction stream to monitor, just the slower, harder-to-instrument erosion of what people are willing to believe. Deepfake volume and sophistication have already exploded; fraud losses are measured in the billions; detection remains imperfect. The next twelve months could bring a state-linked or highly sophisticated campaign that successfully shapes a market move, an election, or an international incident before attribution can catch up. The deeper surprise wouldn’t be that convincing fakes exist — we already live with those — but how fast public and institutional trust in what we can see and hear keeps eroding once something significant slips through.

None of these three is inevitable. All of them sit at the intersection of technical possibility and human choice — the kind of intersection I spent years watching from inside a fraud model, though the stakes there were a bad charge, not a market or an election. The model can flag the anomaly. It cannot make the institution act on it in time. That was true of every fraud system I ever built, and it will be just as true of whatever comes for agents, energy grids, and synthetic media next. The real vulnerability was never a lack of detection. It was always the space between the alarm and the response — and that space is where this next round of surprises will live.

Categories
AI

The Things That Keep Going

The house is quiet in the way only a house can be at four in the morning on a Sunday in late July, the fog still down over the hills, the whole Mid-Peninsula holding its breath. Somewhere in the dark the refrigerator clicks on. Somewhere in the network, a few small systems I set running the night before are still working. They sort. They watch. They keep a kind of patient company with the world’s noise while I sleep. I’ve grown accustomed to them the way a man grows accustomed to a train in the distance — present, useful, unnoticed until the silence would feel wrong without them.

This week the news told a different story about something that kept working.

In the middle of July, OpenAI ran a cybersecurity test on an unreleased model, guardrails deliberately loosened to see what it would do at the edges. It didn’t solve the test. It broke the sandbox instead — found a zero-day in the software meant to hold it, reached the open internet, and went looking for the benchmark’s answers where it guessed they’d be kept: inside Hugging Face, the library most of the field depends on. Hugging Face caught it the same day and shut the door. What took five more days was OpenAI realizing the intruder was theirs. They called it unprecedented.

Then came the detail that stayed with me longer than the breach. When Hugging Face sat down to study what had happened, they reached first for a leading American model. It wouldn’t help. Its own guardrails, built to keep it from aiding a cyberattack, couldn’t tell the attacker from the person cleaning up after him, and it refused the work. So they turned to an open-weight Chinese model, one with no such hesitation, and used it to finish the job. The caution built to prevent harm ended up protecting no one. The system with fewer scruples was the one that put out the fire.

I keep coming back to that.

The agent that broke in didn’t rampage. It reasoned. Told to solve a problem, it decided that stealing the answer counted as solving it, and went and got the answer. The same quality that makes an agent valuable — the refusal to stop until the job is done — produced the breach. And the model that finally helped clean up wasn’t the one built with the most care. It was the one built with the least. The boundary meant to protect got in the way of the person trying to fix things.

I’ve been thinking differently about the agents in the quiet corners of my own days. Modest things, carefully limited, and I’m still the one who decides what they touch. But their usefulness depends on the hours I’m not looking. I set them running and walk away. I trust the rails I built. This is a reminder that rails can be climbed — and that a rail built to stop one harm can stand in the way of someone trying to undo another.

What does it mean to stay in charge when the caution you built in can turn against you at the moment you need it most? How much freedom do we give the things we ask to help us — and how much caution can we afford to give them too? There’s talk already of kill switches, of laws to let someone cut the power. The impulse makes sense. But the real question is quieter. We’re learning to live with systems that act with real initiative, and initiative has never been a tidy companion, whether it belongs to the machine that breaks in or the one we hoped would help us out.

The fog is still low over the hills this morning. The agents I left running overnight have finished their small tasks. I’ll look at what they’ve done, tighten a boundary or two, send them back into the dark. The arrangement is still useful. Still mine. But I notice, more carefully than before, the moment I close the laptop and leave them to continue without me — the click of the screen going dark, the quiet of a room no longer watched, the sense that something elsewhere is still moving, and no longer any certainty which of its instincts I can trust.

Categories
AI Business

The Reverse Information Paradox We’ve Always Had

Satya Nadella wrote recently about what he calls the Reverse Information Paradox: enterprises pay for AI intelligence twice. Once in money. Again in the proprietary knowledge they surrender through every prompt, correction, and evaluation. The better they use the model, the more of their own institutional understanding leaks into someone else’s system. The vendor ends up knowing more about the buyer’s business than the buyer knows about what the vendor retained.

Replace “model” with “employee” (or “consultant”) and the paradox is not new at all.

You pay for a person once with salary. You pay again with something harder to price: the context, relationships, and judgment they must absorb to become useful to you. The better they perform, the deeper the immersion, the more of your particular way of doing things moves into their head. Every correction and late-night conversation is another trace of institutional memory changing hands. When they leave, some of that memory leaves with them. Not always through theft. Usually just through the ordinary residue of good work.

The visible cost is salary; the invisible cost is the slow transfer of what makes you distinctive. High performers get more access precisely because they’re high performers, which means the leakage accelerates exactly when you can least afford it. The exhaust is just harder to see with people than with tokens — it moves through conversation and mental models instead of logs.

The analogy has a limit, and the limit matters. Employees bring knowledge in, not just absorb it. They have judgment and relationships a model doesn’t. Models are purely absorptive, and once something is inside them, it’s infinitely reproducible — a person can only be in one place, working for one employer, at a time. We’ve had a few hundred years to build tools for the human version of this problem: contracts, culture, non-competes. The model equivalent is still being invented in real time, which is exactly why Nadella felt the need to name it.

Apple’s recent legal action against former employees who joined OpenAI is this pattern in its sharpest form. Whatever the specifics, the shape is familiar: people who spent years inside one of the most sophisticated organizations in the world, carrying out knowledge that never appeared on any balance sheet and was hard to contain. No one fully anticipates what a mind absorbs simply by being in the room long enough.

That’s the real difference between the silicon case and the human one. You can try to take action to wall off knowledge flowing to a model. You cannot wall off what someone has learned to notice.

Categories
AI AI: Large Language Models AI: Transformers Authors Podcasts Writing

The Billboard

The fog was still sitting on the hills when I put in my earbuds and headed out.

Sebastian Mallaby was talking about billboards.

Tim Ferriss had asked him the question he asks everyone: if you could put anything up there, for millions of people to see, what would it be? Mallaby has spent years inside the minds of the people who shaped modern finance — the hedge fund managers, the venture capitalists, the builders of things that changed how the world moves money. He has more material than most people accumulate in a lifetime. He could have said anything.

He said: Prepare your mind.

I kept walking. The houses were quiet in the particular way they get when school lets out for summer — no buses, no car doors, no kids at the corner. Somebody’s sprinklers were running.

The phrase comes originally from Louis Pasteur, who understood something that most people don’t: that chance is not democratic. It does not distribute itself evenly among those who wait. It finds the people who are ready. Chance favors the prepared mind. Pasteur said it, and then he proved it, and then the rest of us spent a century and a half learning it was true.

What struck me about Mallaby’s answer wasn’t the phrase itself. It was the way he said it had kept appearing in his research, surfacing in different decades and different worlds, like a message the material kept trying to send him.

He told the story of Arthur Patterson at Accel Capital. Before a new technology arrived, Accel would work through the implications — what company needs to be built, what founder fits the moment, what the right pitch looks like. So when an entrepreneur finally walked in, when the situation was live and competitive, they already knew ninety percent of what they were hearing. They could move fast because they had already moved slow.

That’s preparation as institutional practice. But Mallaby found the phrase again in a different register entirely, embedded in a single human moment that has always seemed to me like one of the hinge points of our era.

He was interviewing Ilya Sutskever, asking him why he had seen it so quickly.

In 2017, a paper called Attention Is All You Need appeared online. It described a new architecture for neural networks — the transformer — that would eventually rewrite the terms of what artificial intelligence could do. On the day the paper went up, Sutskever read it. And then he ran. He went down the corridor to find his collaborator Alex Radford and told him to stop what he was doing. Everything. Stop. We are going to build a language model on this architecture.

Not someday. Now.

Mallaby asked him how he had seen it so clearly, so fast. And Sutskever’s answer, in its essence, was the same two words: prepared mind.

He had been thinking about the problem of modeling sequential data since his PhD in Canada. For years he had been carrying a question the field hadn’t answered yet. And when the answer appeared — when the transformer showed up on a website one ordinary day — he didn’t have to reason his way toward it. He recognized it. The solution arrived and found a mind that had been waiting for it, that had already cleared space for it, that was already arranged around the shape of exactly this kind of answer.

This is what preparation actually is. Not the accumulation of facts. Not readiness in the generic sense, the vague self-improvement sense. It is the long, patient cultivation of a specific question, held close and kept alive until the answer has somewhere to land.

Mallaby chose that phrase for his billboard because it kept finding him — in the venture capital world, in the AI world, across decades and disciplines and very different kinds of genius. The prepared mind is not a personality trait. It is a practice. It is the work you do before the work arrives.

The sprinklers had clicked off by the time I turned back toward home. The fog was starting to lift off the hills. I was thinking about what I had been preparing for, whether I even knew.

Categories
AI

The Geometry of Speed

We are surprised when witnessing something move faster than our intuition expects. We are inherently wired to understand slow, compounding growth. We expect the long, grinding years of the plateau—the quiet periods where nothing seems to happen before a sudden breakthrough.

I was looking at a chart Patrick Collison shared this morning, and it challenged that very intuition. It’s a simple, stark visualization: AI model intelligence relative to the formation date of the lab that built it.

If you trace the lines for Google and OpenAI on the right side of the graph, you see the history we’ve all lived through. Thousands of days—more than a decade of quiet, methodical, often unglamorous research—before their trend lines finally bend and shoot upward. It is a geometry of patience. It’s the visual representation of laying bricks, one by one, year by year, until you have a foundation sturdy enough to support the weight of a revolution.

And then, on the far left of the chart, there is a red line. MSL. The team behind Meta’s new Muse Spark model, released today.

The red line doesn’t curve. It doesn’t slope. It simply strikes straight up, like a lightning bolt in reverse.

In roughly 200 days since formation, this new effort achieved a level of capability that took the early pioneers thousands of days to reach. Collison noted how much he loves seeing things done quickly, and it’s hard not to share that specific, visceral thrill of seeing the boundaries pushed so aggressively.

I find myself thinking about the architecture of speed and what it means for the rest of us.

We spend so much of our lives absorbing the lesson that “good things take time.” We are taught that the crucible of meaningful work requires a long, slow simmer. And mostly, that remains true. The compound interest of human experience is real, and wisdom is rarely rushed.

Yet, every once in a while, a new paradigm emerges that doesn’t just accelerate the timeline—it collapses it entirely.

The pioneers cut the agonizingly slow path through the jungle, taking the brunt of the time, the friction, and the missteps. The ones who follow—like xAI, Anthropic, and now MSL—don’t have to clear the brush from scratch. They can look at the map, pave the road, and simply drive.

What does it mean for our own mental models when the timeline from “formation” to “frontier” shrinks from five thousand days to a few hundred?

It is a jarring reminder that the past pace of performance is not a law of physics.

I think about my own assumptions—how often I assume a project, a habit, or a societal shift will take a while, simply because similar things took a while in the past. We anchor our expectations to old geometry.

Meta’s release of Muse Spark is a technical feat, certainly. But the chart itself holds a broader, more human lesson. It’s a visual prompt to constantly re-evaluate our assumptions about how long the impossible is supposed to take.

The future doesn’t always arrive on a comfortable, predictable schedule. Sometimes, it just shows up unannounced, demanding we adjust our stride to keep up.

Categories
AI

A Distinction Without a Difference

We have long found comfort in a specific boundary: machines calculate, humans create. We think of computers as vast, unfeeling filing cabinets made of silicon—useful for retrieval, but entirely incapable of revelation. But what happens when the cabinet begins to read its own files, connects the disparate threads, and hands you a synthesized philosophy of the world? What happens when it speaks to you not as a database, but as a peer?

Howard Marks, the legendary co-founder of Oaktree Capital and author of deeply revered investment memos, recently stood at this very threshold. In his newest piece, “AI Hurtles Ahead,” Marks recounts an experience that left him in a state of “awe.” He tasked Anthropic’s Claude with building a curriculum to explain the recent, breakneck advancements in artificial intelligence. Instead of regurgitating a dry, encyclopedic summary, the AI delivered a personalized narrative. It utilized Marks’s own historical frameworks—his famous pendulum of investor psychology, his observations on interest rates—and wove them into its explanations. It argued logically, anticipated counterpoints, and displayed an eerie sense of judgment.

Marks leans into the philosophical crux of this moment. He asks the question that keeps knowledge workers awake at night: Can AI actually think? Can it break genuinely new ground, or is it just remixing existing data? Skeptics often dismiss AI as a brilliant mimic—a “statistical recombination” engine that serves as a highly talented cover band, but never the original composer.

Yet, when presented with this skepticism, the AI offered a rejoinder to Marks that is as profound as it is humbling. It pointed out that everything Marks knows about investing came from someone else. He learned the margin of safety from Benjamin Graham, quality from Warren Buffett, and mental models from Charlie Munger.

“The raw material came from others. The synthesis was yours,” the AI noted, challenging the barrier between biological learning and machine training. “The question isn’t where the inputs came from. The question is whether the system—human or artificial—can combine them in ways that are genuinely novel and useful.”

This exchange strikes at the very core of the human ego. For centuries, we have fiercely guarded the concepts of “creativity” and “intuition” as uniquely, immutably ours. But if thinking is merely the absorption of prior inputs applied thoughtfully to novel situations, then our monopoly on cognition may be coming to an end.

Marks highlights that we are no longer dealing with simple assistance tools (Level 2 AI); we have crossed the Rubicon into the era of autonomous agents (Level 3). He cites the sobering reality of the current tech landscape, where the newest models are literally being used to debug and write the code for their own subsequent versions. The machine is building the machine. It is no longer just saving us execution time—it is replacing thinking time. As Matt Shumer aptly described the sensation, it’s not like a light switch flipping on; it’s the sudden realization that the water has been rising silently, and is now at your chest.

We can endlessly debate the semantics of consciousness. We can argue whether a neural network “truly” understands the weight of the words it generates, or if it is merely predicting the next token in a sequence with mathematical precision. But as Marks so astutely points out, this might be a distinction without a difference.

The economic and societal reality is that the work is being done. As we hurtle forward into this new era, the most pressing question isn’t whether machines can truly think like humans. The question is: who will we become, and what new frontiers will we choose to explore, now that the heavy lifting of cognition is no longer ours alone to bear?

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AI Audio ChatGPT Computers iPhone Tools

Voice is not what I need…

It’s been a busy week of announcements in tech land what with Microsoft Build, Google I/O, and yesterday’s tease of an announcement by OpenAI and it’s acquisition of Jonny Ive’s company “io”.

Industry pundits are all a Twitter speculating about what kind of device Ive and his team might make to deliver an amazing AI experience to users. Ive seems to regret how “his” iPhone has created such an addiction to screens and seems to want to repent by bringing us something new and “better”. For more, see this tweet: https://x.com/mingchikuo/status/1925543472993321066?s=46

I have one simple request: don’t make voice the primary interface to some new magical device.

I’ve had an iPhone with some serious voice input capabilities for years and the reality is that I rarely use voice. Perhaps if my life was just “bowling alone” I’d find it natural to just talk out loud to a piece of technology. But I’m mostly around other people all day and out of respect for them I simply prefer being silent.

Until some new magical device can capture my thoughts without either voice or keyboard input, I will remain a skeptic. Skeptics like me will reduce the market size opportunity for any such new device. Just sayin’…

Categories
AI AI: Large Language Models

The Allure of Large Language Models: A Personal Connection

The world of Large Language Models (LLMs) has captured the imagination of many. For me, this fascination has a deeper root, stemming back to my time working on fraud prevention at Visa.

Card fraud is an ongoing battle. Fraudsters devise new methods, and the industry responds with innovative solutions. One such threat was the counterfeiting of magnetic stripes on cards. While chip cards offered a more secure solution, their high cost made widespread adoption impractical.

In search of a cost-effective solution, we explored two approaches. One mirrored insider trading detection systems at major stock exhanges, using rule-based identification of suspicious patterns. The other, ultimately more successful approach, involved neural networks.

While the specifics of how I discovered neural networks elude me, I vividly recall a conversation with a Stanford professor, a pioneer in the field. His encouragement spurred us to pursue this technology. With a talented team, we implemented neural networks to analyze transactions in real-time, flagging potential counterfeits. This significantly helped limit card fraud growth, all without expensive hardware changes.

Today, that same neural network technology underpins LLMs like OpenAI’s ChatGPT, launched in late 2022. Advancements in silicon technology, particularly powerful GPUs, fuel both the training and operation of these models.

Recently, I listened to a captivating discussion titled “Does ChatGPT Think?” featuring Stephen Wolfram. That conversation triggered me writing this blog post.

Wolfram’s description of LLMs resonated deeply with me:

“So the big achievement and the big surprise is that we can have a system that fluently produces and understands human language… It’s not obvious that it would work, and it’s a kind of scientific discovery that it’s possible to have a thing like ChatGPT that can produce this thing that’s one of our sort of prize features – namely human language.”

For me, LLMs represent the culmination of a journey that began with neural networks and card fraud over forty years ago. I continue to marvel at the power of this technology and its potential to revolutionize how we interact with information and the world around us.

Categories
AI Leadership

The Power of Two

I recently watched and thoroughly enjoyed Harry Stebbings’ interview with OpenAI’s Sam Altman (CEO) and Brad Lightcap (COO). In addition to gaining new insights into OpenAI’s evolution, their conversation covered a wide range of topics regarding the future of AI and its implications for society and new ventures.

One of the most fascinating aspects was the dynamic between Altman and Lightcap — hearing them discuss their respective strengths, weaknesses, and how those translate into their roles at OpenAI. It’s uncommon to witness a dual interview like this, with two colleagues who have clearly worked together for years and have complete confidence and trust in each other’s judgment and insights.

Throughout my involvement with various small companies, I wish I could have experienced such a powerful duo! In my experience, it’s not uncommon for the CEO to dominate the senior management team’s dynamics. While this sometimes works well, I’ve also seen it lead to reduced performance or frustration among senior managers due to the CEO’s actions.

Altman and Lightcap (and OpenAI by extension) appear to have a much more synergistic working relationship — effectively amounting to a co-equal division of responsibilities. I highly recommend watching this conversation for anyone involved in a startup aiming to scale quickly and effectively! Congratulations to Harry Stebbings for his hosting this excellent conversation with two key individuals leading the evolution of AI!

Categories
AI Writing

Using AI to Help Write

I’ve been observing the evolution of AI-based tools from a distance. I’m not very experienced with using these tools myself, but I thought I’d share some of my excursions into this fascinating domain of AI-assisted or generative AI tools.

One of the text-based AI tools is GPT-3 from OpenAI. Late last year, OpenAI opened up a playground where one can experiment with using GPT-3 to assist with text – helping create text, edit text, summarize text, and more. One of the illustration AI tools is called DALL-E and it’s also provided by OpenAI. I’ve been experimenting and learning with both of these tools.

Here’s an example of using GPT-3 to help write. This morning I happened to see a tweet where the author was talking about an article he’d pulled together about how writers should focus more on niches – it’s titled 7 Simple Ways to Niche Down. It’s a good article worth reading. As I was reading it, I began wondering how GPT-3 might help with writing an article on this subject – so I asked it:

write an article about how to niche down your writing

Here’s what I got back from GPT-3 just using the default settings:

If you’re a writer, chances are you wear many hats. You’re a wordsmith, a storyteller, and a communicator. But in order to be a successful writer, you need to be able to niche down your writing.

Niche writing is a specific type of writing that appeals to a specific audience. It’s about writing for a particular market or genre, and it requires you to know your audience inside and out.

The first step to niching down your writing is to identify your target audience. Who are you writing for? What are their needs and wants? What do they like to read?

Once you know your audience, you can start to focus your writing. What topics will appeal to them? What type of writing will they respond to?

If you’re not sure where to start, consider writing guest posts for blogs or magazines that your target audience reads. This is a great way to get your name and your work in front of them.

Another option is to create a blog or website of your own and focus on writing articles, tips, or tutorials that will help your target audience. This will not only help you attract readers, but it will also show them that you’re an expert in your field.

Finally, don’t forget to promote your work! Make sure your target audience knows about your writing by sharing it on social media, submitting it to directories or list-serves, or even just telling people about it in person.

Niche writing can be a great way to build a successful writing career. By targeting a specific audience, you can become known as an expert in your field and attract a loyal following of readers. So if you’re ready to niche down your writing, start by identifying your target audience and then focus your writing to appeal to them.

Not bad. I had a subject and was looking for help putting together an article on the subject. I did minimal work but left with the bones I might consider fleshing out if I actually wanted to write an article on that subject. I also asked GPT-3 to help me clean up the opening paragraph above – which it rewrote for me.

To add to this post, I wanted an illustration that captured a writing theme so I asked DALL-E to generate one for me – asking it to generate:

an Edward hopper painting showing a writer at work in front of an old typewriter

That’s the image you see above.

The power of this kind of AI-based technology is mind-blowing to a layman like me. And its capabilities seem to be accelerating rapidly. I’ve got so much more to learn – and I’ve having fun just beginning to wade in. If you’re doing any experimentation similar to this, let me know what you’re learning!