Tag: Visa

Categories
Banking Markets

The Honesty of the Long Bond

Treasury Bond

There is a particular sound a fraud model makes right before someone silences it. Not an alarm, not a siren — a score. A number ticking upward on a screen, quietly, the way a fever climbs before anyone thinks to take the temperature. At Visa, in the years when the network was still teaching itself to smell trouble before trouble arrived, the worst mistake wasn’t missing the signal. It was seeing the signal and deciding, for reasons that felt reasonable in the room, to turn the threshold down. To make the number stop being inconvenient. The fraud didn’t go away when you did that. It just went un-priced for a while, and un-priced things have a way of arriving all at once, later, with interest.

I thought about that instinct — the turned-down threshold — reading Stanley Druckenmiller’s account of what the Treasury Department did on Aug. 19. The 30-year yield had touched a nineteen-year high. Within hours, Treasury announced it would double its long-dated bond buybacks, from two billion dollars a operation to at least four, running through early November. Yields fell. By the next afternoon they’d round-tripped back above where they started. The market had said its piece and gone back to saying it.

Druckenmiller’s point is not really about buybacks. Four billion dollars against a marketable debt stock nearing thirty trillion is a rounding error, and he says so. His point is about what a price is for. The long Treasury yield is the closest thing this country has to an incorruptible witness — a number nobody in Washington controls, that aggregates what millions of lenders actually believe about a borrower’s arithmetic, and reports back without spin. Inflation running above target since 2021. Unemployment low enough to call full employment by any definition. A deficit near six percent of GDP in peacetime, at full employment, which is not a thing this country has produced before. Interest payments outrunning the defense budget. The debt crossing forty trillion the same week Treasury decided the honest price of borrowing against all of that was too loud, and needed managing.

Every institution I’ve ever trusted, from a payments network to a family, runs on the same unglamorous premise: you don’t get to like the number and keep the number. You get one or the other. A fraud score you keep dialing down to preserve the illusion of a clean day isn’t measuring less fraud. It’s measuring your own unwillingness to look. A ten-year yield held below what the underlying arithmetic says it should be isn’t cheaper borrowing. It’s a subsidy, paid by whoever holds dollars, to the politicians who’d rather not have the conversation this year either.

What strikes me, coming from a career spent building systems that exist specifically to catch the moment before the moment gets expensive, is how familiar the defense sounds. It’s routine, they’ll say. It’s liquidity management, cash management, a tool introduced in 2024 for exactly this purpose. All of which can be true and still be beside the point, because routine operations don’t get announced off-cycle, at double size, days after a two-decade high, with an unnamed official telling reporters the Treasury General Account is available too if the market keeps testing resolve. You judge an intervention by what it’s responding to. This one was responding to a price. The market knew it inside of a day and treated the intervention accordingly — which is itself a kind of honesty, the last one still working.

Druckenmiller has been saying some version of this for fifteen years, across debt-ceiling fights and entitlement tours and a scorching verdict on Janet Yellen’s failure to lock in generational-low rates while the door was open. I’ve watched Warren Buffett say an adjacent thing for longer than that, in his own patient register — that a country, like a person, eventually pays for the years it spent not wanting to know its own number. Berkshire’s whole temperament is built around that patience: hold the cash, wait for the price to tell the truth, don’t confuse a quiet tape for a healthy one. It is not an accident that the investors I’ve trusted longest are the ones most willing to sit with an uncomfortable number rather than manage it into silence.

None of this requires believing the ending is dark. Druckenmiller’s own framing is closer to an invoice than a verdict: if the thirty-year needs to trade at five and a half percent to clear, that isn’t a crisis, it’s a bill arriving on schedule, from a system that’s been sending polite notices for a while. The alternative — dialing down the threshold, buying the quiet — doesn’t make the underlying condition go away. It just moves the reckoning to a moment you didn’t choose, at a price you don’t get to negotiate.

I keep coming back to the fraud model, and the engineers who used to argue about where to set it. The good ones never asked how to make the score friendlier. They asked what the score was trying to tell them, and whether they were still willing to hear it.

Categories
Aging Living

The Manufacture of Emergency

The screens at Visa never went dark. Somewhere in a data center, transactions were clearing at a rate that made the number itself feel abstract, and our job was to sit inside that river and notice the one drop moving wrong. A fraud ring testing stolen numbers in ascending increments. A merchant category code that didn’t match the geography. A velocity spike that looked, if you squinted, like nothing at all, until it didn’t. The work had a shape to it: quiet, then a pattern surfaces, then the chase, then the catch or the near-miss, then quiet again. I didn’t think of it as drama at the time. I thought of it as Tuesday.

It took me a long time to notice that the shape of that work was also the shape of something in me, and that I’d built a career, without quite meaning to, out of environments that manufactured the same cycle over and over. Fraud detection was one. A Piper Cherokee is another — not because flying is reckless, it isn’t, competent flying is the opposite of reckless, but because every cross-country trip contains a small structured emergency built into the planning itself: the weather that might close in, the fuel math that has to work, the decision point where you commit or you divert. You solve it. You land. The relief is real and it is, if you’re honest with yourself, part of what you came for.

Retirement removes all of that. Nobody hands you a fraud queue. There is no fuel gauge counting down over Ohio. What retirement hands you instead is something much harder to sit inside: an undifferentiated stretch of days with no built-in shape, no crisis with a clock on it, no adversary to defeat by five o’clock. I did not expect this to be difficult. I had spent decades, if you’d asked me, wanting exactly this — quiet, unstructured time, the absence of alarms. And for the first while it was wonderful. Then I noticed I had opened a second screen.

The options positions started small and reasonable, the way these things do. A covered call here, a defined-risk spread there, sized so that nothing could actually hurt. But I noticed the pull on days when nothing needed attention. I’d be three-quarters through a walk around the pond, the morning doing exactly what a morning is supposed to do, and I’d feel for my phone anyway, not because a position needed managing but because the quiet had started to feel like something unresolved. I would open the app looking, if I am being precise about it, less for information than for a small manufactured stake — something with a clock on it, something I could watch move against me and then, with luck, defeat.

This is not a confession about trading, which when done with defined risk and a plan is just a hobby with numbers attached. It’s an admission about what I was actually shopping for on those walks, which was the old shape. Tension, then resolution. Fear, then relief. The problem with the shape is that it doesn’t ask whether the underlying situation warrants it. It just asks to be fed. A market that is genuinely quiet gets treated the same as a fraud queue with a live threat in it, because the appetite doesn’t distinguish between real stakes and stakes you went looking for. You can tell yourself you’re staying sharp. Sometimes you’re just bored, and bored, for a person built the way I apparently am, does not feel like rest. It feels like a low hum of unfinished business, and unfinished business is a thing I have spent a working lifetime being paid to resolve.

I don’t think this gets solved so much as it gets noticed, and noticing changes the ratio a little. The phone is always in my pocket; that isn’t the part I can negotiate with. What I’ve started doing instead is naming the moment my hand goes for it on a walk that doesn’t need interrupting — catching the reach itself, mid-motion, and asking what exactly I’m expecting to find. Most mornings I still open the app. But I open it now as someone watching himself do it, which is a smaller thing than stopping, and also, it turns out, not nothing.

Categories
AI

Claude as Walter Cronkite

Gavin Baker said something this week that stuck with me.

In his latest conversation with Patrick O’Shaughnessy, he described a quiet shift happening across public markets. Nearly everyone he knows in the equity business—retail and institutional—now feeds every piece of news straight into Claude. Sometimes Claude Code. Sometimes a Claude agent. The model is probabilistic, he noted, and he was speaking from what he sees in his own network rather than from a measured study. But his impression was that the variation in how it interprets the same information is surprisingly small. A huge chunk of the market ends up trading on a shared reading of events.

Baker reached for an old analogy: Claude has become Walter Cronkite for the stock market. The single trusted voice. Everyone just believes what it says.

He tied the observation to Michael Mauboussin’s work on how a breakdown in diversity of thought helps create the conditions for bubbles and crashes. When independent judgment collapses into a narrower set of interpretations, the system becomes more brittle. Moves get sharper. Errors get amplified.

I spent the back half of my career inside fraud detection systems at Visa, watching correlated failure up close. The lesson that never left me: the dangerous moment isn’t when a single model is wrong. Individual errors wash out. It’s when every model in the ecosystem is wrong in the same direction, because they were trained on the same data, tuned against the same benchmarks, built by people reading the same journals and hiring from the same three schools. A fraud ring doesn’t need to beat your model. It needs to find the blind spot every model in the industry shares. That’s not a tail risk. That’s the whole risk.

Which is what made me sit up a few weeks ago, watching a position reprice in a straight line and catching myself, mid-scroll, about to ask Claude what it thought was happening before I’d looked at a single primary source myself. The tool hadn’t done anything wrong. I had reached for the shared interpretive layer before reaching for my own judgment, out of habit, the way you reach for a light switch in a dark room you’ve walked through a thousand times.

Dan Geer wrote about this two decades earlier, from a different angle entirely. Geer and colleagues argued that Microsoft’s dominance had created a software monoculture: nearly identical systems sharing the same vulnerabilities. In biology, monocultures are efficient until a pathogen finds the common flaw. Then the failure is systemic rather than local. Diversity limits the blast radius. Geer’s point was never that the dominant platform was worse in isolation. It was that identicality itself becomes the risk multiplier.

Baker is describing a cognitive version of the same phenomenon.

The platform is no longer Windows. It is a frontier model that a large fraction of market participants now use as their primary interpretive layer. The shared vulnerability is not a buffer overflow. It is a common set of priors, training data, reasoning patterns, and prompt conventions. Slight probabilistic differences still exist. But the center of gravity of interpretation has tightened.

The result is correlated positioning. Feedback loops that reinforce themselves. A market that can reprice more violently than the underlying fundamentals alone would justify. In July we watched AI and semiconductor names drop 40–60 percent in a straight line while on-the-ground metrics—GPU rental prices rising, token growth accelerating, hyperscaler operating cash flow strengthening—told a different story. One plausible contributor to that gap is an AI-mediated consensus that overweighted certain narratives relative to the harder data.

There is an important difference in degree. Software monocultures create technical cascade risk you can patch. Interpretive monocultures create cognitive cascade risk you can’t—there’s no CVE number for a shared blind spot in judgment. The latter is softer and harder to measure. But the mechanism is familiar: reduced diversity of independent judgment.

I use these models constantly. They compress research, surface patterns I’d have missed, and force clearer thinking when I use them well—Claude caught an inconsistency in a cash flow assumption last month that I’d read past twice on my own. That’s real. The danger isn’t the tool. The danger is treating the tool as the authoritative voice rather than one input among many. The edge increasingly belongs to people who combine the model’s speed with proprietary data, primary research, domain experience, and a willingness to hold non-consensus views. Those who simply outsource the interpretation may find themselves more correlated than they realize, and won’t know it until the moment it matters.

Diversity of thought was never free. It was always work.

I noticed myself skipping the work, just for a second, on an ordinary Tuesday. That’s usually how it starts.

Categories
Computers IBM

The Day the Last Mainframe Went Dark

Note: I literally grew up during the heyday of the IBM mainframe era. My first real job was working for IBM in San Francisco beginning in 1968. I’m a “big iron” kind of guy. But this post was imagined after reading the following in a July 14, 2026 announcement from IBM: When we discussed our expectations with you in April, we noted that we would be wrapping on the launch of z17 in the second quarter. Given this was the strongest start to a mainframe program in our history, we expected Infrastructure revenue to decline low-single digits for the year, beginning this quarter. What played out was worse than our expectations, driven by a shortfall in our Z performance and the associated software stack, primarily in Transaction Processing. In the last few weeks of June, we saw clients shift their quarterly capex spend toward servers, storage, and memory purchases to secure supply-constrained infrastructure ahead of expected price increases. This dynamic impacted client buying patterns. While we anticipated some supply chain related impact in our expectations, we did not anticipate the magnitude of the capex reprioritization.

It won’t arrive with fanfare. No countdown, no viral video of engineers raising a glass. One morning—sometime in the 2040s, perhaps later—a small team in a climate-controlled data center will complete the final cutover. They’ll flip the switches, watch the lights dim, and listen as the hum of the last production IBM mainframe fades to silence. An era that began with the System/360 in 1964 will end. Not with a crash. With the soft click of obsolescence.

We’ve been predicting the mainframe’s death for decades. In the early 1990s, pundits declared it doomed. They were wrong. Those systems—reliable, secure, capable of staggering transaction volumes with near-perfect uptime—became the invisible backbone of modern life. Your last bank transfer, airline reservation, insurance claim, or government benefit likely touched one. They endured because they solved hard problems well: high-volume, mission-critical processing where failure was never an option.

The path to that final power-down was never a rupture. It was a long, uneven evolution—driven by economics, technology, talent shifts, and the patient work of modernization. AI tools accelerated the transition. They didn’t cause it.

Lessons from Earlier Transitions

Steam engines dominated railroads for generations—powerful, reliable, deeply integrated into the industrial economy. Diesel won through incremental advantages: better efficiency, lower maintenance, longer trains with less labor. Railroads rebuilt infrastructure and retired the old iron as the economics aligned, route by route.

Prop planes opened the skies to mass travel. Jets brought speed and range that transformed global connectivity—but airlines didn’t scrap fleets overnight. They ran hybrids during the overlap, invested in new airports and training, and retired props as jet economics and passenger demand made the case irresistible.

The mainframe followed this pattern. AI coding agents—Claude Code, Cursor, OpenAI models, AWS Transform, IBM watsonx—transformed the brutal manual work of understanding undocumented COBOL, extracting buried business logic, generating tests, refactoring safely. What once demanded scarce veteran experts for months or years could now be accelerated, with rigorous human oversight and equivalence testing.

Platforms like Visa’s Pismo showed a smarter path: incremental modernization. Cloud-native microservices layered alongside legacy cores, rather than rip-and-replace. Banks demonstrated real progress. Hybrid strategies won—AI inference running close to sensitive data on evolved mainframes (IBM’s z17 and successors, with on-chip accelerators), while new applications and analytics moved to elastic cloud environments.

IBM positioned the platform as an “AI factory” for low-latency, secure workloads. But pricing pressure was constant. High, capacity-based software licensing made the economics harder to defend as cloud offered predictable, usage-driven costs and younger talent gravitated toward modern stacks. For CFOs weighing rising maintenance against retiring COBOL expertise and AI-assisted migration, the scales tipped.

By the mid-2030s, competitive and regulatory forces intensified. Fujitsu’s exit from mainframes created a cliff in affected markets. Skills shortages accelerated. Even the most conservative holdouts—ultra-high-volume, regulated systems in finance, government, specialized industries—began serious moves, as simulation environments and exhaustive parallel testing brought the risk down to manageable size.

The Final Act

The last systems to go were the stubborn ones, where disruption carried outsized consequences. When the final cutover succeeded—after months of flawless parallel running—the team powered down the machine. A global bank, a payments processor, a government entity. Maybe a small ceremony: engineers who’d kept it alive for decades, trading stories of the iron that never failed when the world needed it most.

Picture the aircraft boneyards outside Tucson or Victorville, retired 747s sitting in rows under the sun, giving up parts to new generations before they’re recycled. Mainframes will meet a similar fate, more climate-controlled. Some linger in warehouses as insurance, still humming faintly in test or archival roles. Others get dismantled by IT asset disposition teams—data wiped to standard, processors and I/O cards harvested for niche markets. The bulk gets recycled, metal and circuitry returning to the supply chain. Like the jets, the iron won’t vanish in disgrace. Its lessons in reliability and disciplined engineering at scale live on, embedded in whatever comes next.

The world didn’t stop. Transactions kept flowing, now on distributed, elastic, AI-augmented platforms that had absorbed the best of what came before. The mainframe era didn’t end in failure. It ended because better options finally existed for every workload.

What Endures

We’ll look back with respect and nostalgia. The mainframe wasn’t flashy, but it taught something durable: some problems reward obsessive focus on reliability and scale; disciplined engineering outlasts hype cycles; the wisest transitions are rarely clean breaks. They’re patient evolutions that carry forward what matters.

IBM will have completed its own transformation by then—software, services, hybrid orchestration, AI tools that work across environments. The company that built the platform helps close the book on it.

The last mainframe going dark won’t feel like loss. It will feel like the natural close of a chapter that powered the digital economy through its most formative decades. The iron did its job. Now the next architecture takes the stage, standing on shoulders built to last.

Categories
AI Apple Google

The Library You Already Own

Sharon Park in the morning is not a dramatic place. There’s a duck pond, a stand of oaks that go gold too briefly in November, and a loop I’ve walked enough times that my legs know it better than my eyes do. It is, in other words, exactly the kind of place where a person starts talking to himself. Not out loud. In the productive, low-grade way — turning a sentence over, arguing with an idea from the day before, checking a thought against something you believe about yourself.

I think in five years I’ll be doing that walk with something else along. Not a search engine. Not another chatbot trained to know a little about everything and a lot about nothing in particular. Something closer to a second set of eyes on my own life — a reasoning engine, lean and mostly private, that has actually read the things I’ve written and doesn’t need me to explain who I am before it’s useful.

Here’s the distinction that matters, and it took me longer than it should have to see it clearly. The AI industry has spent years in an arms race over how much of the world a model can hold — more facts, more languages, more of the internet compressed into weights. That race will keep going, and somebody else can have it. What I want is smaller and stranger: a model that knows comparatively little about the world and quite a lot about me. My core values document. The portfolio spreadsheets. Fifteen years of blog posts. The half-finished notes for the I-280 project, sitting in a folder, waiting for someone — or something — to ask the right question about them.

I spent a career in payments infrastructure, which means I spent a career thinking about a very specific kind of trust: the kind where a stranger’s system has to make a judgment call, in milliseconds, about whether to say yes. Fraud models don’t work because they know everything about commerce. They work because they know an enormous amount about one account, one pattern, one person’s ordinary Tuesday — enough to notice when Tuesday stops being ordinary. That’s the architecture I keep picturing, aimed inward instead of outward. Not a system trying to know the world. A system trying to know me, well enough to notice when I’m drifting from what I said I cared about.

I can already feel the shape of the mornings this would change. Right now, when I sit down to look at RMD requirements against the tax picture, I’m doing the translation myself — pulling numbers into a story I can actually feel the weight of. A reasoning engine grounded in my real holdings wouldn’t just run the scenario. It would know that I don’t want the scenario dressed up as a spreadsheet; I want it dressed up as a conversation, unhurried, the kind you’d have over lunch with someone who already knows the whole situation. And on the mornings when I sit down to write, instead of staring at a blinking cursor and a blank page that has no idea I exist, I’d be handing a draft to something that has actually read my last two hundred posts and knows the difference between the sentence I’d write and the sentence I’d cut.

None of this is especially exotic technology. Apple and Google are already building toward it — Neural Engines fast enough to do real reasoning on-device, retrieval systems that can reach into your own files instead of the entire internet, fine-tuning that’s getting cheap enough to personalize rather than merely customize. The more interesting story here isn’t privacy, though privacy is real. It’s architectural: what happens when the expensive, impressive part of the system — the part that knows everything — becomes optional, and the cheap, personal part — the part that knows you — becomes the whole point.

What I don’t yet know is what this will cost me. A tool that reasons this well about my own life is also a tool I could lean on instead of doing the leaning myself, and there’s a version of this future where the walk around Sharon Park stops being mine and starts being a conversation with something that finishes my sentences a little too well. I’d want some way of knowing, plainly, what it’s drawing from and what it’s guessing at — less a nutrition label than a kind of honesty I could check against, the way you’d check a fraud model’s confidence score before you trusted it with a yes.

But most mornings, I think I’d take the trade. Not because I want to think less. Because for thirty years I’ve been collecting the raw material — the notebooks, the portfolios, the half-built essays — and it would be something, finally, to walk beside a mind that had actually done the reading.

Categories
Aging AI Business Living

The Being Phase

There is a metric making the rounds in technology investing circles that is, on its face, about market share and revenue concentration. Alex Sacerdote of Whale Rock Capital calls it the New Rule of 40 for AI. The formula is simple: take the percentage of a company’s sales derived from AI, add its percentage market share in that AI category, and if the sum reaches 40, you have a winner. Celestica, a company most people have never heard of, scores extraordinarily well. It owns somewhere between half and sixty percent of the cloud Ethernet white-box switch market. NVIDIA doesn’t need a formula. It simply is what it is.

Sacerdote designed the metric to cut through a specific kind of noise — the companies claiming AI exposure they don’t actually have, the giants whose AI revenue hovers at one or two percent of their base while their press releases suggest otherwise. The framework is a detector. It finds the companies that have stopped becoming AI infrastructure and started simply being it.

I found myself less interested in the companies than in that distinction.

I spent years at Visa watching a network that had long since crossed that threshold. By the time I arrived, Visa wasn’t becoming the global payments infrastructure. It was the global payments infrastructure. The work was real — fraud detection, modeling, the daily labor of keeping something enormous running — but the existential question had been settled before I got there. The network existed. Merchants accepted it because cardholders carried it. Cardholders carried it because merchants accepted it. That loop had been closing for decades. We were custodians of a fait accompli.

There’s a particular feeling to working inside something that has already won. It’s not complacency exactly. The problems are genuine and the stakes are high. But the uncertainty has a different quality — it’s operational uncertainty, not existential uncertainty. You’re not asking whether the thing will survive. You’re asking how to run it well.

I didn’t have language for that distinction then. Sacerdote’s metric gives me some. The companies that score highest on his New Rule of 40 have resolved their existential question. They’re not fighting for position. They’re administering a position already held.

The question that has followed me out of that career, and out of several decades of watching technology cycles turn, is simpler and more personal than any investment framework.

When did I cross that line myself?

I have been writing at sjl.us since 2001. That’s not a boast — it’s a data point. Twenty-five years of thinking out loud, of ideas arriving rather than being argued, of the specific memory as structural anchor. The blog is not becoming anything. It is what it is: a record of a mind moving through time, accumulated into something that has its own weight and shape.

The book on payments systems exists. The career at Visa exists. The photographs exist. The train journeys exist. The years in Dayton exist, and the years on the Peninsula, and the particular way the light falls on the California coast at Pescadero in the late afternoon — when the fog is still offshore and the hills are improbably green and everything goes briefly, completely quiet, as if the world is deciding whether to continue.

These are not things I am building toward. They are things I am.

Sacerdote would say I have high market share in a specific category. The category is small — one person, one particular configuration of experience and attention and accumulated knowing — but the share is essentially total. There is no competitor for the position of having lived this particular life. The moat is absolute. The switching costs are infinite.

I used to find that thought melancholy. The narrowing as loss. The aperture closing on what remains.

I’m not sure I find it melancholy anymore.

The L-Curve, Sacerdote says, is a long flatline followed by a vertical explosion. The tinkering phase, then the moment of lift. He means it as a description of demand curves for technology infrastructure. But I recognize the shape from somewhere closer. The long middle of a life, building and becoming, and then the morning you wake up and realize the building is substantially done. What remains is the being.

That’s not an ending. It’s a different kind of beginning.

Sacerdote’s metric will eventually stop working. All frameworks do. The AI infrastructure cycle will mature, the L-Curves will flatten, and some new measure will emerge to find the next thing that is just beginning to become what it will be. That’s the nature of markets. The detector has to change as the signal changes.

But there’s a complication worth naming. Analysts at Citadel Securities published a note recently observing that even the most powerful technologies must pass through the prosaic discipline of cost curves, capacity constraints, and marginal returns. Token bills are arriving unexpectedly. Compute is scarce. The vision of AI as ubiquitous, frictionless, and immediate is colliding with physical reality. Their conclusion: asset prices will periodically be forced to reconcile ambition with physical constraint.

That’s not a refutation of Sacerdote. It’s a reminder that feeling like you’ve arrived and having actually arrived are different things. The being phase has to be load-tested. The position has to hold under pressure.

I think about the fiber optics Corning is laying into the massive data center clusters — ultra-thin, bendable, carrying more light than anything that came before. The cable doesn’t know it’s infrastructure. It just carries what it’s given, at the speed it’s capable of, across whatever distance is required. It doesn’t matter what the cable believes about itself. What matters is whether the light actually moves.

That seems right to me. You become what you are over a long time, largely without noticing. And then one day someone builds a metric that accidentally describes your life, and you recognize yourself in it, and you think: yes. That’s the shape of it. High concentration. High share. A moat that deepened while you were looking elsewhere.

But the moat still has to hold.

The being phase, it turns out, is not the end of something. It’s the proof that something was built. And the daily question — for companies, for infrastructure, for a person in his late seventies still writing, still paying attention — is whether what was built is actually load-bearing.

You don’t get to stop finding out.

Categories
AI AI: Transformers

The State You Never See

The transaction arrives in milliseconds. A purchase attempt — a gas station in Phoenix, a grocery store in suburban Atlanta, a wire transfer at 2 a.m. — and somewhere in the authorization chain, a system has to decide. Not later. Now. The clock is already running.

When I led the fraud detection team at Visa, this was the problem that lived in your chest. You couldn’t see what you needed to see. You couldn’t know whether the person presenting that card was the person who owned it, whether the account had been compromised six hours ago in a breach you hadn’t yet detected, whether the behavioral signature of these transactions was the legitimate cardholder running errands or a fraudster working methodically through a stolen number before the window closed. You could only see what the transactions said. You could never see the state underneath.

That distinction — between what you can observe and what is actually true — turns out to be one of the organizing problems of our time. It has a name, a formal structure, and a history that runs from mid-century mathematics through the trading floors of quantitative hedge funds to the frontier of artificial intelligence. The name is the hidden Markov model. But the problem it addresses is older than the math, and more human than the jargon suggests.

Continue reading “The State You Never See”
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.