Hey everyone, welcome back to My Weird Prompts. We are coming to you from our home in Jerusalem, and I have to say, the energy in the house has been a bit different this week. It is that transition into early March, the light is changing over the stone walls, and it feels like a time for reflection. Our housemate Daniel sent us a prompt that really got me thinking about how short our collective memory is when it comes to technology. We live in this era where everyone acts like artificial intelligence was invented on November thirtieth, twenty twenty-two, when ChatGPT dropped. It is like the world thinks we just discovered fire, but the reality is that some people have been sitting by the hearth for forty years.
It is the ultimate overnight success myth, Corn. Herman Poppleberry here, and I have been chomping at the bit to talk about this. People see these flashy generative models and think we have just entered the age of AI, but for some of us who have been buried in the research papers for years, it feels more like the world finally noticed a marathon that has been running since the middle of the last century. Daniel’s question today is about the long haulers, the industries that didn't wait for the hype. They have been operationalizing machine learning and probabilistic modeling for decades. We are talking about sectors where if the AI fails, people die or economies collapse. This isn't about writing a funny poem; it is about mission-critical infrastructure.
Right, and that is a crucial distinction to start with. We need to separate simple automation, like those old if-then-else logical trees, from actual artificial intelligence. Daniel was asking about sectors like defense, medical imaging, and finance. These are areas where the stakes are incredibly high. You cannot just have a hallucinating chatbot when you are looking for a tumor or trying to intercept a missile. So, Herman, where do we actually draw the line? When did we stop just doing fancy math and start doing what we can honestly call AI in these legacy sectors?
That is the right place to start. If we look at the history, we have to look at the transition from expert systems to pattern recognition. In the nineteen eighties, the big trend was expert systems. These were essentially massive libraries of rules encoded by human experts. If a sensor sees X and the altitude is Y, then do Z. It was impressive, but it was brittle. It couldn't handle the messy, noisy reality of the physical world. The true long haulers are the ones who moved into statistical inference and neural networks way before it was cool. If you look at the military side, which is a great place to start, we are talking about the late nineteen seventies and early nineteen eighties. This was the era of the Strategic Computing Initiative.
That early? I mean, back then, the computing power in a handheld calculator was considered high-tech. How were they running anything resembling a neural network? I remember my first computer in the late eighties, and it could barely load a text file without screaming.
They weren't running large language models, obviously, but they were working on something called Automated Target Recognition, or ATR. This was a massive focus for the Defense Advanced Research Projects Agency, or DARPA. Think about a cruise missile or a high-altitude reconnaissance drone. You have a massive amount of sensor data coming in, like synthetic aperture radar or infrared imagery. A human cannot process that in real-time at those speeds, especially not in a combat environment. So, by the late eighties, the military was already deploying early neural networks and signal processing algorithms that could learn to identify the shape of a T-seventy-two tank versus a civilian tractor. They were using what we call connectionism, which is the foundational philosophy of modern AI, while the rest of the world was still playing Minesweeper.
It is fascinating because that is a second-order effect of the Cold War that we rarely talk about. We think about the space race, but the race for computer vision was happening in the shadows. And that leads into one of the other sectors Daniel mentioned, which is medical imaging. I remember us talking briefly in episode five hundred ninety-nine about how invisible AI has been for seventy years. In medicine, this isn't just experimental. It has been standard care for a long time. It is almost like we have been living in a world of invisible intelligence and we just didn't have a name for it that the public liked.
People go into a hospital today and see an AI tool and think it is brand new. But the Food and Drug Administration approved the first Computer-Aided Detection system, or CAD, for mammography way back in nineteen ninety-eight. Think about that for a second. Bill Clinton was in office, people were still using dial-up internet, and we already had machine learning algorithms assisting radiologists in spotting calcifications that the human eye might miss. That system, developed by a company called R-two Technology, was a pioneer. It wasn't just a set of rules; it was trained on thousands of images to recognize the probabilistic markers of cancer.
Nineteen ninety-eight. That is nearly thirty years ago. Why is there such a massive gap between that reality and the public perception? Is it just because those systems didn't talk back to us? They didn't have a personality? I feel like as humans, we only recognize intelligence if it mimics our own social behaviors.
I think that is a huge part of it. Those systems were narrow AI. They did one thing perfectly, or at least very reliably. They were tools, not partners. And because they were tucked away in the backrooms of radiology departments or inside the guidance systems of missiles, the average person never saw the probabilistic nature of the work. But the technical evolution there is what really matters. In the early days of medical CAD, it was a mix of hand-coded feature extraction and early neural weights. The system would be trained on thousands of known cases to recognize the specific textural patterns of a malignancy. It wasn't just following a rule; it was recognizing a pattern it had learned. And it had to be reliable. If a CAD system has too many false positives, radiologists start to ignore it. If it has too many false negatives, people die. The tuning of those models in the nineties was a masterclass in what we now call alignment and safety.
It strikes me that the common thread between the military and medicine is the need for high-reliability. You mentioned the military was looking at tanks versus tractors. In finance, which is the third big long hauler, the stakes are different but equally intense. I am thinking about fraud detection. Every time I swipe my credit card and get an instant text asking if it was me, that isn't a guy in a call center watching my account. It feels like magic, but it is actually a very old type of magic, isn't it?
No, it is a system called Falcon. Or at least, that was the pioneer. The Falcon fraud detection system was developed by a company called H-N-C Software in the early nineteen nineties. They were using neural networks to score credit card transactions for risk in real-time. By the mid-nineties, they were processing a huge percentage of all credit card transactions in the United States. If you used a card in nineteen ninety-five, your transaction was likely being evaluated by a neural network. And think about the hardware constraints then! They had to make a decision in milliseconds using the processing power of a toaster compared to what we have now. They had to be incredibly efficient with their code and their feature engineering.
That really puts the current hype in perspective. We act like we are living in this unprecedented revolution, but the financial backbone of the country has been running on neural nets for three decades. But here is my question, Herman. If these guys have been doing it for so long, why does it feel like everything changed recently? Is it just the scale of the compute, or has the actual approach shifted for these long haulers? Are they looking at the new kids on the block with LLMs and feeling like they missed something, or are they just laughing at us?
It is a bit of both, and this is where it gets really interesting for the sectors Daniel asked about. In the nineties and early two thousands, these sectors were using what we call discriminative models. Their job was to classify things. Is this a tank? Is this a tumor? Is this a fraudulent transaction? The current explosion we are seeing is in generative models. These models don't just classify; they create. But for a long hauler like the military or a medical lab, generative AI is actually a bit of a headache. It introduces a level of stochasticity—or randomness—that is terrifying in a high-stakes environment.
I can imagine. If you are a radiologist, you don't want the AI to generate what a tumor might look like. You want it to tell you with ninety-nine percent certainty if there is one actually there. There is a tension there, isn't there? Between the reliability of the old guard and the flexibility of the new tools. It is like trying to replace a reliable old tractor with a Ferrari that occasionally decides to drive into a lake because it thought the lake looked like a road.
There is a massive tension, and I call it the Hype Tax. Because AI is so high-profile now, these veteran sectors are being pressured to integrate modern Large Language Models or transformer-based architectures into systems that were already working fine. Take Customs and Border Protection, for example. They have been using predictive risk-scoring models for cargo screening for a long time. They look at manifests, shipping routes, and historical data to flag high-risk containers. It is a very effective, quiet use of machine learning that keeps the country safe without slowing down trade too much. It is based on the Automated Targeting System, or A-T-S, which has been evolving since the late nineties.
And I assume that aligns with the broader goal of national security and efficient border management. If you have a system that has been refined over twenty years to catch contraband or illegal shipments, what happens when a new administration or a new tech-savvy director says, we need to add a chatbot to this? Or we need to use a generative model to predict where the next threat is coming from?
That is exactly the problem. You end up with what we call legacy debt. You have these incredibly robust, thirty-year-old expert systems or early machine learning models that are highly interpretable. If the system flags a shipping container from a specific port, the officers know exactly why. It is because that port has a high correlation with specific types of illicit trade. But if you bolt on a modern, black-box transformer model, you might get slightly better predictive power, but you lose the explainability. In a legal and security context, like at the border or in a courtroom, you need to be able to explain why the machine made a certain decision. You can't just say, well, the weights in the hidden layers preferred this outcome. That doesn't hold up in a court of law or a diplomatic incident.
This is a point we have hit on before, but it bears repeating. Explainable AI, or X-A-I, isn't just a buzzword for these long haulers. It is a legal requirement. If the military uses an autonomous system that makes a mistake, there has to be a clear audit trail. You can't just say, well, the neural net had a hallucination. That is a non-starter when you are talking about kinetic force.
Precisely. And that is why these sectors are actually more cautious than the tech industry right now. You see companies like OpenAI or Google moving fast and breaking things, but the long haulers are looking at this and saying, wait a minute. We have been doing this since the eighties, and we know how many ways this can go wrong. If you look at the evolution of something like the Aegis Combat System on Navy ships, it has had automated engagement modes for decades. It can track and engage hundreds of targets simultaneously. But the humans in the loop have spent forty years refining the rules of engagement for that AI. You don't just swap that out for a trendy new model because it can write a poem or pass a bar exam. The military understands that the cost of a false positive in their world isn't a bad user experience; it is an international crisis.
It is a great point. It reminds me of the discussion we had in episode seven hundred ninety-one about the reality check of the hype cycle. These legacy sectors are already at the top of the S-curve in many ways. They have found the utility. They aren't looking for a reason to use AI; they have been using it to solve specific problems for a generation. So, does the current boom actually help them, or is it just noise? Is there any actual benefit to the long haulers from this massive influx of capital and attention?
It is a double-edged sword. On one hand, the current boom has led to a massive decrease in the cost of compute. The chips we have now, the H-one-hundreds and the newer Blackwell architectures that have dominated the last couple of years, allow these legacy sectors to train their specialized models much faster and on much larger datasets. For medical imaging, that is a godsend. We are moving from CAD systems that just flag a spot to systems that can provide a full differential diagnosis by comparing a scan against millions of others in seconds. That is a direct benefit of the hardware revolution fueled by the AI boom. We are seeing a leap in precision that was unthinkable in nineteen ninety-eight.
But the noise part? I imagine it is hard to hear yourself think when everyone is screaming about the end of the world or the dawn of a utopia.
The noise is the talent war and the distraction. If you are a top-tier machine learning engineer, are you going to go work on refining fraud detection algorithms for a bank, or are you going to go to a startup that is trying to build a digital girlfriend or a meme generator? The brain drain away from mission-critical, boring AI toward flashy, consumer AI is a real problem for the long haulers. And then there is the procurement side. Government agencies are being pitched AI solutions by every startup under the sun, and many of these startups don't understand the rigorous testing and validation that a sector like defense or medicine requires. They think if it works eighty percent of the time on a benchmark, it is ready for the field. The long haulers know that the last five percent of reliability is where ninety-nine percent of the work is.
I want to go back to the border and customs example for a second, because that feels very relevant to the current political climate and our worldview here. We talk a lot about the importance of technological leadership and security. If we have these quiet, effective systems, how do we make sure they stay ahead of the curve without falling into the trap of the hype? How do you modernize a system that is already the best in the world but is built on older foundations?
It comes down to what I call the boring AI advantage. The most impactful AI is often the most invisible. If you look at how the United States handles cargo security or passenger screening, it is a sophisticated web of pattern recognition. We are looking for anomalies. That is what machine learning is best at. The challenge is that as our adversaries get access to generative AI, they can use it to create better disguises or more complex smuggling routes. They can use AI to find the gaps in our detection models. So the long haulers at Customs and Border Protection have to use the new tools to simulate what the adversaries might do. It becomes a game of AI versus AI, but it is happening in the world of logistics and physical security, not on a computer screen. It is a Red Queen's race where you have to run as fast as you can just to stay in the same place.
That is a fascinating thought experiment. We usually think of AI versus AI in terms of cybersecurity or deepfakes. But you are talking about it in terms of the physical movement of goods and people. If an adversary uses AI to optimize a smuggling route to evade our current detection models, we need our own models to predict that shift. It is a constant arms race that started long before we were talking about Large Language Models. It is about the flow of atoms, not just bits.
It really did. And if you look at the finance sector, they have been in that arms race since the nineteen nineties. High-frequency trading is essentially an AI battle. You have algorithms competing to find micro-inefficiencies in the market and exploit them in milliseconds. People blame the twenty ten Flash Crash on these systems, but the reality is that those systems are what provide the liquidity that keeps our markets functioning. They are the ultimate long haulers. They moved past human intuition decades ago. In the world of quantitative finance, if you are still using human intuition to execute trades, you are already broke. They have been living in the post-AI world for twenty years.
So, looking at Daniel's question about the impact of the boom, it sounds like these sectors are in a weird position. They are the veterans who are suddenly being lectured by the rookies. I can imagine a researcher who has been working on military ATR since nineteen ninety-two being a bit annoyed when a twenty-two-year-old developer tells them they need to use a specific new framework that was released last Tuesday.
Oh, the saltiness in those departments is real, Corn. I have talked to people in the defense space who find the current conversation exhausting because it lacks historical context. They have already solved many of the problems people are just now discovering. For example, the problem of data poisoning. If you are training a model to recognize enemy vehicles, you have to worry about the enemy planting deceptive data in your training set—like painting specific patterns on their tanks to trick your neural net. The military has been studying this for forty years. Now, the tech world is suddenly worried about it because people are trying to mess with LLMs. The long haulers are sitting there saying, welcome to the party, we have been dealing with adversarial attacks since you were in diapers.
It is like we are reinventing the wheel, but the wheel is made of silicon and we are calling it something different every time. I want to shift a bit to the practical side of this. For our listeners who might be in these industries, or who are looking at how to apply these lessons to their own work, what can we learn from the long haulers? If these sectors have been doing this for thirty years, what is their secret to longevity? How do you keep an AI system relevant for three decades?
The first lesson is that reliability beats scale every single time in the real world. In the research world, we love to talk about how a model with a trillion parameters is better than one with five hundred billion. But in the operational world, a small, specialized model that you fully understand and that works ninety-nine point nine percent of the time is worth more than a massive, erratic model. The long haulers prioritize auditability. They don't just want the answer; they want to know how the machine got there. They use techniques like feature importance and sensitivity analysis to ensure the model isn't just picking up on noise in the data.
That is a great takeaway. It is about the difference between a toy and a tool. A toy can be unpredictable and still be fun. A tool has to work exactly the same way every time you pick it up. If my hammer occasionally turned into a screwdriver, I wouldn't call it advanced; I would call it broken.
And the second lesson is about the importance of domain expertise. The reason the Falcon system worked so well for fraud is because it wasn't just built by computer scientists. It was built by people who understood the mechanics of financial crime. They knew what patterns to look for. They knew that a transaction in a different city followed by a high-value purchase at a jewelry store was a classic fraud signature. Today, we see a lot of AI companies trying to solve problems in fields they don't understand, like healthcare or law, and they wonder why their models fail in the real world. The long haulers never forgot that the AI is an extension of the expert, not a replacement for them. The AI handles the scale, but the human provides the context.
That actually reminds me of episode eight hundred twenty-one where we talked about the pattern seekers and how certain cognitive profiles are drawn to these high-intelligence roles. In these long-hauler industries, the synergy between the human expert and the machine pattern-seeker is what creates the value. It isn't just about the algorithm. It is about the human who knows which questions to ask the algorithm. It is about that feedback loop where the expert corrects the machine, and the machine learns from the expert's intuition.
That is such a crucial point. If you look at the best radiologists today, they aren't the ones who are afraid of AI. They are the ones who use the AI as a second set of eyes. They know the AI is better at spotting tiny calcifications, but they are better at understanding the clinical context of the patient—the family history, the physical symptoms, the subtle things that don't show up on a scan. That partnership is what the long haulers have been refining for thirty years. It is a mature relationship, whereas the rest of the world is still in the honeymoon phase with AI, where everything looks amazing and we haven't realized the flaws yet. We are still in the stage where we think the AI can do everything, while the veterans know exactly what it can't do.
I think there is also a lesson here about legacy debt, which you mentioned earlier. Many organizations are sitting on what I would call hidden AI debt. They have these old systems running in the background that nobody really understands anymore because the people who wrote the code retired ten years ago. How do you audit that? How do you move forward without breaking the very foundation of your operations? It feels like a ticking time bomb in some of these older industries.
That is the big challenge for the next decade. We are going to see a massive wave of modernization where these thirty-year-old neural nets are finally being upgraded. The key is to do it incrementally. You don't just rip and replace. You use the old system as a benchmark. If the new, fancy transformer-based model can't beat the nineteen ninety-eight CAD system in a head-to-head trial on historical data, then the new model isn't ready. You have to respect the veterans. You have to understand why the old system worked before you try to build a new one. This is what we call the Ship of Theseus problem in software engineering—how much can you change before it is a different system, and how do you keep it sailing while you are swapping out the planks?
I love that. Respect the veterans. It applies to people and to software. It is easy to look at old code and think it is primitive, but that code has survived thirty years of real-world edge cases. It has been battle-tested in a way that no brand-new model can claim. It has seen the weird anomalies, the sensor failures, and the data glitches that happen in the real world, not just in a clean lab environment.
And that is why I am actually very bullish on the long haulers leading what we are calling the agentic revolution. We are moving toward AI agents that can actually take actions in the world, not just generate text. The military, finance, and logistics sectors are the ones who already have the infrastructure and the safety protocols for autonomous action. They have been doing it for years. While a tech company is trying to figure out if their agent should be allowed to book a flight without a human clicking okay, the military has already figured out the fail-safes for an autonomous drone or a missile defense system. They have the governance models that the rest of the world is still trying to draft.
That is a provocative thought. Instead of the startups disrupting the legacy players, the legacy players might actually be the ones who show the startups how to build agents that actually work in the real world. They have the data, they have the experience with high-stakes environments, and they have the institutional knowledge of what happens when things go wrong. They know how to build a kill-switch that actually works.
I think that is exactly what is going to happen. We are going to see a shift where the expertise moves back toward the domain-specific sectors. The general-purpose models will be the foundation, but the real power will stay with the long haulers who know how to prune and refine those models for specific, mission-critical tasks. It is an exciting time, but we have to keep our heads on straight and not get swept up in the idea that this is all brand new. We are building on a foundation that was laid when we were kids.
Well, this has been a really grounding conversation, Herman. I think it is important for our listeners to realize that the AI revolution isn't a sudden explosion; it is a slow-motion earthquake that has been building for decades. The sectors Daniel mentioned—military, medical, finance, and border security—are the ones who have been doing the hard, unsexy work of making this technology actually function when it matters most. They are the ones who turned the theory into reality while the rest of the world was still debating if computers were even useful.
It really is the history of the invisible. And if you are listening to this and you work in one of these legacy industries, don't feel like you are behind. You are probably sitting on decades of insights that the flashy AI startups are only just starting to realize they need. Reliability, auditability, and domain expertise—those are the true pillars of AI success. They aren't as exciting as a chatbot that can write a screenplay, but they are what keep the lights on and the world turning.
And as we look toward the future, I think we will see these veteran sectors continue to be the anchor that keeps the AI industry tethered to reality. Whether it is ensuring our borders are secure or that our medical diagnoses are accurate, the long haulers are the ones we rely on, even if we don't always see the work they are doing. They are the silent partners in our modern life.
Well said, Corn. It is a maturing utility, not just a new invention. And I think that is a much healthier way to look at the world. We aren't just at the beginning of something; we are in the middle of a very long, very important story. We are just the latest chapter in a book that started in the nineteen fifties.
So, for everyone listening, I hope this gives you a bit of a different perspective the next time you see a headline about the latest AI breakthrough. Ask yourself, how did the long haulers do this twenty years ago? What have we forgotten that they still know? There is so much wisdom in that history if we are willing to look for it. Don't be blinded by the newness; look for the continuity.
And if you want to dive deeper into that history, I really recommend checking out some of our older episodes. We have been tracking this for a long time ourselves. Episode five hundred ninety-nine is a great deep dive into the pre-ChatGPT era, and if you are curious about where the hype is headed next, episode seven hundred ninety-one is definitely worth a listen. We have covered everything from the early days of neural nets to the current transformer boom.
Yeah, we have a huge archive at myweirdprompts dot com. You can search for any of these topics, and it is a great way to see how our own thinking has evolved over the years. We have been at this for nearly a thousand episodes now, and the brothers are still learning every day. It is a journey we are all on together.
We really are. And hey, if you have been enjoying the show and you find these deep dives helpful, we would really appreciate it if you could leave us a review on your podcast app or on Spotify. It sounds like a small thing, but it genuinely helps the show reach new people who are looking for a more substantive conversation about tech and society. We want to reach the people who are tired of the surface-level hype and want to get into the gears of how things actually work.
It really does make a difference. We see every review, and we appreciate the feedback more than you know. It keeps us going, especially when we are diving into these long, technical histories. It lets us know that there is an audience for the deep, unsexy truth about technology.
Definitely. And thanks again to Daniel for sending this one in. It was a great excuse to get back into the archives and look at the real foundations of the technology we talk about every week. It reminds us that we are standing on the shoulders of giants.
Truly. Well, I think that is a good place to wrap it up for today. This has been My Weird Prompts. I am Corn Poppleberry.
And I am Herman Poppleberry. Thanks for joining us, and we will catch you in the next one.
Until next time, stay curious and keep looking for the patterns beneath the surface. Bye everyone.
