Cowards and Criminals: How the Felonious Behavior of Cyber Underwriters Caused the Ransomware Crisis

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A chapter from my upcoming book examines cyber security and how AI impacts risks. AI does not substantive change the cyber security equation. It does require that we accept the reality of cyber security.

Every single American is owed one thing by every single publicly traded company: They must conduct themselves in a law abiding manner. No company has the right to engage in criminal activities. Any reasonable examination of ransomware insurance reveals the normalization of felonious activities.

Cyber security is a topic that has come up frequently in the discussion of artificial intelligence. Cyber security, like artificial intelligence, is a very poorly understood subject. It is something that, like AI, has created a great deal of banter from non-experts about doom scenarios.  What makes cyber security different is that the lack of understanding is not related to any inherent complexity or unfamiliarity. Cyber security is one of the most straightforward pursuits. The confusion around it is completely artificial.

The primary way in which AI changes the cyber security threat landscape is that it enables malicious actors to automate attacks in new ways and it removes previous barriers to hacking. Many attackers are located in foreign countries, but AI removes the language barrier. Attackers may need to survey many systems to find a weakness, but AI can do that automatically. Finding bugs in software code is a task that requires expert human review, but AI can find bugs without needing an expert.

AI doesn’t only speed up and automate cyber-attacks. It makes new forms of cyber attack possible.  With AI it’s possible to create more diverse code than could be generated with human programmers. AI allows for exploits to be discovered and taken advantage of in ways that can’t be easily remedied. It can perform the kind of deep analysis that would be impractical with only humans.

One of the most important aspects of modern AI is its ability to process conversational, written and human communications. It does not only interface with technical systems and commands. AI can parse huge amounts of copy, looking for useful information about targets.  It can be used to aid in open-source intelligence. It can also be used in direct social engineering attacks, creating phishing messages and even engaging in two-way communications with end users, completely on its own. It also complicates identity due to deep fakes and voice cloning.

There is another side to this. AI can also be used to enhance cyber security, and there are many AI cyber products on the market. Of course, part of this is branding, but there is an enormous potential to improve cyber security defenses, responses and evaluation.

Some have said that AI is the end of cyber security, as we know it. That may very well be true. That’s not necessarily a bad thing either. Cyber security, as it now exists, is barely economically sustainable.  The levels of economic loss from cyber security incidents are staggering. The problem is that, if cyber security losses increase substantially, they will begin to cross the threshold of being truly economically unsustainable.

Cybercrime already carries an enormous economic cost. Cybersecurity Ventures projected worldwide cybercrime costs of $10.5 trillion in 2025, including stolen money, lost productivity, business disruption, intellectual property theft and the expense of recovering from attacks. The FBI’s Internet Crime Complaint Center received more than one million complaints in 2025, reporting $20.9 billion in losses, an increase of 26 percent over the previous year.1 These reported losses do not capture the full economic damage, including many costs associated with interrupted operations and recovery.

The amount of money lost to cybercrime is staggering. It’s a huge invisible tax we all pay. Cyber-attacks have cost so much, at more than ten trillion a year, that they have become a driving force of inflation. The cost is redistributed in the form of insurance premiums, taxes, interest rates and other economic losses.  That’s why it is not easily visible. Hospitals are under constant cyber-attack, something that would not have been tolerated at one time. The cost to healthcare has been enormous. It is one reason why healthcare inflation is so hard to keep in check.

Cyber-attacks are driven primarily by ransomware.  Ransomware has hit thousands of small businesses.  It has compromised the information of millions of Americans. It has taken out large companies, costing billions, as well as small towns and emergency services. Worst of all, ransomware is constantly being used to attack the American hospital system. In 2024, Change Healthcare, part of United Healthcare was hacked by a ransomware gang and taken offline for more than a month.2  Millions of Americans could not get their prescriptions.  Clinics went unpaid, and financial losses measured in the hundreds of billions.

Cyber security does not just protect companies from losing money. It is also a vital part of national defence. Cyber-attacks can compromise secrets, shut down systems and be used in hybrid attacks against infrastructure. Concerns have been raised over water systems and other vital infrastructure.3

Current losses are almost unsustainable as is. If AI based cyber-attacks raise the losses much higher, then the system may begin to buckle under the losses.

Surprisingly, and contrary to popular belief, it’s not at all inevitable that cyber losses be high. The current situation, with all its trillions in losses, is entirely artificial and could be fixed with policy changes. AI may finally drive the change.

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Book Chapter on AGI

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If you happen to visit this site from time to time, you probably will have noticed that I have not been posting many updates. There’s a reason for that: I have been primarily focused on writing my book about AI. It has been a few months, but writing a non-fiction book is extremely difficult. In fact, almost everyone who starts writing a serious non-fiction book later will say that they underestimated how difficult and long it would be. I figured I could handle it, because I’m a good writer. Like everyone else, I underestimated how hard it would be.

The book I am writing has a sense of urgency to it, because as AI is being adopted so widely, we are seeing unprecedented misinformation and mythology being presented in the media and taken seriously by adults, who truly should know better.

What is far worse is the AI fear movement (AI Doom) which has become one of the most ridiculously unhinged parts of the effort pushing myths and stupidity about AI. Of course, machines do not have desires, do not act on their own accord and are not capable of being gods. Superintelligence is a fictional construct that does not even make sense once you start to ask serious questions about what it means. Intelligence in and of itself is rarely a trump card when someone else has resources or knowledge you do not. LLMs are linear and have inherent limitations.

The idea that machines will gain bad intentions is complete nonsense, and yet we see increasingly unhinged and dangerous rhetoric saying the opposite. Take this Youtube video I came across recently, The braindead nature of AI doom isn’t really what we have to fear. If people truly believe that the human race is in danger, is violence even unreasonable?

Yudkowsky, a junior high dropout who has no background in almost anything, and is one of the most celebrated profits of the cult of doom has made statements about bombing data centers. A splinter group of AI doom has already committed murders. Sam Altman’s house was shot at, and a Molotov cocktail thrown at it. A mentally disturbed AI protestor went missing causing major concerns by police. None of this is surprising at all.

And then I see yet another post, this time on YouTube, celebrating a terrorist who had an unhinged fear of technology driven by literal schizophrenia.

And this is why I am writing the book. It’s an urgent plea for sanity, and an attempt to dispel myths about AI and provide all the supporting evidence so that people can truly understand how AI works and what it is and is not.

AGI (Whatever the hell that is) is a major topic of confusion and misinformation, so I have decided to present my core chapter on AGI. Bear in mind that this chapter does not really stand on its own. By the time a reader gets to this chapter (the 14th) they will have fully learned about: The history of AI, machine learning, deep learning networks, neural network dynamics, how training works, the transformer architecture, vector embeddings, attention mechanisms, catastrophic forgetting, neural compression, scaling laws and alike. If it seems like “This point assumes the audience already knows..” that is likely because it is the 14th chapter.

This is presented as is. It is a draft. It will almost surely be very refined and revised before the book is finalized. Also, keep in mind that, as an early draft, it is likely to have a few typographical or formatting errors, sub-optimal wording and similar. Normally, I might hesitate to make public such a work in progress. However, with the increasing nonsense and dangerous degeneration of understanding about what AI is and how it works, I have decided to try to get as much out as soon as possible.

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The “Protect Our Games Act” is the Pinnacle of First World Problems

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Imaging this situation: You purchased a video game and really enjoy it. You may even invest something in becoming a great player. Then, suddenly, the game’s developer shuts it down. In the past, you could probably play it offline or on private multiplayer sessions, but increasingly, games are cloud-based and require backend infrastructure to run at all.

That can be a huge bummer. A massive one. It’s like many things in digital life. Stuff gets sunset. A lot of people were sad to see AIM die, and there were certainly people who still relied on it. Perhaps you were one of the few who really loved the Metaverse. Now it’s being shut down too. The same happened to a huge number of other services. GeoCities, MSN messenger, Skype and plenty of others are gone.

There is a legitimate question is to whether providers owe their end users some level of continued support, or at least providing some kind of open source or third party option for support of deprecated software and IT infrastructure. There is already a mechanism to enforce this, for those who take it seriously: contractual obligations for ongoing support or graceful sunsetting and be built into agreements, but rarely are.

But where does that leave video games?

As mentioned, this can absolutely happen with video games, but that would seem to be one the least concerning examples. After all, video game developers can’t really be expected to provide the support for old games forever. They are private organizations and they exist for entertainment purposes. They may well find that old game infrastructure operates at a net loss and cannibalizes new game sales. There are also times that a game just is not that successful. Even with a few dedicated fans, many games just fail in the market.

There’s also the issue of video games as a form of artistic expression. If the game developer really has created something in their vision, there’s an argument that they should maintain creative control over its destiny, even choosing to shut it down when it is still popular, the way that Jerry Seinfeld took his show off air before it began to fade.

Well California feels differently, and I have to call this what it is: the most first world problem I have ever seen in my life. Because, while we can go around in circles endlessly, debating whether it’s not fair to the consumer or whether the producer should have greater rights, one thing is undeniable: these are video games.

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A Full List of Risks of AI to Society

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AI is a big thing, a huge force in the economy and basically the top story in tech since 2022. It promises to have major impacts on society and technology, and it already has. With this, of course, come risks. Every new technology or change in a society has ups and downs. This is no different with AI. With new capability comes the potential for misuse or unexpected failure modes.

This has happened many times when technology was deployed, with great enthusiasm and little concern for risks and controls. The problem with AI, unfortunately, is that discussion of AI risk quickly descends into ridiculous philosophical banter, in which people who have no idea how the technology works try to appoint themselves experts and then dominate with riduclous concerns.

The “AI Risk” and “AI Safety” community are dominated by people who bought into the ramblings of doom grifters with books and Ted talks to sell. This is a problem, because there are real risks and risks that should be considered. Rarely do the adults in the room get to have the conversation. AI has no intentions, it is flawed and imperfect, and the idea of superintelligence is flawed. And yet, the real danger is that this will crowd out discussion of the issues that are legitimate risks.

Here is a comprehensive taxonomy of what the risks are to society of the deployment of AI at scale. This does not include the internal risks to organizations of AI failure. Discussing how models fail is another important area, but it’s a different topic.

I am sure that some will disagree about how severe the risks are and if I am downplaying them. For one thing, I started thinking AI would result in mass unemployment, but after looking at the situation and the model capabilities, I was surprised to find that I found that conclusion is not supported by evidence. This is probably the one area people will disagree with the most.

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Preview of Another Book Chapter

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I have decided to publish a second rough draft of a book chapter. One of my primary reasons is to get the content out as soon as possible, since my motivation is to help with the extreme misinformation being circulated about AI.

The idiotic “doom” movement continues to operate with seeming credibility despite the childish message that superintelligence will take over the world, like some kind of cartoon villain. Unfortunately, refuting this is hard, because adherents have a completely wrong understanding of how the technology works.

It is clear that to resist this lunacy, along with the equally stupid idea that models might be conscious or have feelings, you simply need to know how this actually works. That’s what “Understanding AI” is all about. That’s why I’m writing the book. It’s a decidedly not dumbed down primer on AI. It explains the theory, early evolution, why things are done as they are, neural networks, deep learning, natural language processing and generative AI.

This unapologetic adult and broad primer will help make anyone immune to the extreme cultish nonsense that has surround the subject. It does not go into depth more than it needs to, but it assures all relevant concepts are covered in a computer way that does not insult the readers intelligence.

This is not the second chapter of the book, but rather the 7th. It is, of course, subject to change, as it is a draft. But, putting it out there, if nothing else, holds my feet to the fire, to get it done. I’m sure it will receive at least three major revisions, but so far I have not seen such a comprehensive account of the dawn of generative AI from anyone inside the industry.

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AGI: Cutting Through The Confusion

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AGI, ASI and the extreme confusion of it all

There has recently been a huge amount of confusion over the concept of Artificial General Intelligence, AGI, and exactly what it means, whether it is something that should be expected, and what it means for society.  One thing that is seen frequently is speculation of the “Race to AGI” or questions like “How will we know when we have AGI” or “What if they already have AGI and haven’t told anyone?”

This whole line of reasoning, the way it is framed, and the questions being asked here indicate complete incoherence about what AGI or Artificial General Intelligence is, or at least what it is supposed to be. If that is not bad enough we now are being told that AI is close to “super intelligence” or “ASI.” This is an entirely fictional idea, and nobody can even agree as to what it is, other than it might be scary.

The Basic Idea of AGI

The concept of artificial intelligence in the form of a fictionalized “thinking machine” goes back centuries.  The modern concept of computer systems that simulate intelligent behavior dates to the 1950’s.  As systems dubbed AI were developed, it was clear that they were relatively narrow and bounded in what they could do.  Machine learning and cognitive simulations could optimize systems and respond to variables, but they lacked the kind of “intelligence” that we think of in a human.

Intuitively, it was always clear that there existed a higher level of “general intelligence” of the type found in humans and other thinking beings.  In the simplest sense, an AI that could be communicated with, like a person and could understand human-like concepts, like situations being subjectively better or worse.  It made perfect sense that the mental model for what general intelligence would look like would be a synthetic human mind.

The terms for Artificial General Intelligence versus Narrow Artificial Intelligence was coined in 2007, but the basic concept goes back much further.  It had been often called “strong AI,” “human-like AI,” “full AI,” or “true AI.”   In fact, this distinction became obvious early in the field of AI, when it was clear that systems that could mimic certain aspects of human intelligence were distinct from the popular nation of a fully digital mind, or anything like human level capabilities across domains.

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Restaurant Automation: The Bad Idea That Won’t Die

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While researching the wild claims that everyone is going to lose their job forever, because of AI, I came across a claim that struck me as strange. A large number of organizations, which claim to help understand potential job displacement have indicated food service and restaurant automation would soon be coming to take the jobs of cooks, waitstaff and everyone else who works in the sector. In fact, some have predicted that within a year we may lose 80% of jobs in the restaurant sector to robots.

I was pretty stunned. A large number of commentators, for many years, have smugly preached that the waitstaff, cooks, chefs and everyone else who works in the sector were in a terrible position, where their jobs were about to go away for good. Really?

The restaurant sector has been on of the most resistant to automation, and for obvious reasons: it’s a messy, irregular, fast moving business that operates favorably with human labor economics and seems to be purpose built to be hostile to automation. Still, this idea is not new at all.

In fact, restaurants have been the subject of nearly constant efforts to automate processes. There is really nothing at all modern about the idea. Fully automated kitchens and restaurant that deliver food by motorized cart or conveyor have been a staple at Worlds Fairs for decades and other novelty settings. The concept is rehashed every few years and with it comes the predictions of the robotic takeover of food service work. Of course, this never happens.

From the numerous attempts to replace the service staff in restaurants, which many engineers have spent much time on, since at least the 1930s, and the ongoing hype, this presents an interesting case study in an attempt to automate jobs that really have no rational reason to be automated. What is clear here is that there are many roles that technically can be automated, but are far worse off by automating and have little to no economic incentive to do so. It’s an important lesson to keep in mind with the “AI will take everyone’s job” rhetoric that has been making the rounds.

Here is a video on the topic from 1966

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First Draft of First Chapter of My Book “Understanding AI”

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So I have decided to write a book. Crazy, perhaps. It is something I have wanted to do for years and faced many false starts. I think a lot of people have been through that. It’s not easy, but I knew that. It’s actually harder than you’d ever imagine, at least for me, but then again, this is the first time I have truly committed to the pursuit.

The reason I choose to write this is that AI is the most poorly understood topic I have ever seen, and has been wrapped in mythology. This is especially true with “AI Doom.” The problem with these beliefs is that they are difficult to refute without teaching a full lesson on AI. So, I guess that’s what I’m going to have to do! I’m not complaining, however. The fact is, there is really no in-depth guide for smart people who want to truly understand the concept of artificial intelligence.

I want to be clear that this is a first draft of the first chapter. It will be revised, probably several times. It currently lacks citations, but those will be added. Citations will be minimal because this is not a scholarly work, but should still have basic source credibility. Additionally, this chapter contains assertions which some might call bold or unsupported. There’s a reason for that and the reason is that this is just the first chapter.

Subsequent chapters will explore the history of AI as a concept and technology, including the pre-history and how formal logic rules, early computers and language itself set the stage for a new form of data processing, which we call artificial intelligence. It goes on to explain the theory of deep learning and neural networks, why this approach won out, and will take readers all the way to understanding natural language processing.

It’s not designed to be easy or dumbed down. However, it is intended to be fully complete for any lay person to fully understand why AI is the way it is, how we got her and where it is likely to go next. It is also not entirely technical. It features historic concept, public perception, regulatory issues and economic realities.

I also want to add that there are a number of people out there who are writing hot takes on AI that are completely uninformed, and this is intended to be the opposite of that. It’s down to earth, skeptical and presents the truth about artificial intelligence.

The working title is “Understanding AI.”


AND HERE IT IS!

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The Problem of False Expertise in AI

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There is something worse than ignorance, and that is false expertise. This happens in all fiends, but it’s worse in fields that are poorly understood and those which are hot and popular in the press. Of course, nothing is hotter than AI and few things are more poorly understood.

There are quite a few people out there calling themselves experts. There are legitimately not that many people out there who understand AI, and even fewer of them are known for public speaking. Yuval Noah Harari is an example of this. He’s an historian and best selling author.

But does that mean he knows AI? No. No, it does noit.

Should Chatbots Refuse to Give High Risk Advice?

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Chatbots are becoming increasingly popular. ChatGPT, for example, has nearly a billion weekly users. These LLM based services are used for all kinds of things, including many things their initial developers never dreamed of: planning, brainstorming, writing, translation, companionship, functional play, humor, studying, reformatting text, creating code. People also ask chatbots for all kinds of advice and facts. Chatbots have become the goto answer engines for questions ranging from “What is the capital of Chad?” to “How long should I boil a lobster for?”

However, there is a problem with LLMs and that is that they “hallucinate.” The term hallucination is a bit of a misnomer, because what is actually happening has less to do with figments of the imagination and more to do with patterns and probabilities. What actually happens is that the LLM confabulates a response that fits the patterns of a valid response but is not factually accurate. This often happens due to the model lacking information on a topic, but it can happen even when the model does have the knowledge in its training data.

No, it is not this kind of hallucination…

Hallucinations are impossible to completely eliminate from large language models. They are as much a feature as a bug, because the ability to create false information is inseparable from the model’s ability to generate fiction and hypotheticals or engage in role playing. It’s the nature of LLMs as stochastic probability engines. The only real way to eliminate hallucinations is to have some sort of output pipeline that involves checking and verification of outputs. That’s not something that chatbots currently do.

This is well understood and documented, but that does not change the fact that hallucinations continue to slip past people and be believed. A number of high profile events have included false citations in scientific journals, fake caselaw presented in court and medical advice for diseases that don’t even exist. One of the problems here is that people tend to believe the results a computer gives them, because in the past computers have been reliable and deterministic.

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