“The real problem is not whether machines think but whether men do.” – B. F. Skinner

You are smarter than AI.

You are more creative than AI.

AI is faster than you and can digest more information.

AI is a tool.

“With great power there must also come great responsibility.” – Stan Lee

You are responsible for determining whether AI results are correct and fit for purpose. If and how any of these results find their way into a finished product is your decision. Your intelligence, energy, and creativity must be harnessed to determine any ends. Anything less is human malpractice.

This process is known as verification and validation. It is your superpower in this new age of AI.

“If you’re nothing without this suit, then you shouldn’t have it.” – Tony Stark

Tony Stark’s Iron Man armor is a good analogy for all this. When Tony Stark puts on the Iron Man armor, what happens in that armor is still his responsibility. He is still in charge. He is still the one completing the action. He is in control.

With AI, you are granted a different kind of power in an intellectual sense. Your suit of intellectual armor is something you are responsible for. You must use it in a way you can stand behind. If you want to be a superhero and use this superpower, you are the one who must be responsible for what it does. Doing that is a skill. It t is a skill I am working hard to develop better. I believe it should be part of the development of everyone using AI. This is a society-wide project.

I submit that verification/validation, as a skill, is the equivalent of your suit of armor. Practicing it when working with AI is the path toward success. It is also the path toward responsible use. It is the path toward getting the best out of these AI platforms. Step one is looking at each query as one in a series of tests. Reacting to each through iterative refinement of what I had asked is always necessary. In almost every case an essential step to getting the best results.

“Essentially, all models are wrong, but some are useful.” – George E. P. Box

Our experience with verification/validation is not new. It was born in a closely related area of science and engineering. The practice of verification and validation started under similar circumstances for computational simulation. It is a practice in which the scientific method is harnessed to produce evidence that a simulation is correct.

What AI shares with simulation is the speed and volume of computation done with modern computers. In both cases, they vastly exceed anything humans can comprehend. Just because things are fast and involve many computations does not mean they’re correct. The whole verification and validation mindset is structured to test correctness. Discover how true it is and the evidence of that truth.

Just as in AI, the vast amount of computing does not equal correctness. Correctness must be proved with serious evidence. That evidence can then guide decisions.

The advent of AI as a productivity tool has created a need to restructure the way we work. It also demands a restructuring of how we teach and how we learn. One benefit of AI might be making work look more like school. That would be a very good thing. The impact on the workplace and the educational system is profound. They are also closely aligned.

“Trust, but verify.” – Ronald Reagan

As the Borg say, “Resistance is futile.” This AI genie is not going back in the bottle. It needs to be developed for the good of humanity. Not the good of a few humans. We need to learn how to use this technology effectively. If we’re educating young people, their success in the workplace is predicated on mastering this technology. The more it’s mastered, the more it’s used, the more successful individuals will be.

What I have found is that the concepts of verification and validation have grown in importance. Any result AI comes up with, whether it’s a body of writing, a literature search, or coding work, requires testing and verification. This actually starts with the mindset you approach the use of software.

The foundational thought is “this is probably wrong. I just need to find out where.”

One needs to approach anything it produces with doubts. Doubt the output and look for things that are not sensible or expected. There are techniques for this that we need to teach people, and their use needs to be expected. It’s a necessary part of the educational reform needed to meet the moment. One way to do this is to outfit the output with many tests. This should always include tests you know the answer to. If AI can’t give you the answer you already know, you can’t trust it.

This is the verification part of the work.

This assumes that you asked the question or request properly. One of the things to recognize is that there are different failure modes. Most people think the failures are all in the output. Garbage in. Garbage out. One more important mode of failure is an inadequate prompt. You were not clear. You were not specific. You were misunderstood. Often, the prompt is flawed and needs to be refined. Corrected and given more detail in order to get the right answer. So, correct yourself and try again.

Then there is the case of hallucinations or outright mistakes. These require other steps. For instance, when code is being produced, demand unit tests for each capability (to establish the adequacy and dependability of what’s produced). Check those test cases! There’s also spot-checking outputs, making sure references actually exist, and a host of other checks that AI can help with.

The key is always to approach the output from an AI platform with an element of doubt. Work steadfastly to remove those doubts. Go again looking for anything you missed. Only when you feel sufficiently confident, move forward.

Given how weak the actual practice of verification and validation is, there’s plenty of reason for concern. V&V is often the first thing to go in computational simulation. With AI, this will be calamitous. In many areas of endeavor, the entire workforce will be using AI to do their work. We need to think about how to do this in an effective, efficient, and safe manner. If we don’t develop these skills, we’ll feel very productive, but that productivity will be an illusion.

The ability to test AI starts with your prompt. Is your prompt well understood? Is it clear, and does it get the response you were expecting? Sometimes that’s the core of your problem. A second way prompting is essential is whether you outfit the response with tests that get at the legitimacy of the output. Do you actually build the sort of strong testing environment that lets you check whether the results you get are good? This is one of the key things to do.

Part of good prompting is being well equipped with an understanding of the topic you’re prompting about.

This is where education comes in. The education of students needs to include the basics, an understanding of how to get to the basics. Additionally, a refined sense of where the gaps in understanding are. What is foundational knowledge, and where is that knowledge a little fuzzy? Finally, how do you test knowledge? How do you test what you’re doing, and how do you make sure you’re on the right track? These form the foundation of any product. The product will go further than the foundation, but must include it..

The key is repeated prompting and correction. The corrections are born from the doubt and reflection on the output. The whole cycle is driven by not being satisfied with the result until it is “perfect”. The first prompt is rarely sufficient for any complex task.

Using more than one LLM is another path to success. This might fill the role of code comparison in simulation. Where answers are most interesting is where they differ. You have some evidence of correctness where they agree. I did not say correct, both could be wrong. Still, the differences in the answers are where to focus initially.

Recently, I asked Muse to recreate what I asked Claude to do. This was creating a modern Von Neumann-Richtmyer Lagrangian code. Claude had done an outstanding job and matched what I had once done by conventional means. Muse messed it up. The answers were different. The numbers were off due to errors. Then I looked at the answers; they had oscillations that were unexpected. It turned out Muse had left out some of the stabilizing artificial viscosity. Things were almost identical with this. Things are still a little different, but I haven’t figured it out yet.

That said, if Muse had done this a year ago, I’d be writing about it.

The validation part with AI is a bit less structured. This is just like simulation. Validation is comparing the new information or results against reality. That reality depends greatly on what the prompt is doing. One place it is easy is in checking references. New references should be confirmed. Hopefully, lay eyes on the actual print. You cannot test all the new information. You need to look to confirm some of what it produces. The more you confirm, the more confidence you can have. Each confirmation is that evidence.

“There is surely nothing quite so useless as doing with great efficiency what should not be done at all.” – Peter Drucker

“Scientific progress on a broad front results from the free play of free intellects, working on subjects of their own choice, in the manner dictated by their curiosity for exploration of the unknown.” – Vannevar Bush

We need to talk about how misaligned incentives have eroded the quality of the Labs. I will discuss the important role these labs have for the nation, its short- and long-term security, and how to fix the problems. At this moment of extreme change and danger, the lack of high-performing federal Labs is dangerous. They exemplify the decline in American science.

Before I get to the bigger, deeper issues, I’d like to recount an episode that shows how far the moral and ethical behavior that’s accepted at the labs has fallen. This was one of the most abhorrent episodes while I was at Sandia. This gets to some deep issues with the mindset of people in parts of the lab.

I had a student who was quite good. He has his PhD now and is a staff member. He was working on his degree at Texas A&M University. His advisor at the time was an Iranian citizen who had lived in the United States for quite some time. He is a citizen now.

“Ignorance, allied with power, is the most ferocious enemy justice can have.” – James Baldwin

Ar required, the counterintelligence part of the lab demands that we elaborate on any external collaborations we have with foreign nationals. I complied. When I looked within the database for the information to make sure it was correct, I noticed that the line item associated with this professor was labeled as “terrorist.” In other words, the professor was labeled as a terrorist. I found this alarming and investigated. It turned out to simply be a general label for Iranians, not a statement about the person.

This only made the label worse.

I found this to be a genuinely appalling lack of judgment on the part of the counterintelligence people. I called it out, and they declined to address the issue, saying that it was too difficult to fix within their database. This should have been a three-alarm fire for the counterintelligence office. Nothing the government funds should ever use this kind of language or slur to describe anyone. Can you imagine what would happen if they took someone who came from an African country and labeled them with the N-word?

I will just note that the label “terrorist,” when given to anyone of Arabic descent, is a racial slur these days. The lack of any sort of sense of this on the part of people at the national laboratory was one of the grossest and most disgusting things I had seen in my entire career. As far as I know, this practice persists, and they still label people of Iranian descent as terrorists. I don’t know whether this applies to other nationalities, but it is an appalling practice. Full stop.

This repulsive example is just the tip of the iceberg of fucked up things ruining the Labs.

“The prospect of domination of the nation’s scholars by Federal employment, project allocations, and the power of money is ever present and is gravely to be regarded.” – Dwight D. Eisenhower

For all the talk of alignment with AI models, the thing we rarely talk about is the misalignment of incentives for corporations and institutions. Increasingly, corporations, for all their wealth, are viewed as the model for institutions. I saw this at the labs, where corporate governance became the way to do things. In the process, the incentives totally aligned towards money and profit, and power became aligned similarly at the labs. This has led to destruction of capability and incompetence.

“The best minds of my generation are thinking about how to make people click ads. That sucks.” – Jeff Hammerbacher

One of the big problems is the advent of the gold standard science from the Trump administration. They are bootstrapping off of genuine problems in the scientific community to make things worse. They are pushing forward an ideological project that is undermining science. I would not mince words on how much the national labs declined during my tenure. There are real problems, and those problems are huge. Nothing the current administration has done addresses those problems directly or makes things better.

They are only making things far worse.

The Lab’s missions are entirely different from corporations. I would argue the alignment of incentives at corporations is similarly misaligned. Societal good and the well-being of the people in society are not even considered. They often suffer under the decisions that are most beneficial for profit. We’ve seen this unfold decade after decade, most acutely with the nature of social media. Their software is engineered to get humans’ attention because that attention is a source of money. This engineering is done without regard for the damage that it does to society as a whole or the individuals in it. Children are particularly vulnerable to this.

Now we have AI that is being written by these corporations.

Somehow we think that the misalignment of the corporations won’t seep into the alignment of these models. They have the power to do even more damage than the social media software has already done. This is absolute madness!

For a long time, the labs have suffered under the governance of the NNSA and the Department of Energy. The labs have become completely allergic to anything that looks like failure. Without failure, you also sacrifice success. This is particularly true in safety, but it’s poisoned research as well. There is this element of fear and hostility to any risk-taking that has led to a decline in everything the lab does. This is the pre-existing condition the current administration inherited. They have done nothing to address it.

Part of the reason for this is the inability of the agency managing the labs to accept failure in a way that is positive. They make little or no attempt to ever cast the failure as an opportunity to learn. It is an opportunity to punish. Anyone who screws up infects everyone with punishment. Every failure just generates more bureaucracy and oversight, and it creates a vicious, self-perpetuating cycle of decline.

“A ship in harbor is safe, but that is not what ships are built for.” – John A. Shedd

I watched this happen for virtually my entire career. I saw a wonderful, generous environment at Los Alamos savaged and destroyed. There, everything good was destroyed along with the problems being wrapped in red tape. They took a hatchet to the Lab where precision surgery was needed. Sandia already had its issues when I arrived with its corporate takeover in 1994. Those issues have gotten worse and worse over those 20 years. This is twinned with the priority given to money. By the time I left, the only thing that really got managed was money. The science, the engineering, the technology were not managed, except for reduction of risk.

AI is a great example of how this manifests itself. For starters, the labs have lost their supremacy as a source of superior scientific and technical knowledge for the nation. AI is this in spades. Private companies have all the expertise, and the labs are playing catch-up. Furthermore, the labs can’t take risks and do the research necessary to close the gap. In fact, the gap is just widening. The nation needs their expertise, and needs it for national security. They need to weigh in on the big subjects of our day. Today, the Labs are impotent.

I’ll get more of this in terms of how cowardly and weak the lab’s leadership is. They have capitulated to the Orwellian demands of the administration. This includes a new version of political correctness in terms of terms that can be used. First, the terms related to climate change were outlawed. No longer could researchers write their results freely. They were censored. Now they have outlawed “artificial intelligence”. It has been replaced by the President’s edict of “super intelligence”. This edict is pure propaganda and completely un-American.

All this does is undermine the integrity of the lab’s leadership and show how weak and craven they are. All they care about is funding. They have no moral certitude or virtue to lean on. In their capitulation, they’ve shown how unfit they are for leadership. They should have lost everyone’s respect.

It is the same thing that is poisoning the law firms that simply collapsed under the pressure from the administration. The ones who gave in to everything based on their threats, threats that were illegal. It violates freedom of speech, and it basically implements a form of chosen stupidity as policy. This simply accelerates the decline of the Labs.

Another example where safetyism has completely destroyed important national capabilities is launch to orbit and the satellite business. The obsession with safety and fear of failure means that NASA is no longer an organization that can lead in space. All our launch capability has been given over to business interests such as SpaceX or Bezos’s Space Launch Enterprise. This is completely unnecessary. Rather than empower and fix NASA, the government just gave up. The key is that NASA should be allowed to take risks and fail, just in the same manner that SpaceX does. Just as NASA used to do. The only reason it doesn’t is the attitudes that we have in government, which hamstring these agencies.

What I can say is that the NNSA is going the same way NASA went. It is in decline, and the way we have governed these institutions is destroying essential national capabilities. It may already be too late. The labs may already be in such a horrendous shape that they are just as incapable of executing their mission as NASA is capable of executing its mission.

“Failure is only the opportunity more intelligently to begin again.” – Henry Ford

I can start with one of my last experiences at the lab around AI. This was the nature of the chat LLMs the labs could access compared to what was accessible outside the labs. Invariably, when I tested the various chat capabilities at Sandia, the internal chat (which we should have had a free, unlimited license to use) was dramatically inferior to any of the LLMs on the outside in free mode. I was working at a premier National organization that had lobotomized these tools on purpose.

It’s my understanding that this imbalance has persisted. The whole reason this is occurring is fear: the lab’s fear of information getting leaked, their information control protocols being violated, and a host of other concerns. The concerns are very legitimate, but the fear of all this means that the labs are using dramatically inferior products to do seemingly essential research. In the end, the fear is completely undermining what ought to be essential work for the nation. AI is such an enormous transition and opportunity that the fear will have long-lasting, profound consequences.

The opportunities that I have described with AI are largely unavailable to the people in the lab. Thus, the productivity, the ability to explore ideas, and the basic capability are lost to them. Unless something changes, the labs will always be significantly behind the cutting edge. In a year, they may have capabilities that are equivalent to what I have now. By then, what I have now will be a pittance to what is available then.

“Political language… is designed to make lies sound truthful and murder respectable, and to give an appearance of solidity to pure wind.” – George Orwell

How can we fix this utterly, completely fucked-up situation?

The key is to start trusting people again. The labs need to be trusted. The people running the labs need to start looking at failures and problems differently. Change the mindset from thinking that these are not incompetent or malicious, but rather failures: things that can be fixed and learned from. They are not reasons to mete out punishments every time something goes wrong.

The key is to actually fail and learn from those failures and create better, more effective, more efficient systems. Every bad incident, every bad thing that happens at the lab, whether it’s a safety problem such as a fire or a security incident, is viewed as an apocalyptic event. It simply puts more bureaucratic measures onto the lab and increases the fear and the reticence of the lab’s management to do things. The government and public need to react differently. Bureaucracy is never the answer, and less of it is better.

Over the span of my career, this became an unremitting and corrosive influence on the labs. The labs are much less capable and effective places than they were. They are worse engineering and scientific institutions. It is all because of a lack of trust and the punishments and policies imposed by the governance. To be blunt, the current governance of the labs is a failure. The current management of the labs is a failure. This failure is something we have created and continue to amplify. It’s time to change course.

The ideology that they are promoting is unscientific and overturned by the casual examination of facts. Even worse, they have changed the priorities and the studies in research so that various ideological bits are now removed from the federal research agenda. This is another place where they’re making our future less wealthy, less safe, and less secure than it would be. Part of doing science is sometimes taking the evidence and accepting a hypothesis that violates the current mode of thinking. When a mode of thinking is simply imposed, the evidence is disregarded. The administration has taken a bad situation and made it even worse.

Let’s take the example of the COVID pandemic as being instructive. The right-wing administration instituted a bunch of conspiracy theories that now drive their actions. There were some genuine problems with how we responded to COVID. The view of expertise, as exemplified by someone like Dr. Fauci. The advice that Dr. Fauci gave was from one extreme point of view. Where it went wrong was that it was unbalanced in terms of what the suggestions meant for society. Fundamentally, it was toxic safteyism. There were other bodies of expertise that should have weighed in to moderate the suggestions made and come up with a better solution for society as a whole.

Trump proceeded to pour gasoline all over the conflagration. The issue, particularly for democratically run states, was that the medical advice was taken out of balance and fed into the rampant safetyism that defines society today. Rather than allow there to be reasonable risks for populations that were not susceptible to disease, they imposed draconian lockdowns on all of society. This is probably most acutely felt in the impact on children and education. The problem is that there was an inadequate level of risk-taking and a balanced perspective. It wasn’t that the expert advice was wrong or bad. It was that the expert opinion and advice came from one point of view that was not measured against other considerations effectively. This has turned into another attack on science that has moved everything in the wrong direction. It’s one of the reasons we have unprecedented measles outbreaks in the United States and falling vaccination rates.

In the end, both the left and the right wing of politics failed: the left wing with too much safety and unbalanced advice, the right wing with unscientific ideas and conspiracy theories.

Returning to the governance of the labs, the impact of both of these trends is clear. By punishing failure and failing to take risks, we are setting the science back dramatically. The science and technology we should be counting on for a better future are being crippled by both of these trends.

“Facts do not cease to exist because they are ignored.” – Aldous Huxley

This is not a left-wing or right-wing thing. Both sides of the political spectrum have damaged the labs. They’ve just done it in different ways. Although the right-wing assault is more current and visceral, the left-wing assault has gone on for decades. In both cases, they are harming the nation and ultimately leading to less wealth, less health, and less security for the vast majority of the population.

“Moral courage is a rarer commodity than bravery in battle or great intelligence.” – Robert F. Kennedy

“The quantitative influence of these terms can be made as small as one wishes by choice of a sufficiently fine mesh for the numerical integrations.” – John von Neumann and Robert D. Richtmyer

The implementation of such a method is one of my most enduring achievements and contributions to national security. It was put into a major production code for Los Alamos. The methodology remains important and may be a path forward into a development of improved capability across a whole spectrum of codes. It can be explained with some simple principles. I hope to do that here for a broader audience.

“A new algorithm for the volume tracking of interfaces in two dimensions is presented. The algorithm is based upon a well-defined, second-order geometric solution of a volume evolution equation.” – William J. Rider and Douglas B. Kothe

Another significant professional accomplishment was the paper I wrote with Doug Kothe on volume tracking using volume-of-fluid methods. It’s got nearly 2,500 citations for the Journal of Computational Physics article we wrote. There was a related Annual Reviews Fluid Mechanics article that came out a couple of years after our article. I’m sure our article would probably have 4,000 citations were it not for that. It has a similar citation count to ours. Nonetheless, it is a great accomplishment, and I’ve been told that the code that I wrote (almost as a hobby and part of an unpaid project at Los Alamos) is still being used. It’s the same production code I contributed to for steepened methods.

I didn’t actually put the code that I wrote into the production code. This was the work of a colleague, Ed Dendy, who did the heavy lifting. I was the idea man, and we worked in concert with one another.

In the late ’90s, we discovered that the use of adaptive mesh refinement was insufficient to retain sharp interfaces. This in physical circumstances where those sharp interfaces should not diffuse. The situation where this becomes most acute is when the flow is expanding, but there’s a material interface embedded. In this situation, the material interface grows almost without bound and becomes extremely diffuse. For some simulations, this is unacceptable and must be mitigated.

This shows up in a test problem that is quite common. The Woodward-Colella interacting blast wave problem at late time has a contact discontinuity that becomes embedded in an expansion wave. For most methods, the contact becomes extremely distended. This ends up being a very critical test for the ability of the code to handle a sharp interface.

Thus, we needed to add the ability to retain sharp interfaces to this code. There were two means to do this:

1. The volume tracking that I discussed in the paper with Kothe. I wrote the toolbox code to demonstrate how to do this in an object-oriented way. I’ve discussed this before, where I learned that the standard way of implementing volume tracking with a series of nested if-then loops had too much cyclomatic complexity to be easily maintained or extended.

2. There is a second way to maintain a sharp interface. This is the notion of what’s often called steepeners. This ends up being a controlled way of combating numerical diffusion. This often involves anti-diffusion, and there are a host of methods for doing this. Some are physically motivated, effectively looking like surface tension; these use an analogy to physics to achieve sharp interfaces.

“Simplicity is prerequisite for reliability.” – Edsger W. Dijkstra

This idea originally came from Ami Harten. He introduced a method called the artificial compression method (ACM). This method worked to remove numerical dissipation. It would remove it in a safe way that removed the dissipation where it was unnecessary. ACM was amongst the set of first methods designed to overcome Godunov’s theorem. It would turn out to be useful with later generations of methods.

My mind, when developing numerical methods, works in a Godunov framework. A geometric reconstruction of the profile in a cell is used. That reconstruction informs how fluxes are computed. Below, I’m going to describe and show the performance of methods using this formulation. I’ll also show some plots to illustrate the effect. I will note that this was the project that started to show me what Claude could do that blew my fucking mind.

The key idea in all of this is that, in order to get anti-diffusion, you reverse the way you get diffusion. Numerical diffusion is often viewed as the bane of numerical simulation. This is doubly so in places like Los Alamos, where the canonical numerical method for solving problems is the Lagrangian method, where interfaces are naturally preserved as invariants. Thus, the numerical diffusion necessary in Eulerian computations is viewed as pathological. I’ve spoken before about how wrong-headed this is. The solution of hyperbolic PDEs, which include the linearly degenerate interfaces such as contacts and shears, requires a modicum of numerical diffusion in order to ensure numerically and physically stable and consistent interfaces. Without numerical diffusion, solutions are a mess. Nonetheless, it is around interfaces that much of the annoyance and distress of code users revolves.

I will just note that Lagrangian material interfaces are themselves filled with some pathologies. The most famous of these pathologies is known as wall heating, which arises most classically in terms of the Noh problem, where the heating that results from it is quite extreme. In planar geometry, this is an infinitely strong shock bouncing off a wall, and there’s a dip in the density and a rise in internal energy that is called “wall heating.”

The desire for this Lagrangian material identity-invariant point of view can be seen in our physics education. By and large, when we learn physics in college, the Lagrangian frame of reference is taken. In some cases, this is merely due to simplicity. In other cases, it has a genuine character. In terms of relationships like solid mechanics, the Lagrangian frame is king. Most of more complex dynamics has its foundation with mechanics. Radiation transport is the same. All of this matters greatly in Los Alamos.

The problem with the Lagrangian frame is that phenomena such as shear and turbulence (and not to mention diffusion of a real physical kind) will always induce the destruction of the Lagrangian frame over time. This becomes unavoidable in virtually any multidimensional complex simulation. In this case, some degree of Eulerian representation is necessary if we want to compute the physical circumstances we care about. This is where interface tracking comes in.

Volume tracking methods are generally thought of as ways to keep the interfaces completely sharp in a nearly Lagrangian approach. David Youngs introduced the idea of viewing this geometrically. Youngs introduced a linear planar interface for tracking. This built on a representation known as SLIC from Lawrence Livermore that represented the interfaces as a series of straight lines. In some ways, this was analogous to the Godunov method, where constant values represent the reconstruction in a cell. One of the issues is that this method can produce side effects that are quite profound, and you trade numerical surface tension for numerical diffusion.

With numerical surface tension, the interfaces bead and form substructures or islands that are equally physically incorrect. You trade this for excessive diffusion, so pathologies are unavoidable. Wall heating is another one you get. The key is to be cognizant of the issues and manage them.

Steepened or anti-diffusive transport methods are an alternative. It was this alternative that I divined. Ultimately, my friend Ed implemented it in the production code at Los Alamos. As fate would have it, the production code people gave the method a completely different name than you would find in the literature. It produced a sense that it was unique and separate from what was there. They called the method “interface preserving.” As with most labels, it’s not correct in any way, but it also incorrectly divorces the approach from its origin.

“Numerical analysis is the study of algorithms for the problems of continuous mathematics.” – Lloyd N. Trefethen

In the remainder of this article, I’m going to describe how these methods work and how they perform. In showing how they perform, I’m going to go on a slight tangent to present a sidebar discussion of how much current AI work using Claude specifically assisted me. It’s an example of the sort of incredible boost to productivity these tools can provide.

As they discovered with Claude, I could reproduce almost everything I could do with Mathematica in numerical analysis of numerical methods. I used to do all this work with Mathematica, which carries a heavy price tag that a retired person such as myself isn’t very eager to bear.

I started to experiment with Claude and began to go through a host of numerical analysis tests and techniques that I had used over the years to see what the AI could produce. Ultimately, I want to use Claude to reproduce the results we already know and extend those results. Once I established that I could do everything that I used to do competently and correctly, I’m ready to extend things. This post will benefit from that capability.

One of the big things that Claude does is do this with ease and speed. I estimate that in 2 days of intermittent use of Claude, I was able to produce what would have taken me a month in Mathematica and writing my own code to produce. It also created figures and effortlessly produced a report that I could look at to check the quality of the work and its correctness. The key to doing this work is writing good prompts at the start. Next, the output needs to be carefully examined and verified. The prompt and the work from AI can be wrong. It makes mistakes, and your job is to find them.

In what follows, the results were produced in this manner.

You can get the interface tracking by using an anti-upwind method. The key is to do this in a way that does not go unstable. You take the anti-upwind flux and make sure it is nonlinearly stable with a limiter. TVD theory gives you that theory. The result is quite similar to SLIC.

You can keep this simple by using the so-called Superbee limiter. This is a good, but not great solution. You can amp up the performance by making the limiter sharper, including the CFL number in the form. It is still second-order when flow is resolved, but very sharp for interfaces.

“…a steeper representation of discontinuities, particularly contact discontinuities.” – Phillip Colella and Paul R. Woodward

Starting with Colella and Woodward’s PPM method, this can be improved. A way to do this is to use an anti-upwind high-order method. For example, you find an anti-upwind method using a minmod limiter. This is what we used in the IP method. The key is knowing that the more diffusive a method is in the upwinding, the more anti-diffusive it will be. This is what you do for PPM, ENO, and WENO methods to great effect. PPM also had a heuristic contact-material interface detection. It’s all that simple.

I’ve included some figures to demonstrate what these methods look like geometrically and in performance. Enjoy. This is a really simple and relatively bulletproof method. Going to multi-dimensions and avoiding pathological results is important. Anti-dissipation is playing with fire and must be done with extreme care.

“It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience.” – Albert Einstein

References

Rider, William J., and Douglas B. Kothe. “Reconstructing volume tracking.” Journal of computational physics 141, no. 2 (1998): 112-152.

Scardovelli, Ruben, and Stéphane Zaleski. “Direct numerical simulation of free-surface and interfacial flow.” Annual review of fluid mechanics 31, no. 1 (1999): 567-603.

Noh, William F., and Paul Woodward. “SLIC (simple line interface calculation).” In Proceedings of the fifth international conference on numerical methods in fluid dynamics June 28–July 2, 1976 Twente University, Enschede, pp. 330-340. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005.

Harten, Amiram. “The artificial compression method for computation of shocks and contact discontinuities.” Maths Comput 32 (1978): 363.

Colella, Phillip, and Paul R. Woodward. “The piecewise parabolic method (PPM) for gas-dynamical simulations.” Journal of computational physics 54, no. 1 (1984): 174-201.

Yang, Huanan. “An artificial compression method for ENO schemes: the slope modification method.” Journal of Computational Physics 89, no. 1 (1990): 125-160.

Shu, Chi-Wang. “High order ENO and WENO schemes for computational fluid dynamics.” In High-order methods for computational physics, pp. 439-582. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999.

“The best way to have a good idea is to have lots of ideas.” – Linus Pauling

Steve Jobs called the computer “a bicycle for our minds.”

I’ve been working on an idea with a friend and colleague. Some of that work called for numerical analysis, and since retiring I no longer have the software I used to rely on. For these tasks, I had always used Mathematica, which is expensive. My friend kept saying I should just ask Claude to check things, as if it were easy. This weekend I finally took him up on it.

I quickly found that Claude could do the analysis using Python libraries. Even better, it wrote up a report, and I could ask questions in plain language. What else could it do? I started asking Claude for the kinds of analyses I used to do myself. I derived numerical approximations. I analyzed accuracy and stability for ODEs and PDEs. I did modified equation analysis of various methods. It did a great job on every task, did it quickly, and produced documentation for me to check.

Next, I wanted to see the methods in action. I had Claude write and run code. I defined metrics and computed convergence rates. I implemented a host of linear and nonlinear methods, and I created some new ones. All of this happened at lightning speed. Soon I had a 200-page report covering everything I had asked for. The work was extremely competent, the feedback was useful, and all of it was scrupulously documented.

My mind was completely blown!

By the time I was done, it had done a month’s worth of work in hours. Counting the time I spent checking results, it was maybe one full working day. That is a speedup of 20 to 50 times, depending on how well I specified the tests. I can now see how easily it can help with work and accelerate the fuck out of progress. It gives me feedback, but the ideas and the direction are mine. I also need to check all the work. It needs to be verified! Without my guidance and creativity, the work isn’t worth much.

This should be the model for AI. It increases your productivity. You create and define the work, and it implements and tests your ideas. This applies to some very specific kinds of knowledge work, and it mirrors what we’ve seen with coding agents. The human is still driving. It lets you test, study, and implement more ideas. You can innovate and fail at a breakneck pace.

This is a change from how we usually work. Checking and verifying the work is critical. Verification needs to be based on well-defined and understood principles. It is somewhat more results-based, but requires a full education. I can see this change being needed in education. Any work done with AI needs to be seriously scrutinized. This is a skill to be learned as an emphasis.

This is the model we need. AI accelerates humans. It lets us try more ideas. Really, it lets us fail more. The barrier to exploring an idea gets lower. Most ideas won’t be good. The key is to fail and learn, then refine the idea and try again. You go through more cycles: fail more, learn more, create more.

“Ever tried. Ever failed. No matter. Try again. Fail again. Fail better.”– Samuel Beckett

“The fundamental cause of the trouble is that in the modern world the stupid are cocksure while the intelligent are full of doubt.” – Bertrand Russell

The other day, I got a comment online that made my blood boil. It was from someone whose profile said they were intelligent, but their comment said that they were not. The comment offered nothing but outrage. I can’t comment on their motivations, but they don’t seem good. I would have rather been able to find out what prompted them. I still would.

This was a prototypical internet troll.

The comment was implying that when I wrote my post on the Navier-Stokes equations last week, I must have been high on marijuana. There was no comment or point made on why this person felt this way. It simply was delivered as a personal attack against me. This personal attack did nothing to elevate the dialogue around the topic chosen or provide any sort of useful critique for my writing. It was simply an ad hominem attack.

One of the things that I appreciate intellectually is that I’m often wrong, and I hold that possibility open. I‘m eager to engage in dialogue about differences in opinion or belief. We are all simply seeking the truth. I also don’t believe I’m the most eloquent or effective writer. I only want to get better at it. This person’s comment did nothing in any of those regards. It was purely negative.

“What can be asserted without evidence can also be dismissed without evidence.” – Christopher Hitchens

It was a comment made that did not elevate any conversation, and only simply attacked me. I declared his comment an ad hominem attack. A day later, this person’s response showed that they couldn’t comprehend what the word ad hominem meant. Or how they had committed such a crime themselves. This person is just an internet troll.

“The greatest remedy for anger is delay.” – Seneca

One of the things that’s most poisonous to our current world is that internet trolls have entered into the real world. Our American President is an internet troll. He has taken trolling as a political and governing strategy. Every Truth Social post has trolling built in. We have a government that spends a great deal of its time simply trolling people. Doing this instead of actually engaging in any sort of constructive problem-solving.

The goal of the troll is to dig into someone and enrage them. Then get them to respond with rage and create a downward spiral. This happened to me. Fortunately, I caught myself and deleted my incredibly nasty reply to the LinkedIn post. The blog post is still up! This also deleted the troll’s comment.

This is the same bait that the president throws out all the time: something incendiary to invoke a response. He has taken a highly respected office, the presidency of the United States, and turned it into a platform for trolling. It is something all of us know something about. It is also something all of us should reject, regardless of our political stance. Unfortunately, this isn’t happening. It’s been highly effective.

“The amount of energy needed to refute bullshit is an order of magnitude bigger than to produce it.” – Alberto Brandolini

I probably can’t do anything to fix this phenomenon. It seems to be a fixture of our modern era. What I can say is that the internet troll, or just the troll in general, is a parasite. They feed off your anger and outrage. Resist it. It only encourages them. Worse than being useless, they actively work to destroy and sow destruction rather than contribute anything positive.

We have plenty of problems and issues to consider. It’s much better to work towards constructive development of ideas and apply critique in a manner that makes things better. The internet troll does none of this. They’re purely destructive. The best thing that I can do is remind myself to actively ignore them and give them little or no space or air.

For those of you out there, don’t be a troll. Trolls simply sow anger and division. We have too much of that. Instead, engage online with thoughtful and considered thought. Raise the level of dialog and thought. We live in dangerous and challenging times. All of us need to work together to make a better future. Thoughtful and powerful thinking can lead to solutions to problems. Working together, people can get better if they support each other.

“I learned long ago, never to wrestle with a pig. You get dirty, and besides, the pig likes it.” – Cyrus S. Ching.

“As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality.”– Albert Einstein

The recent news that OpenAI solved the Navier-Stokes Millennium Prize problem has put these equations in the spotlight. I must admit to feeling fairly significant irritation every time I read a story.

Why? Because of how these equations are being presented.

They are not the fundamental equations of motion. The solution, if it is indeed true, actually points to some fundamental limitations of that formulation. It does not undermine the utility or correctness of the actual equations that best describe reality. Those are different equations! Any fault lies with this particular form of the Navier-Stokes equations. This should form a key part of any narrative. To date, it has not, and it is a disservice.

When one understands the true nature of the fundamental equations, it brings into the light the deficiencies of the Navier-Stokes equations as stated by the Millennium Prize. The true equations do not have these deficiencies. It’s useful to elaborate on this. That means describing the true equations and the derivation of the equation solved in all the stories. It also means pointing out the limitations that may have been highlighted by this solution.

Just to review, the solution involved adding a forcing term to the equations. This created a situation where you had an infinite velocity. This blowup is completely unphysical. It is not related to other blowups that would be of physical significance. Instead, it is related to some sort of failure in the framework.

The whole situation is quite complicated technically. Thus, one can give some credence to the popular descriptions of these equations. However, the damage done by the descriptions can be quite great, because they don’t come with any of this nuance. The fulcrum of this deeper explanation is the simplest equation in the stated set of Navier-Stokes equations, the divergence-free condition for the velocity. This is the term is While being simple, it also injects the most profound difficulties into the equations.

The divergence-free condition can be derived several ways. Fundamentally, it means that the equations themselves do not support variation in density. This variation in density is a fundamental characteristic of almost every fluid flow. This includes the situations these equations claim to model! When it is removed, there are a number of consequences. Most acutely, from a mathematical perspective, this equation renders the coupled system of equations elliptic in nature. This ellipticity brings several consequences that are probably related directly to the blowup that was discovered. I would be quite surprised if the blowup was not directly related to the divergence condition.

The ellipticity results from the removal of sound waves from the equations. These sound waves are an important mechanism by which density varies in real fluid flows. The removal of sound waves effectively means that they have been rendered infinite in speed. Thus, the equations, as stated, violate causality. As one of my friends at Los Alamos quipped, you have suddenly introduced superluminal sound waves into the system of equations. By superluminal, I mean sound waves that move faster than the speed of light. Here the speed is infinite.

I will return to causality later in the essay.

“If your theory is found to be against the second law of thermodynamics I can give you no hope; there is nothing for it but to collapse in deepest humiliation.”– Sir Arthur Eddington

A second, and potentially more persuasive, way to make this point is that the divergence-free condition removes thermodynamics from the equations. Thermodynamics is a fundamental constraint on continuum physics. In that sense, it results in the removal of the second law of thermodynamics. The energy equation could be added to Navier-Stokes for good measure. However, with incompressibility, it lacks some of its connections to the full system. Again, this is a fundamental flaw in the equations as stated. The reality is that they do not describe flows in reality. They are, rather, an approximation to those flows. The nature of this approximation is worth discussing in a bit more detail.

The Navier-Stokes equations described in the stories can be derived from a truer set of equations by simply doing what mathematicians call an asymptotic expansion. Klainerman and Majda did this work, and their derivation is bulletproof. However, the equations they start with are themselves the adiabatic (meaning constant entropy) version of the compressible flow equations. Thus the second law of thermodynamics is removed by fiat. This is a fatal flaw. To get to the equations they wanted, they needed to remove it. They knew the end point of the incompressible equations and left the second law behind to get there.

This is an explicit statement that one is not operating in flows where the second law of thermodynamics is working. What one should know is that the second law of thermodynamics is always at work in continuum systems. It is a fundamental bridge to the determination of physically admissible flows, particularly as the effects of viscosity become small and negligible. In turn, the small and negligible viscosity behavior is important for understanding the fundamental dynamics of turbulence.

This, in turn, gets to the fundamental failing of these equations in terms of their ability to describe nature. As posed, there are many who believe that the phenomena of turbulence are contained wholly within the incompressible equations being solved. I, for one, have expressed extreme doubts about this. My doubts come mostly from two things. The first is the removal of the second law of thermodynamics.

The second is the observed behavior in turbulence matching, in a very real sense, the derived behaviors of compressible flows. Namely, the presence and behavior of shock waves do not depend on the value of viscosity. Just as with shocks, the behavior of turbulence in a higher Reynolds number flow does not depend explicitly on the value of viscosity either. This similarity is not a happenstance. This similarity is fundamental.

In both cases, the amount of dissipation that is seen is proportional to the cubed difference in the longitudinal velocity of the flow field. The two expressions, for dissipation in shocks and dissipation in turbulence, only differ by the constant leading them. The shock relationship is a one-dimensional relation, whereas the turbulence relationship derived by Kolmogorov is 3D. Nonetheless, the removal of thermodynamics is a huge gap. It is something that should be recognized when looking for the physical significance of these solutions. It should make one disregard them.

If I haven’t courted enough controversy yet, let me dig myself an even deeper hole.

The dissipative terms in the equations also carry with them some difficulties. In short, they also imply an infinite speed of propagation, thus violating causality as well. This is a more subtle and far less well-accepted point. The dissipative terms, whether they are associated with viscosity or heat diffusion, are approximations as well. The true terms actually include a finite speed of propagation. Thus, the real, true system is entirely hyperbolic. In nature, what one might expect is that, under similar conditions, the parabolic terms might also produce some abhorrent results that produce infinite velocities. This is pure supposition.

By and large, the hyperbolic version of these equations is not useful to solve. However, it is pretty clear that causality and finite speed of propagation are something worth keeping in mind. Thus, the same kind of limitations that one sees with Navier-Stokes in terms of incompressibility may also be present with the parabolic terms. This would include parabolic terms included in the compressible flow equations.

Just throwing it out there as a possible deeper outcome of all of this work.

“When I die and go to heaven there are two matters on which I hope for enlightenment. One is quantum electrodynamics, and the other is the turbulent motion of fluids.”– Horace Lamb (attributed)

The whole term “blowup” is also something that requires a bit more explanation. With a shock wave, one experiences a blowup in derivatives. In other words, the derivative of the flow values becomes infinite. The values of the quantities being evolved remain finite, but there is a formation of an infinite gradient. This is true in the absence of viscosity, but it can be navigated with the second law of thermodynamics. The same blowup is almost certainly the mechanism by which turbulence achieves its observed characteristics. In both cases, this produces the sort of dissipation that arises independent of the value of viscosity.

The real mathematical exercise that would be useful going forward would be to determine under which conditions such a blowup occurs in low-speed flows. These are the flows approximated by the Navier-Stokes equations that were solved. Instead, keep the 2nd law and finite-speed sound waves.

The clear message is that these equations have been shown to have a problem by this AI proof. They already had problems that were rather obvious. Now we have a mathematical exercise that demonstrates the consequences of this. This is useful mostly in navigating the fundamental flaws in these equations. It should be an exercise in appropriate limits on their use. Instead, they are being promoted as the fundamental equations in most of the descriptions, and that is a damaging thing to do.

The reality is that all of these equations are models of reality. The universe does not run on mathematics, but mathematics is a good description of how the universe runs.

“Mathematics is the part of physics where experiments are cheap.”– V. I. Arnold

The reality is that these materials are formed from collections of atoms or molecules. They involve certain collective behavior described by even more fundamental equations. Those equations are too detailed to be useful for solving these equations in a regular way. By the same token, the fully hyperbolic description of all these equations is only useful in a limited frame of reference. The key thing to do is to determine which approximation is appropriate for which circumstances.

More than anything, it is these nuances and notions that need a human touch. The greatest place for humanity is to put cognitive limits and choices into the recipe for mathematics. The mathematics done in the absence of this becomes something that may be much ado about nothing. Meanwhile, the OpenAI result is impressive and troubling, all in one fell swoop.

It also fails to recognize the broader context of “so what?” Why is this strategically important? What are the consequences? Even if the proof is entirely true, they didn’t make any sort of broader conceptual understanding of the equations themselves. They may have discovered something really important, but they can’t tell you why it is important. This is, in an essential way, the role of humanity and human cognitive effort.

It would be good if there were a broader recognition that the Navier-Stokes Millennium Prize is poorly stated. As such, they are useless for understanding our physical world. We should instead be focused on equations that don’t have these profound causality issues. Equations that allow the invocation, either explicitly or implicitly, of the second law of thermodynamics. This would be a route by which mathematics could have a deeper and more profound impact on our understanding of the universe.

“Science is built up of facts, as a house is built of stones; but an accumulation of facts is no more a science than a heap of stones is a house.”– Henri Poincaré

“Never attribute to malice that which is adequately explained by incompetence.”– Arthur C. Clarke

Incompetence is the lack of the skill, ability, qualifications, or legal capacity required to perform a task or job to a satisfactory standard. It represents the opposite of competence and can manifest across general performance, specialized fields, and interpersonal dynamics.

I got some fairly sharp feedback on my essay about incompetence and its spread among leadership. It even went so far as to say I should take the post down because leaders can’t possibly be interested in putting incompetent people beneath them. I beg to differ on the basis of what I see, both at the national level and in my experience over the past few years at Sandia. It is not what should be. It is what is actually present. Examples follow below.

It’s from the observation that the incompetence we see in the Trump administration is actually present across society.

Typically, when we think of incompetent people being placed in positions of authority, it is the selection of someone due to loyalty. Often related to a personal relationship, a bribe, or some other decision factor that has nothing to do with the job being done. This happens in situations where loyalty to the leader is the most important thing. Sometimes these people have the capacity to be competent but simply choose not to operate competently so that the leader is happy. This is selective incompetence. It is quite common everywhere in society.

“I find it rather easy to portray a businessman. Being bland, rather cruel and incompetent comes naturally to me.” – John Cleese

To most of us, the current executive branch of the government is a sparkling example of incompetence in action. We see the different types of incompetence on display.

For example, Jared Kushner and Steve Witkoff are our envoys and negotiators. They are completely incompetent and were chosen only because of their personal relationship with the president. Then you see the incompetent people chosen for loyalty. These are people like Pete Hegseth and, astonishingly, RFK Jr. Finally, you have the people who are competent and simply choose not to use their competence. These are people like Treasury Secretary Scott Bessent and Marco Rubio, our Secretary of State.

The way the incompetence manifests itself is different in each case. For Kushner-Witkoff, it is simply the capacity to operate in a manner that would be viewed as criminal in any sane world. They are simply using their positions to make corrupt deals rather than negotiating in the best interests of the United States. This is ordinary and blatant incompetence. They are loyal to the boss and work to line their pockets along with his.

Then you have the people who merely operate through loyalty or vested interest. Hegseth is this sort of incompetent person. He is certainly intelligent, but his fundamental philosophy and approach to the world, basic values, and ethical system undermine any competence he might have. He is simply an astonishingly amoral person who has no business being in a position of authority. He’s also intellectually incapable of the job he’s doing, including an inability to administer something as sprawling and huge as the Department of Defense.

Finally, you have Bessent and Rubio, who, by all accounts, are both highly competent individuals. They choose to support incompetent decisions time and time again, merely out of loyalty. Both Rubio and Bessent are agreeing to and implementing the administration’s policies. These policies are basically a withdrawal of American power and prestige, purely for near-term gain, usually the gain of the president himself. They are implementing them without regard to the damage they’re doing to the future of the nation. This is the epitome of selective incompetence.

“The first method for estimating the intelligence of a ruler is to look at the men he has around him.” – Niccolò Machiavelli

When I change over to my observations about Sandia, this is the most common form of incompetence I encountered.

Before I retired, I saw people who certainly had the intelligence and the capacity to operate competently but chose not to. The reasons for choosing not to were varied. Most often, it was to avoid making waves and causing problems for those above them. This was probably the motivation behind what happened to me.

There also emerged this maxim among leadership: problems were not to be named. Often, the idea of simply covering them up and hoping that they wouldn’t blow up became the operative principle. This was seen clearly in what Sandia would call the tier board, a means of moving and escalating information up or down the management chain. It is useless because of implicit incentives.

What I noticed more and more over time was that anything negative on the tier board was simply killed. It never made it up the chain. My understanding is that this behavior was repeated over and over across the levels. Thus, bad news, problems, and issues never made their way up to the highest ranks. We see this same behavior reflected in our government. There seems to be an absolute moratorium on reality anytime that reality is in any way, shape, or form negative for the leader. The long-term consequences of this sort of leadership are likely to be catastrophic. Problems fester and grow, and solutions are rarely sought. Thus disaster is courted.

The biggest problem at the labs is the increasing inability to do big things. Doing big things is the result of solving hard problems. When those problems don’t exist or aren’t acknowledged, we don’t create, we don’t innovate, and we don’t do the hard things. The reason is the incompetence of leaders who refuse to acknowledge or accept big problems. Worse yet, they punish those who bring them the problems and do not enlist expertise in solving them. This is something I witnessed over and over in my final years at the labs. The decisions are made by nominally competent people who chose a different path. The easy, incompetent decision in front of them is easier to implement. Thus, they choose the easy way. Again, selective incompetence is found.

Selective incompetence is a fundamental moral or ethical failing. It is taken by a competent individual who chooses the path of incompetence. Why do they do it? I can only surmise that it is the path to success, money or power. Other considerations are insufficient to choose competence.

The path forward is to start prizing competence more than money and power. Incompetence becomes incompatible with success.

“Work is accomplished by those employees who have not yet reached their level of incompetence.”– Laurence J. Peter

Incompetence
/inˈkämpətn(t)s/
is the lack of the ability, skill, knowledge, or qualification needed to do a job or perform a task properly to a satisfactory standard. It is the direct opposite of competence. A question to think about whether simply not exercising one’s skill, ability, knowledge or willfully not performing a task is also incompetence.

“Totalitarianism in power invariably replaces all first-rate talents, regardless of their sympathies, with those crackpots and fools whose lack of intelligence and creativity is still the best guarantee of their loyalty.” – Hannah Arendt, The Origins of Totalitarianism (1951)

The root cause of the sprawling incompetence in our country today is the value placed on loyalty over competence. The complementary driver of this is the primacy of money. Money has become the sole measure of success. The rich and powerful are strip-mining our society to line their pockets. For them, profits outweigh damage to society. They have accomplices who benefit from assisting them with incompetent leadership. The promise of making lots of money is the root of it. In the public sector, government funding is used to bludgeon everyone into submission. We see this in Universities. I’ve seen it at the Labs too. It is bad, and it is getting far worse.

Loyalty to power is the price, and integrity is the cost.

One of the key elements of all this is that you pay a personal price. It is clear that the way to make the most money these days is by playing the game, not speaking out, and serving the incompetent leadership. Incompetent leadership wants to do what they do without any feedback from anybody who knows what they’re doing. If you speak out, you’re put out into the wilderness. There won’t be any money, there won’t be any work, and ultimately your career suffers. If you play ball with them, you can make money, and you can be successful.

I saw this at Sandia, and you see this in the political dynamic. The price of demonstrating competence is a loss of money. If you’re loyal and play the game, you are paid handsomely, and it’s the road to success in this dynamic. Competence, honesty, ethics, and morality all come at a price, a personal one. In this way, the powerful lock into their power and save themselves from any criticism coming from those who actually know what to do. Meanwhile, society as a whole suffers while the powerful thrive.

“For the love of money is the root of all evil.” – 1 Timothy 6:10

I’ve learned from friends that the laboratories (Sandia at least, and I assume Livermore and Los Alamos as well) have all bought into Trump’s demand that artificial intelligence be relabeled as “super intelligence.” I am sure the rank and file at the laboratories roll their eyes at this. They see it as another example of how craven and incompetent their leadership is. True, no matter how smart or scientifically accomplished those leaders are. Their integrity is simply sacrificed. All of them have turned into craven whores simply because their funding and money are at risk.

“For a successful technology, reality must take precedence over public relations, for nature cannot be fooled.” – Richard Feynman, Rogers Commission Report, Appendix F (1986)

We can see how Trump wields this money and power like a chainsaw against our greatest institutions, producing ever greater damage. Our biggest corporations are even more susceptible. One casualty is the promise that artificial intelligence will deliver good things for society. Right now, all it’s going to deliver is more inequality and more money in the hands of the few. The price to society will be massive. The opportunity for the technology to deliver good will be destroyed in the process. It is going to be the damage of social media on steroids.

The cost is going to be vast societal damage that exceeds even the vast damage done by social media. Artificial intelligence is a much more powerful and, by no coincidence, more dangerous technology. In the hands of these money-grubbing whores, it is all of us who are going to get fucked.

“I have a foreboding of an America in my children’s or grandchildren’s time… when awesome technological powers are in the hands of a very few, and no one representing the public interest can even grasp the issues; when the people have lost the ability to set their own agendas or knowledgeably question those in authority…” – Carl Sagan, The Demon-Haunted World (1995)

Let’s take the interview Ezra Klein did with Jensen Huang, the CEO of Nvidia. Jensen managed to say some very intelligent, learned things until the conversation got to regulation and control. Then he turned on his heel and delivered a craven, self-serving view of AI. He only cares about his wealth. All his credibility should have been shredded in that moment.

He has submitted completely to the president because of all the money he stands to make. He wants to sell cutting-edge chips to China despite export controls. He wants the AI companies to go full speed ahead with data centers, because those data centers will mostly buy the chips his company sells. He’s just looking at the stock price, and his answer lacks a scintilla of credibility.

“If men were angels, no government would be necessary.” – James Madison, Federalist No. 51 (1788)

This pathological response to money has been brewing for decades. I saw it at the labs, where the competence of leadership was sacrificed over and over again for the promise of funding. Money became everything, and it became the only thing that managed ideas. Research and science were sacrificed on the altar of money.

The end result has been incredibly toxic for our most important research institutions and for the country as a whole. We see managers repeatedly trading their integrity for financial gain, or to avoid financial loss. When offered the choice between money and doing what is right, what is ethical, or what is competent, they choose money every single time.

“Scientific progress on a broad front results from the free play of free intellects, working on subjects of their own choice, in the manner dictated by their curiosity for exploration of the unknown.” – Vannevar Bush, Science, the Endless Frontier (1945)

This played out in my own career. Unethical decisions were made, and the only explanation for the lack of ethics was concern about funding. Ultimately, ethics and integrity were traded for money.

The result is a lack of competence. Incompetent leaders are everywhere; one need only look at the top of our federal departments and institutions. Increasingly, the thing that binds them all together is a lack of competence and an insatiable desire for money. Corruption and graft are rampant.

“…he looked for three things in hiring people: integrity, intelligence and energy. And he said if the person did not have the first, the latter two would kill him.” – Warren Buffett, quoting Peter Kiewit, University of Florida lecture (1998).

Nowhere is this truer than in our largest corporations, where profit and maximizing shareholder value are the only things they care about. No attention at all goes to the price paid by society or the citizens of our country. If there is money to be made, our citizens are simply sacrificed.

The Social Responsibility of Business Is to Increase Its Profits.” – Milton Friedman, title of his New York Times Magazine essay (1970).

The real problem is that there is perhaps no worse time for this than the present. We need competent leadership and direction now more than ever, and yet that competence has been swallowed by a bottomless greed for money and power. The fixation on money creeps in and destroys competence wherever it takes hold. We have monumental challenges to contend with. AI is one of them, and massive changes in what warfare and national defense look like are another. In the face of these challenges, we see greed, the raw exercise of power, and a focus on money swallowing all the good answers.

One of the most insidious ways this dynamic manifests itself is by actively undermining competence. Someone who is incompetent ultimately becomes more useful to those in power, because they lack the characteristics that drive a person toward competence. Competence is the characteristic that interferes with doing exactly what the leadership wants. Moreover, acquiring competence becomes detrimental, and rather than developing competent people, we actually foster the development and promotion of incompetent ones. This makes the situation even worse.

There are plenty of examples to choose from. Perhaps foremost among them is our Secretary of State, Marco Rubio, who seems to have traded his competence for loyalty and now has to defend all sorts of incompetent decisions. He could have been a decent or even good public servant. Now he simply serves greed and incompetent decision-making.

Increasingly, incompetent leaders will avoid competence at all costs. Competence, ethics, and morals have a habit of getting in their way. One might think this is limited to the political realm, but I saw the same thing at the laboratories. Over time, more and more incompetent leaders were put in place. Decisions favored money above all else, and competent, correct decisions were often disfavored because they meant somewhat less money.

“If we use, to achieve our purposes, a mechanical agency with whose operation we cannot efficiently interfere once we have started it… then we had better be quite sure that the purpose put into the machine is the purpose which we really desire and not merely a colorful imitation of it.” – Norbert Wiener, “Some Moral and Technical Consequences of Automation,” Science (1960)

The second example is warfare. Drones and autonomous warfare are clearly the future. Our national defense is built around Cold War systems. They increasingly resemble the Maginot Line, which the Nazi blitzkrieg simply went around in 1940. We are flirting with the same outcome for ourselves. Inaction will ensure a future catastrophe. We are already behind the necessary transformation.

“Generals always prepare to fight the last war.” – Proverb.

Perhaps the more consequential danger we should face head-on is the change in how warfare is conducted. The future is being shaped every day in the war between Ukraine and Russia. We have seen similar things happen in Iran. In both cases, a middle power uses drones and automated warfare to hold its own, or perhaps even to defeat a traditional power. We ignore this. We are imperiled.

Rather than take notice of this, our Department of Defense is promoting old ideas about warfare. It’s pretty clear that Pete Hegseth would much rather have a bunch of oiled-up, muscle-bound men reminiscent of the movie 300 and Sparta than a fleet of drones tailor-made to actually fight a war today. The focus should be on modernizing our military. Instead, we focus on performative physical fitness, hormone levels, and beards. Any minority that is promoted to a higher rank is suspect. Intellect is optional when it should be prized.

“Every age had its own kind of war, its own limiting conditions, and its own peculiar preconceptions.” – Carl von Clausewitz, On War, Book 8

He is, without a doubt, the single most incompetent person in the entire cabinet. He is completely unfit for the job. I will note that being the most incompetent member of Trump’s cabinet is a difficult thing to achieve. Just look at the host of supplicants and morons he has assembled. Somehow, Hegseth is even less competent than RFK Jr. This should terrify all of us. Worse yet, his incompetence comes just as we enter a period of immense danger. Today demands a complete transformation of our warfighting capabilities. Hegseth stands in the way of the transformation we need.

Using money, or the threat that they would not make as much as they wanted, Trump brought the entire high-tech world to heel. Just think: a group of incredibly rich, incredibly intelligent, accomplished men, all reduced to subservience to a blithering, anti-intellectual idiot, merely because he threatened how much money they could make. Rather than speak wisdom or act independently, they gleefully submitted to his idiotic ideas. Super Intelligence was the most obvious and pathetic example of their capitulation. It was done solely to allow their acquisition of more money.

“To announce that there must be no criticism of the President, or that we are to stand by the President, right or wrong, is not only unpatriotic and servile, but is morally treasonable to the American public.” – Theodore Roosevelt, Kansas City Star (1918)

The only silver lining is that this has generated immense political backlash. The plans our tech overlords have for us are meeting a great deal of resistance, and it is growing. In a time when we need intelligence, wisdom, and integrity, we have the opposite. In a time when we need to shape AI to produce genuine good for society, we have greed and a lust for power in its place. None of us should have any faith that this is going to end up in a good place.

Today, managers and leaders want loyalty masquerading as competence. We see over and over again that those who have that competence are willing to trade it for loyalty as long as the illusion of competence is upheld. The formerly competent experts will be rewarded handsomely for their service to the leaders. The synonym for this is: The competent ones who serve the incompetent leaders trade their integrity for a bigger paycheck. In today’s world, success is measured in dollars, not by being correct or doing the right thing.

The big lesson today is that the trade of loyalty for competence we see with the Trump administration is going on everywhere. I increasingly observed this in my professional life. The trade is happening at our top research laboratories and at our universities. I know it was happening in Sandia. I suspect it is happening almost everywhere else. The only difference is that Trump is more brazen and nakedly obvious in this regard. In a sense, what Trump is doing is simply vividly reflecting the choice that society has already made.

We live in a time of transformational change, and we also live in a time of numerous existential dangers. Given the challenges we face, confidence should be required to navigate them. Today, that competence comes with the demand of obedience and the ability to reject that competence if it chafes against the desires of leadership. The greatest danger is that, in this moment, competence has been rejected and incompetence is sought. This incompetence only serves to amplify the power of those already winning. The threat is that the incompetence will turn these potential dangers into real existential catastrophes that endanger us.

The fundamental cause of the trouble is that in the modern world the stupid are cocksure while the intelligent are full of doubt.” – Bertrand Russell, “The Triumph of Stupidity” (1933)

“It is difficult to get a man to understand something when his salary depends upon his not understanding it.” — Upton Sinclair

In the last week, I’ve been working through my part of the eulogy for my friend Tim. Tim and I worked together at Sandia, and over the years, many of our conversations were really just the two of us trying to make sense of the place. Writing the eulogy brought up old thoughts and feelings about how I retired. It had been a topic Tim and I talked about at length. On the one hand, it would be good to let it go. On the other hand, it was a serious issue with deep implications. The problems go further and farther than the knee-jerk response would allow.

I have made peace with my decision to retire. Continuing to work there was a complete waste of my time. It would have been financially beneficial to stay. In terms of my life, that benefit would have come at too great a cost. I’ll say something a bit arrogant too: I was too talented to work for those people. Continuing to be associated with incompetent and unethical people was beneath me. It was a waste of my considerable talents and my time on Earth.

“Silence becomes cowardice when occasion demands speaking out the whole truth and acting accordingly.” — Mahatma Gandhi

It should be obvious to my readers that I’m still engaged with science and technology. I’m. engaged with a large degree of passion. I have ideas I still want to explore. The reality is that every bit of that engagement was wasted at Sandia. It’s simply the sad truth that a national lab was a dismal place to put my talents to use. Not every talented person belongs at the Lab. That’s fine. That’s normal. The difference in my case is that my interests and talents sit pretty close to the bullseye of the Lab’s actual mission. That’s what concludes utterly damning.

“Loyalty to the organization, loyalty up and down the line, is prized above integrity to oneself.” — William H. Whyte,

In the process of all this pondering, I wandered into another thought. Why did Sandia protect the terrible managers? Why did they choose to do the wrong thing? I started seeing direct parallels to a couple of other issues. We seem to be having a society-wide collective-action problem around ethics and morals. Bad behavior and corruption run rampant among the rich and powerful. Nobody with the power to stop it does. As a society, we simply look away. The corrupt have impunity.

“Every society honors its live conformists and its dead troublemakers.” — Mignon McLaughlin

What if bad cops and bad managers are manifestations of the same problem? Think of the Catholic Church and its molesting priests. Think of the echoes of the same dynamic in the Cornell gang-rape case. Penn State sat on what it knew about Jerry Sandusky for years. USA Gymnastics protected molester-rapist Larry Nassar. They protected them long after coaches and staff had known of the violations. Wells Fargo managers were pressured to hit sales quotas by opening fraudulent accounts. The bank’s leadership looked away for years; money was being made. Police departments and the “thin blue line” look the other way, even protecting bad cops. Why do organizations cover for bad behavior instead of ridding themselves of the offenders?

“We must take sides. Neutrality helps the oppressor, never the victim.” — Elie Wiesel

The Cornell case is the one that has stuck with me deeply. I’ve dug into the details of the secrecy around it and the sheer number of people who stayed quiet. What’s remarkable, and damning, is that so many people knew what was happening and did nothing. No one intervened. No one alerted officials. No one helped the young woman at the center of it. This happened among a group of privileged, by all accounts intelligent, young men. That’s a damning indictment of moral rot and a basic lack of character in people we’re about to hand the future to. If bystander silence shows up naturally like this, before anyone has a career or a pension or an institution to protect, the issues run deeper than any incentive structure. It condemns society as a whole. It looks closer to a default, and we just give that default legitimacy

“The sad truth is that most evil is done by people who never make up their minds to be good or evil at all.” — Hannah Arendt

That same willingness to look away shows up anywhere institutions have a reputation worth protecting. I’ve always wondered why police forces protect bad cops. Bad cops are a huge threat to public trust. You’d think good cops wouldn’t want to be associated with them. But the behavior of police forces and police unions is the opposite. Bad cops are defended reflexively. They defend bad cops to a degree that’s repugnant. They let them keep working. They rarely drive them out. The bad cops keep doing bad things.

The question I kept circling back to: is the tolerance of bad management I’ve seen the same idea? Do managers, writ large, close ranks around the bad ones among them, the same way police close ranks; the same way dioceses did; the same way as frat boys; coaches finding a criminal among their ranks; the same way corporate boards do when a star executive turns out to be a fraud?

Upon reflection, you start to see the parallels and the reasons these things happen. A few maxims of human group behavior are doing the damage. These show up again and again, in every one of these institutions. The Labs, it turns out, are completely ordinary in this regard. They are subject to the same human frailties and shortcomings.

A big one is self-interest dressed up as institutional interest. Exposing a bad actor costs the people who’d have to do the exposing: lawsuits, bad press, lost trust, lost donors or votes or customers. The people positioned to catch the problem are usually the same people whose careers and reputations are tied to the institution looking good. So the short-term incentive is almost always to contain the story, not tell it. Increasingly, the cover-up is assisted by policy and secrecy.

Identity fused with the group. Police, clergy, and tight political or corporate networks all ask for, and get, unusually strong identification: the thin blue line, the body of Christ, the firm, the family business. Once your sense of self is wrapped up in membership, a colleague’s wrongdoing starts to feel like an attack on you personally, not a policy problem to be solved. Covering for a fellow member reads internally as loyalty. The cover-up becomes a virtue instead of a damning action.

Internal, self-run discipline. In every case I’ve mentioned, there are disciplinary procedures that are largely internal to the organization. That lets punishment stay hidden, or not happen at all. Police investigate police. Dioceses handled abuse allegations themselves for decades. Legislatures police their own members’ ethics. Corporations run HR processes before, if ever, anything reaches an outside authority. Self-policing without an external check reliably degrades into self-protection. The investigators and the investigated share the same stake of institutional cleanliness.

Secrecy and privacy, formalized. There’s always an element of confidentiality that these organizations provide their members, and it’s remarkably easy to stretch. In my case, the clearest version of this was the laboratory’s own culture of secrecy, which has only grown. There was always secrecy around nuclear weapons data. That part makes sense. That necessary secrecy has expanded hugely in recent years. More and more of the lab’s ordinary business gets classified as protected data. What used to be OUO, Official Use Only, is now CUI. Basically the same thing: a category where information gets locked away from external eyes. Once locked away, scrutiny is impossible.

I’ll note this gets used for exactly the purpose you’d expect: protecting the organization, not the mission. In my case, one category was “export control” data (CUI). Export control is so poorly defined that almost anything can be declared export-controlled without any rational basis. That’s exactly what happened to the report that ultimately led to my decision to retire. That kind of power lets bad actors act without consequence and without accountability. It was clear the organization backed their lack of accountability. They actively defined information that was not export-controlled to hide a problem from the outside world.

The secrecy at Sandia points to something bigger than any one report: transparency itself. Sandia was always far less transparent than its sister lab, Los Alamos. Some of that came from Los Alamos being a company town. There, the lab and the community were practically the same thing, and almost nothing stayed hidden for long. The differences went deeper than geography. It was a real cultural difference between the two labs, baked in from the start. A lack of transparency is the perfect environment for corruption to take root. Secrecy is part of the basic mission at both labs, for understandable reasons tied to weapons work. That same secrecy is easy to misuse for reasons that have nothing to do with the mission, and at Sandia, it was.

We’re also living through a moment when secrecy and a lack of transparency are getting worse generally, not better. The powerful understand that transparency is their enemy. They lean on the threat of retaliation and on legal tools like NDAs, all built to make sure no one can shine a light on the corruption, or on the people benefiting from it.

“Sunlight is said to be the best of disinfectants.” — Louis Brandeis

Add a collective-action problem on top of all that secrecy, and you get exactly the paralysis I watched play out at the lab. Often many people have partial knowledge of a problem: rumors, a handful of complaints, a pattern that looks off. But no single person has enough to act alone. Each person who might speak up carries the full personal risk, while the benefit of exposure is shared diffusely by everyone else. That’s a reliable recipe for silence. Whistleblowers aren’t just disliked. They’re frequently pushed out, sued, or frozen out of advancement, while the people who stayed quiet suffer nothing at all.

It’s very much like internal-affairs investigations in police departments. Rather than drum out the people who do a bad job, abuse their position, and bring disrepute on the organization, the organization protects them and lets them keep operating as members in good standing.

The counterpoint is brutal and consistent: if you’re the one who sheds light on the problem, you’re the one who gets drummed out, looked down on, and punished. I saw this directly. The investigatory process at the lab was oriented around protecting the lab, not around doing what it claimed to do. Corporate ethics means the corporation is ethical without regard to actual ethics.

This is corporate ethics in its purest, ugliest form: whatever the corporation wants to do is right. If you oppose the corporation, you’re the one in the wrong. It’s a might-makes-right argument dressed up in process and procedure. It ends in the same place every time: protecting the organization over protecting anything genuinely ethical. The mission quietly gets replaced by the institution’s own self-perpetuation. Once that substitution happens, anything that threatens the institution’s standing gets treated as the real threat. The wrongdoers being protected are just collateral damage. Calling it out is worse than the original crime.

“Whistleblowers are seen not as allies but as threats, because they expose the gap between what an institution claims to be and what it actually is.” – Conventional wisdom

Can it be fixed? Imperfectly, but yes, and not through better mission statements or stronger-worded codes of conduct. The institutions that have actually improved did it by giving something up, not by writing something down. They give up control. Independent investigation and discipline, run by people with no stake in the institution’s reputation, is the single most effective lever. Examples include civilian review boards with real subpoena power for police, outside lay boards for dioceses, independent ethics commissions with real enforcement teeth for legislatures, and regulators with real authority for corporations and national labs alike. The word doing the work there is independent. A review board stocked with the institution’s own alumni just recreates the same conflict of interest wearing a different badge.

Strong, enforced whistleblower protection changes the math for the person deciding whether to speak. In my case and another close friend’s, whistleblowing was an absurd prospect. It is a completely toothless option. It exists, but it is completely ineffective. If the personal cost of reporting drops, and the cost of retaliation to the institution goes up, more people with partial knowledge come forward instead of staying quiet and hoping someone else goes first. Mandatory public disclosure of misconduct complaints and their outcomes takes away the institution’s ability to absorb its bad actors quietly. It costs money and reputation up front, which is exactly why institutions fight it, and exactly why it works.

“It takes 20 years to build a reputation and five minutes to ruin it.” — Warren Buffett

Decoupling the people who benefit from the institution’s good name from the people who judge its wrongdoing attacks the conflict of interest at the root. It makes accountability systematic, instead of hoping individual virtue will outcompete a bad incentive structure. None of this is a clean fix, because the dynamics are structural, not a matter of a few bad apples with weak morals. They show up anywhere a group controls its own oversight and ties its members’ sense of self and livelihood to the group’s image.

“Those who stand for nothing fall for anything.” — attributed to Alexander Hamilton

The honest, uncomfortable truth is that each of these fixes requires the institution to give up some of its autonomy. Humans prize control, and control is exactly what institutions exist to protect and amplify. The fix rarely comes from inside. It comes from outside pressure: courts, legislators, the press, public opinion, or simply someone deciding to speak up. I made that decision myself, when the cost of staying became higher than the cost of leaving.

Zooming out from my own story, the upshot is grim. We should expect far more corruption, graft, and outright fraud in the years ahead, not less. Until we change the underlying dynamic, the cost of declining trust will keep compounding across the clergy, the police, corporations, national authorities, our government, all of it.

“Power concedes nothing without a demand. It never did and it never will.” — Frederick Douglass

Almost everything runs on trust, but nothing runs on it more than the economy. The economy depends on the faith of consumers and investors that the system is basically honest. Take away that faith, and take away a legal system willing to hold bad actors accountable, and the economy suffers. Eventually, the whole society bears that cost.

In the meantime, I’ve started seeing this behavior for what it actually is: the exercise of raw power. That power gets exercised for the benefit of the powerful, not for the good of society. The people running these institutions act with impunity, because there is no real recourse against them. Competence doesn’t enter into it. The good of society doesn’t enter into it. They get to do what they want, and nothing happens to them.

“Evil, when we are in its power, is not felt as evil but as a necessity, or even a duty.” — Simone Weil

“Once men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them.” – Frank Herbert

Recently there has been a flurry of concern about the safety of AI. This is all prompted by the obsession with the possibility that AI could cause the extinction of mankind. This is typically envisioned through a variety of scenarios. These are like creating apocaolyptic biological weapons. Perhaps even a malicious AI that decides that humans are a nuisance to be exterminated. Of course, more spectacularly, there is the famous Terminator scenario from the movie. The AI gets control of nuclear weapons and decides to wipe out humanity in one fell swoop. A handful of survivors live in an apocalyptic doomscape and resist in vain.

“The automatic machine… is the precise economic equivalent of slave labor.” – Norbert Wiener

I’ve written recently that I believe a more likely scenario is that humanity could end up enslaved by AI. This would come directly from the same sort of corporate practices we’ve seen with social media. Tech companies operate under motives where profits outweigh any other perspective. Healthy societal impact isn’t on the radar when the profit motive is completely and totally in control. In that case, the negative societal impact is merely fallout.

“The best minds of my generation are thinking about how to make people click ads. That sucks.” – Jeff Hammerbacher

One of the fallouts may be that AI will become a much more sophisticated and powerful tool. It will provide these corporations with control over humanity. Lost in all of this safety discussion is a more positive framing. We get a smattering of potential benefits spoken of by the companies, which are mostly thrown out in scattershot, ill-formed thoughts. There’s talk of medicine, cures for diseases, and universal basic income. There is little rhyme or reason. No one is asking what humanity wants or needs. All of this begs the question of why these sorts of good outcomes haven’t already been received from our tech overlords. There is zero track record of a single step toward societal or social well-being.

The same incentives that gave us Facebook and Twitter are working for AI. Those incentives will be weaponized by corporations to make more money. To make their founders and employees rich. None of the current incentives have social or societal good as a focus. Without explicit engineering of the incentive structure, we will get the same result. That result will be social and societal damage and chaos. Anyone happy with the current chaos?

“I tend to think that most fears about A.I. are best understood as fears about capitalism.” – Ted Chiang

The discussion we are not having is: What sort of future do we want, and how will AI play into that future? I think this is the flip side to the conversation we’ve been having about doomsday. Instead, we should be talking about what we’re going to get from AI, and specifically how the particular genius of humanity will be preserved. Even better than preserving our genius, how can AI augment it.

A large part of my enslavement idea comes from the belief that there are aspects of thinking and intelligence that AI cannot replicate. Aspects of humanity that are purely human. It may necessarily be found in our embodied selves. Maybe other ways of thinking that AI does not currently replicate and perhaps never will. Other breakthroughs are likely necessary to expand AI’s intelligence.

In lieu of this, one of the most important questions is whether the genius of humanity is preserved. Does it serve AI, or serve the corporations that currently develop AI and tech? This may be the path towards enslavement. An alternative is that society places value on preserving the genius of humanity. AI is engineered in a way that expands the positive aspects of our lives and produces wealth and beauty, art, and things of aesthetic value. These positive aspects have primacy over pure profit and wealth.

“Perhaps the culture of accountability that we are relentlessly building for ourselves actually damages trust rather than supporting it.” – Onora O’Neill

I can certainly see many positives in AI. One of the most positive things I could see about AI was what it could do for my former workload when I was employed. I’ve opined that, as my career went on, the load of bureaucratic garbage increased monotonically. This increase is driven by reporting requirements reflecting the overall lack of trust that has developed in our society. This untrusting attitude arose from a belief that the most likely outcome for my research was simply that I wasn’t getting anything good done. Worse yet, I was absconding with the money and fucking off with it.

I had to regularly report on everything I did, with that reporting eating up an increasing portion of my work life. In no small way, I cannot overstate the degree to which this burden ate into my productive capacity. Worse yet, the objectives and goals at work began to lower. One way to not look like you are failing is to make success a very low bar. Over the years, we’ve seen the bar drop lower and lower in order to ensure that success is engineered into every activity. This does nothing to reduce the burden of reporting.

“Huge swathes of people… spend their entire working lives performing tasks they secretly believe do not really need to be performed.” – David Graeber

AI was perfectly well suited to more seamlessly and easily generating the sorts of reports that you fed into the system. In doing so, it could free up useful time to do real work. Nonetheless, the problems are still there. We see something toxic and in need of reform in how work itself is structured. We need to change the work so that there is more trust and more capacity for people to do creative work without all the oversight. This is where the focus of AI should really be. AI should be something that can assist us in developing, running through, and executing more of our ideas.

“Move fast and break things.” – Facebook motto

This would be something that could enliven research and work to generate more progress than we are used to seeing. By the same token, ideas and innovations are the engine by which wealth and prosperity are generated. These are all outcomes we should seek. Currently, there is not nearly enough progress going on in the world, and AI could be an engine for assisting humans in finding it.

The problem is that we need to escape several things:

1. The expanding collapse of trust across society, which saps all of our energy.
2. The profit motive standing above all else, without any sort of societal responsibility, that our corporations currently exhibit.

If both of these things can be wrestled under control, the possibilities are endless. Perhaps the most important single theme to work toward is greater trust. The recent behavior of corporate high tech has only hollowed out trust further. Sam Altman is the poster boy for untrustworthiness. He is only eclipsed in being sketchy by the President himself. Rather than build trust that allows a better future, they are digging a deeper hole.

“AI will probably most likely lead to the end of the world, but in the meantime, there’ll be great companies.” – Sam Altman

Unleashing AI into a world where Altman and Trump are at the helm is almost suicidal. A superpowered intelligent tool will simply take our crisis of trust and put it into overdrive. It is courting disaster. This would be akin to striking a match adjacent to a gasoline-soaked blanket. You can see what’s going to happen, and if you go forward and do this, you should be ready for the outcome. Mindfully, we should turn toward better possibilities.

AI has shown great facility in a couple of areas that are near and dear to my heart.

1. Code writing: Over my career, I did a substantial amount of it. This is something AI has shown miraculous power at, but also limitations.
2. Mathematics: Again, mathematics has become the showpiece for AI, where its capacity, as the companies want to show it, is being demonstrated. The key is how much of this is mere mimicry or raw computation versus genuine intelligence.

“Programs must be written for people to read, and only incidentally for machines to execute.” – Harold Abelson and Gerald Jay Sussman

In both cases, the case for human control and the necessity of human intervention is clear. In coding, our key strategic and intellectual ideas are manifested in the code. The thing AI has shown itself to be most capable of is grinding through the gross, gory, and intricate details of implementations. Where it falls short is in the broader strategic thoughts needed to weave everything together. This is where humanity is essential. This must be preserved and augmented.

This is much like the story with bureaucracy. AI can simply devour the mind-numbing details, but human thought is needed to manage the overarching views, the meaning, and ultimately how to put everything together into a coherent whole. The key to making things better is to amplify the message of human control. We are in charge, and AI simply becomes an efficiency. AI becomes a way to get these awful parts of work out of the way. There needs to be a focus on genuine work benefits that people can feel and control.

“It is by logic that we prove, but by intuition that we discover.” – Henri Poincaré

With mathematics, we see this pattern repeated again. We see that the need for humans to solve problems comes from inspiration, leaps of faith, and insight. All of these are beyond what AI can generate. The recent spate of results is crowned by the Navier-Stokes-related Millennium Prize. The basic concept, ideas, approach to solving the problem, and strategy, if you will, were devised by humans. AI simply ground through details too complex and mind-numbing for humans to easily tackle.

This model is similar to the supercomputing we are all familiar with. The well-programmed supercomputer is incredibly useful. It can do calculations at blazing speed. If these are arranged and done in the right way, miracles are possible. If they are not, it is garbage. AI is no different. Human guidance and genius make sure the strategy is meaningful. Utility is found in the collaboration between man and machine. The machine alone is wasteful.

The key is that without human inspiration and invention, the mind-numbing details and all the work I could do would be for naught. Again, human control is the key. AI does not do things by itself. The failed message from the companies is that AI does everything. This is bullshit. It is damaging to the future. The real and true message is that AI can bring ideas to fruition. AI makes the impossible to execute possible. More importantly, AI does not replace people; it makes them better.

This gets to the core of the narrative that the AI companies, the US government, and the whole dynamic get wrong. They are completely fucking this up! They have only seeded fear and opposition. If AI is coming for your job, you oppose it. It is also the seed of opposition to data centers. The AI story is a public relations nightmare. The reason is that they didn’t think they needed to convince the public. Money and greed were the only things necessary.

“Imagination is more important than knowledge.” – Albert Einstein

When one talks about reform in the working world, this needs to be backed by reform in the educational system. When I’ve spoken to people associated with or working directly in the educational system, the reaction I get from all of them is a desire to put the genie back in the bottle. Somehow we will keep students from using AI and force them to learn the old-fashioned way. This is shortsighted and foolish. This genie is not going back in the bottle, and students will use AI. These students will need to use it well to survive in the future. Avoiding AI is malpractice.

“What I cannot create, I do not understand.” – Richard Feynman

All of these students will need to learn to use AI effectively when they get out into the workforce. Sending them out of school without training on how to properly use AI is like sending lambs to the slaughter. As consumers, they are destined to be consumed by these corporate overlords. The educational system needs to adapt, and fast. We need to prepare them for the World that is. AI is going to be with us. It is going to be important. Our future prosperity, health, and safety all depend on it. Students must be ready.

“Plants don’t flourish when we pull them up too often to check how their roots are growing.” – Onora O’Neill

The way I see it, AI is going to be used. The question is: are we going to be teaching people to use it effectively? Are we going to be expecting people to add human value to AI? In a very real sense, the danger is that people will turn off their thinking entirely and simply regurgitate what they ask AI for, question by question. This means the questions, the teachings, and the expectations for students need to change to account for this behavior.

“We are drowning in information, while starving for wisdom.” – E. O. Wilson

Rather than trying to put our heads in the sand and pretend that AI is not there, students need to know how to effectively engage with AI. How to produce the sort of results that we desire. It should be clear that all students are expected to put some of themselves into every solution. True even if they are using AI to assist them along the way. Our job is to guide them to make that use effective and appropriate.

We then need to accentuate and accelerate the degree to which human uniqueness, creativity, innovation, adaptation, and thought are applied to these results. In this way, we will produce students who actually can deal with the world that is before them.

“The best way to predict the future is to invent it.” – Alan Kay