Which Group Is the More Unethical? Rich or Poor? Give Up?
September 7, 2026
I read “Higher Social Class Predicts Increased Unethical Behavior.” I don’t want to be a research skeptic. I do want to point out that the findings are not news, surprising, or at odds with common sense. I know that some people who like social science-type research will be annoyed with my statement. Some may launch well-reasoned Don Quixote assaults on my little windmill. No problem. I understand: Grants, tenure points, and peer love at a specialist conference. I get it.
What’s missing from the write up from my point of view is linking the research to behavior in a specific sector. How about Wall Street tycoons and tycoonettes with the right stuff? Or, why not take a look at the modern-day Platos posting screeds on X.com and other online media? None of that makes it into the serious research writing. Let’s have some fun then. I want to apply a couple of the paper’s findings to a couple of the BAITs (that’s my lingo for Big AI Tech).
Here is the “official” abstract of the research paper:
Seven studies using experimental and naturalistic methods reveal that upper-class individuals behave more unethically than lower-class individuals. In studies 1 and 2, upper-class individuals were more likely to break the law while driving, relative to lower-class individuals. In follow-up laboratory studies, upper-class individuals were more likely to exhibit unethical decision-making tendencies (study 3), take valued goods from others (study 4), lie in a negotiation (study 5), cheat to increase their chances of winning a prize (study 6), and endorse unethical behavior at work (study 7) than were lower-class individuals. Mediator and moderator data demonstrated that upper-class individuals’ unethical tendencies are accounted for, in part, by their more favorable attitudes toward greed.
It’s suitable for my purpose. I want to run through a research “finding” and then relate that to a BAIT humanoid or a cluster of BAIT humanoids in a coffee shops like Coupa Cafe or Philz.
ITEM 1. Elite drive up in snazzy rides, park in places the driver designates is suitable for his/her vehicle, and drives with flair, slow mode to be noticed and fast mode to be cool. Application: Observe employees with expensive and exotic vehicles leaving a parking lot. Research required: Open your eyes and look.
ITEM 2. Elite make decisions based on the rules and the law accepted or defined by the elite’s world view. A group of elites will, if there is disagreement about how to maximize a financial payoff, the group will compromise on methods that produce as much money as possible without [a] getting caught, [b] being fired assuming leadership cares, [c] being ostracized by other elites, or [d] being killed; e.g., the former Googler who became unalive as a result of a paid escort’s injecting a controlled substance into the wizard. (Bummer and a PR problem). Non elites (you know who you are) have an ethical compass calibrated differently. A person who volunteers to work at a food bank is unlikely to snap into the ethical architecture of some Silicon Valley BAIT professionals. That’s a generalization, but it is close enough for horse shoes like AI marketing I think.
ITEM 3: Lie and cheat are useful words. In the context of a group of elite AI professionals, what a less-than-important humanoid might label a bad way to act is irrelevant. I would hypothesize that the BAIT elite view deceptition and cutting corners, among related actions, as the only way to behave toward those in the not-elite set. One does not amass vast sums of money without a little bit of fancy dancing. I suppose it is more accurate for me to say, “A Cirque du Soleil-style performance” at a minimum.
The research is useful because it provides what some people would describe as “expert witness output.” That’s okay, but I think one need only look at the behaviors of the BAITs and one can assemble a comprehensive set of data about elites in this Silicon Valley metaphor.
If you doubt me, think about the circular financing, the litigation among the BAITs, and the Looney Tunes-type marketing about systems that generate errors, make up information, and refuse to talk about the real winners from AI. Whom? you ask? Online cyber criminals. For these folks, AI is a gift from heaven, and the ROI is good now and only going to be better when the agentic online crimes ramp up.
Net net: Abstract research is not as much fun as observation. Watch lab rats. Write down what you see. After a couple of days, you have nailed the behaviors.
Stephen E Arnold, September 7, 2026
AI-Yi-AI: We Know Where This Is Heading or Gimme Some Moore
August 28, 2026
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
Every once in a while I read an output from Semi Analysis (please, don’t confuse this consulting firm with Artificial Analysis). I have learned that my blood pressure ticks up, and I need a break to water the flowers. “Are Open Models Catching Up?” The assumptions underlying the write up, in my opinion as an official dinobaby, are:
Assumption 1: The US is the world’s leader in proprietary closed (proprietary) frontier models
Assumption 2: Open models deliver results on benchmark tests that show the frontier faces an encroaching suburbia of smart software
Assumption 3: Assume that the US will be the big winner in opening up the Wild West frontier of artificial intelligence
Assumption 4: Writing blue-chip thinking and giving it away for free will generate sales leads
Assumption 5: Open models have to catch up. (Spoiler: No, open models only have to be good enough, available, and cheap.)
You and Semi Analysis won’t agree, but just for fun, suspend your doubt, and let’s think about what is happening in the “if we build it, they will come” world of smart software.
The write up says:
The past two months have been a breakout period for open source AI. Yes, there was the “Deepseek moment” back in January 2025, but no one actually used R1 to do any economically valuable work. In contrast, models like GLM 5.3 and Kimi K3 are genuinely capable of many of the same coding and agentic tasks that rocketed Anthropic to $65B+ ARR. Unlike others who inflated ARR, our figures were much closer to reality.
Let’s accept the statement about “no one actually used R1 to do any economically valuable work” and “our figures were much closer to reality.”
The “killer chart” is this one:
Sure, it is black and hard to read. The idea the chart is supposed to make is that Semi Analysis has discovered a chart that someday soon will look like this one from Wikipedia:
By golly, Semi Analysis and “their figures were much closer to reality” appears to suggest that open AI models are improving in the same way chips were. The swizzle in this some Moore law is that the open models are improving from “below.” The idea is that open models are catching up with the frontier pioneers’ trailblazing innovations.
But wait! The future of smart software is agentic. That means that new players will enter the fray and use the existing models as an installed base of capabilities. Agents will become the next big thing. Is this a new insight? My answer: Nope. Agents are the next big thing. If the Semi Analysis-type thinkers take a look at what outfits like Alibaba have been doing for months, the progress is rapid. Depending upon what a user wants to do, one can look at Alibaba, its deal with Google, and the distributed innovation method in use. The conclusion could be phrased this way: “Let the open models catch up. We will use wrapper software and agents to create a different type of system and user interaction method.”
Why is this important? In my upcoming Ciffer lecture about the future of online crime, I point out:
- Open tools and systems are good enough
- Improvement in these tools and systems allow bureaucracy-free actors to select, test, discard, learn, and use what works
- Cyber security professionals have to figure out what has happened, pinpoint the agent, and the figure out how to prevent the action from taking place again.
Bad actors have been tapping LLM capabilities since the systems became available. This means that the assertion the early open models were not economically viable. Sorry, Semi Analysis, your semi analysis is dead wrong. You are looking at the corporate sector which is still trying to figure out a use case that delivers a payoff. The open models work just fine for the ransomware folks, the data suckers, and the systemic disruption people.
What’s the future look like? Unlike Semi Analysis I don’t have a big orange button so you can buy the answer to the question. I will go through what’s coming from within the US and from non-US players at the Ciffer event. Let me hint at what’s happening: Frontier models are struggling to improve. Open models are good enough. Economic evidence that open models work is available. But that’s not the story. The nascent attempt to create a Moore’s Law for AI has to look beyond what BAIT outfits are doing (BAIT is my lingo for big AI tech and “ai tech” rhymes with “train wreck”).
Stephen E Arnold, August 28, 2026
Google Goes 1930s Hollywood : Lights! Camera! Manny Take Cash and Get Her to Sign a Contract
August 26, 2026
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
Ah, Google has embraced the spirit of 1930s Hollywood. Louis B. Mayer of MGM fame land Mickey Rooney, Spencer Tracy, and — take a deep breath — Clark Gable. He’s the “frankly, I don’t give tweet” guy. The logic was simple. Pay money. Sign up the big names. Lock the talent in. That lion logo captures Louis’ spirit. Greta Garbo, Joan Crawford, Jean Harlow, and the Marx Brothers felt at home in the MGM den. The lion himself allowed Heddy Lamarr to research a wireless network innovation for which she obtained US Patent 2,292,387. In a gesture of politesse, the co-inventor was George Antheil, not Louis B. Mayer.
Thanks, MidJourney. Good enough.
“YouTube Starts Funding Shows Directly to Keep Creators from Licensing to Netflix” provides information I find quite suggestive. I want to come back to Louis the Lion and the Heddy the Inventor (neé Eva Maria Kiesler) at the end of this blog post. Select your frequency and tune in, please.
The write up explains that Google’s push to sign up star talent is just getting started. The article asserts:
The offers reportedly take three forms: direct financing for a creator’s shows, a share of the platform-wide brand deals YouTube negotiates with advertisers, and upfront cash. In exchange, YouTube wants windows in which the work stays on YouTube alone. That is a departure for a company whose creator relationship has, for two decades, been an ad revenue split and very little else.
In the good old days before building copycat versions of Henry Ford’s River Rouge, Google allowed people to upload their videos to YouTube. Then Google did its magic and some received oodles of money. Others received nothing. Applying the old Silicon Valley money algorithms, Google kept the advertising cash and remained loosey-goosey about how the money was split. Some lucky creators were hits. Others were flops. Some were sort of hits and then ran into trouble with Mother Google and their traffic and money vaporized. I translated these tales of YouTube financial frippery as “It’s not Nice to Fool Mother Google.:”
Netflix has a different model which the cited article explains. The key question I had was not addressed; that is, “What is Netflix going to do as Google buys talent?” Keep in mind that YouTube and Netflix allegedly have about the same annual revenue. However, YouTube is just a component of the Googzilla operation. (Yeah, that’s my pet name for Mother Google.)
I don’t know anything about Netflix. I know a tiny bit about Hollywood because I read “The Day of the Locusts” years ago. Based on this non-AI foundation, the answer to my question about what’s Netflix going to do is Netflix will have to deliver more cash, more upsides, and more creator love than Google. Sunset Boulevard may be a dismal place now, but if YouTube and Netflix follow the path of the old-time moguls, Hollywood may come roaring back. Big money means production. Production means specialized needs. I bet you wonder what a key grip did, right?
My horizon thought is that Hollywood is not dead, it just has a new cast of characters, new people with deep pockets, new needs for specialized services, and new guys who broker deals. Maybe the German in Venice will document the rebirth of the old Hollywood?
August 26, 2026
The Golden Age of AI Hollywood Is Arriving
August 25, 2026
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
One of my team sent me a link to “‘I See the Incredible Promise’: On Set of an AI Film Shoot As New Studios Embrace Controversial Tech.” I noted this statement:
Backed by Google, Silicon Valley venture capitalists and Disney, Promise is one of a rising number of AI film studios now taking root in Hollywood, with the result that traditional studios’ vast sound stages and financing are becoming less necessary.
First, we have a new studio. Second, there are others. Third, the old-school sound stages may play a lesser role or no part in next-gen AI flicks. And, finally, the financing has changed.
Thanks, MidJourney. Good enough.
That’s the big point: “Film” or “motion pictures” produced with smart software are for now cheaper than the big budget woofers like Heaven’s Gate with its $20,000 a pair authentic roller skates. AI roller skates output by a Chinese model like the one running on the computer I am using to input this blog post cost nothing. Sure, I had to have a decent PC, but the software and harness? Free for now.
The essay quotes Joel Humek, an Oscar winner, as saying:
“When we went from optical to digital film, we thought we’d gone to heaven,” he told the Guardian. “It’s like that again. So many things are easy to do. I am an embracer of AI. Some people are wary of it, a little afraid of it, but I see the incredible promise.”
I interpreted the statement as meaning: More creative free, more easily. When “work” is hard; it is expensive. That means that one can interpret the four points I called out are now being pulled into the orbit of “cheaper.” And that’s the point. Getting money for a less expensive video that could make money from the non-Hollywood channels may be appealing to some funding sources. The exceptions are Hollywood old timers who want to “own” talent, distribution services, theaters, and concessions. Now the economics (and don’t forget the creative freedom) are different … for now.
I noticed this statement in the essay:
George Strompolos, a co-founder and the chief executive of Promise, estimated that hybrid AI films – human actors alongside AI technology – would prove 20% to 50% cheaper than conventional production. That could free film-makers from requiring major studio investment.
Gee, this “cheaper” theme seems to be like the bass beats in a Venice Beach two-person rap performance.
I urge you to work through the write up. Ignore the creative freedom sprinkles. The real message is that the “old” Hollywood methods are not yet applied to the AI-infused “new” Hollywood. The reason AI is embraced by some creatives is that it is cheaper and less dependent on the “old” Hollywood.
I want to point out that one feature of the “old” Hollywood will be its accounting methods. If you have time, you may find exploration of “Hollywood accounting methods” interesting. I want to point out that the AI software that produces the digital yummies for those who embrace the technology is available, some software is free, and other software is subsidized by those making what the creatives use.
How long will making motion pictures the “new” way be cheap? That’s a question to consider. The roots of the “old” Hollywood are deep even if the major studios are in a kettle of piranhas at this time. The spirit of the “old” Hollywood and its DNA is part of making videos.
Can the “old” Hollywood adapt and control the new production companies? How quickly will the “new” Hollywood movers and shakers remain footloose and fancy free? My guess is, “Not too long.” Why? When the cost of the software goes up, the “old” methods may become useful and “new” again. Right, Mr. Zanuck?
Stephen E Arnold, August 25, 2026
Yat Siu: Morphing a Stradivarius into a Crypto Token with Web3 Magic
August 19, 2026
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
In our Telegram Notes online magazine, we presented Yat Siu and his Stradivarius violin. The article is “Yat Siu’s Web3 Violin Performance.” What’s interesting is that buying a Strad is rarely a PR event. When one spends millions for a relatively small musical instrument, publicity can attract bad actors or their hench people. Siu’s violin is the Empress Caterina, and it is allegedly on of the best of the Strads in existence. For collectors of rare violins, Siu has a good one. In our Telegram Notes’ write up, you will learn what Siu did as soon as he took possession of the Strad. Spoiler: He turned it into a version of the Bored Ape non-fungible token. How did this happen? An outfit named Galaxy has a tokenization machine. Shove the notional violin in one end and out pops a token. The next step was to hit the Go button on a PR machine. Can you view the Empress? Yes, but you have to be a musical luminary or a wealthy Web3 savvy person. The violin is in hock and held by a trusted intermediary, and there is a procedure to follow. How did this violin performance work out? Check the Telegram Notes story. No paywall. No ads. Just an instrument and a score.
Stephen E Arnold, August 19, 2026
Google: Everything Is Coming Up Roses Except ….
July 27, 2026
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
Stories about Google’s second quarter revenue are ubiquitous. The numbers are good, certainly better than those for most businesses. (How’s that mall in San Mateo doing?) Revenue up 24 percent. Cloud up 82 percent. (Compare that to the powerhouse Alpha Compute, please.) Services up 15 percent. YouTube ad revenue up 13 percent. Those wonderful Google everyday ads are up as well.
A distraught soccer (football) player scores an own goal. Thanks, MidJourney, good enough.
I want to point out that Google chalked up another first. CNBC, that bastion of financial reporting, said in “Alphabet and Tesla Test Wall Street Patience As AI Spending Overshadows Growth”:
Alphabet and Tesla both reported negative free cash flow in the second quarter.
Yep, Google has a negative cash flow. If one ignores the move from the dormy Backrub to the garage era, the negative cash flow is a Google first. What caused it? A lack of wizards? Nope. A failure to think like a big time, blue chip consultant? Nope. Employee protests? Nope. Legal fees? Nope.
The answer is, “AI.”
Several questions:
- Given Google’s revenue generation capability, why has the firm decided negative cash flow is okay for AI?
- What happens if and when one of these actions takes place? [a] A breakthrough in the AI systems and methods that sharply reduces computational demand; [b] Another country dumps advanced AI and the US continues on the path of expensive AI? [c] AI creates more economic drag than efficiency in certain key business sectors?
- What happens if one of Google’s revenue engines falters due to broader economic weakening or an alternative marketing platform gains traction?
- What happens if Google’s “innovations” are unable to perform at the levels other firms’ AI solutions reach?
- What happens if this “we have to win so do whatever it takes” approach does not pan out?
I am a dinobaby. Some people do not understand how I think. Google, based on my research for my three monographs about the company, is not an innovator. Google is a me-too operation. That means that batting 1000 or scoring in every soccer match is unlikely.
Thus, negative cash flow could be an own goal. Is this be a Messi moment?
Stephen E Arnold, July 27, 2026
Data Centers Might Not Pay Their Fair Share. Might Not!
July 17, 2026
There are many complaints about data centers, including that they consume too many resources: electricity and water. Communities are concerned that essential resources will be depleted in pursuit of AI. The Conversation investigated data centers and if, “It May Be Almost Impossible To Make Data Centers Pay Their ‘Fair Share’ Of Electricity Costs.” No one is sure how data centers costs will be calculated and what the long term effects will be. PJM Market estimated the amount of power data centers will use and concluded that demand will cost consumers $23 billion that will last until 2028.
How are prices for energy determined?
“First, regulators identify the costs that a utility company incurs to provide service. Regulators look at the value of the assets the utility company invests in, such as power plants, transmission lines and substations, as well as its day-to-day operating expenses, such as salaries, fuel, replacement parts and electricity it purchases from other sources. Then these costs are allocated to categories of customers, such as residential, commercial and industrial.”
It is ideal that consumers pay for the amount of energy they use. There are other costs that go into estimating cost such as securing additional electricity sources, substation upgrades, etc. In these cases, the costs are usually shared among consumers. Peak demand, when consumers draw the largest amount of energy from the grid, is another collective way to estimate costs.
Data centers are different. Using computers they can adjust how much energy they use from one moment to the next. Consumers can’t do that. It means that…
“Their flexibility means data centers may be able to learn to predict when system loads will peak and consume little to no power in just the right period to avoid contributing to peak loads, as has happened with cryptocurrency-mining operations in Texas. So when regulators look at their usage to determine prices, data centers may be able to avoid paying any costs allocated through coincident peak demand, even if they use large amounts of electricity at other times.”
Consumers need to be aware about the risks associated with data centers and advocate for themselves. If they don’t, they could be left paying the bill for data centers.
Whitney Grace, July 17, 2026
Are The AI Accelerationalists Getting Nosebleeds?
July 9, 2026
Bloomberg reports that, “AI Is Making Silicon Valley Productive, Anxious And Afraid to Log Off.” We thought AI was supposed to make things easier, but apparently not. AI wizards are logged into their computers 24/7, even when they’re asleep. They are afraid that AI could advance without them and it has created severe anxiety:
“‘The Fortune 500 is having a collective panic attack,’ says Writer Chief Executive Officer May Habib, in an interview with Bloomberg News during an AI conference in San Francisco. ‘The stress, the anxiety, the inability to make decisions given how fast the space is moving and the fact that nobody wants to make a big career-limiting mistake. We are definitely seeing that.’”
Silicon Valley workaholics have been romanticized, but long gone are the days of long hours and big papyrus.? ? It is now a relentless race where sudden gains can mean big payouts for companies and huge losses for people who are deemed replaceable or useless. The technology is advancing so fast that the AI wizards can’t keep up.
The AI wizards aren’t the only people experiencing burnout. What is happening is that jobs that have incorporated AI are forcing their employees to do more high level tasks. There is less downtime with low effort output and it is leading to more burnout.
There is no downtime in the AI industry. A single moment could mean that something is missed: a deal, a new breakthrough, the next evolution of computing. This is leading to extreme anxiety and more burnout than past tech booms in Silicon Valley.
However, it still is another tech boom. People are making choices and they are dealing with the consequences of them. Why are they so insecure about missing the AI moment? One answer might be, “These bros are frightened that their marketing department promised that which is impossible to deliver.” There are jobs available; for example, the local lawn services are looking for lawn mower operators.
Whitney Grace, July 9, 2026
EU, Let Me Introduce You to the Concept of Lock In. Lock In, Meet the EU.
July 7, 2026
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
Let’s go back in time. When a bank wanted a computer in Europe in the early 1960s, whom did those savvy cats ring up? Sperry Rand, Univac, Honeywell? Answer: IBM. Why? One call did it all. Banks liked that approach. Visit a big bank today and ask to look at their computer facilities. What will you find after some poking around? Answer: Some IBM machines. That’s lock in.
Yep, lock in works. Thanks, Midjourney. Good enough.
I want to highlight this old fashioned idea in the context of “European Digital ID Wallets Are a Gift to Google and Apple.” This write up reports:
European governments are rolling out digital identity wallets, which are to be used by citizens to access services, and to verify their age online. As reported by Follow the Money and Android Authority, there is a serious problem with this: these wallets rely on safety services of Google and Apple. These are known as Google Play Integrity API, and Apple’s Managed Device Attestation. Such safety services (known as “remote attestation”) are used to ensure that wallet apps run on hardware that is not tampered with. In this article we explain why the EU-wallet case is part of a bigger problem: by embedding these safety services in public infrastructure, Europe risks making society dependent on private companies while serving their corporate interests. Here is the problem: Google’s Play Integrity API is not just a security feature: it is reinforcing Google’s control over the Android ecosystem.
Keep in mind that the EU wants to reduce its dependency on US technology. If the information in this write up is on the money, Apple and Google are in the money. There a couple of Chinese outfits willing to help the EU out. However, I assume that when one figures which big tech outfits are likely to be slightly more supportive of the Old World, the decision is easy. Hey, we love those red, white, and blue shirts and hats.
If true, this lean to the Apple and Google systems are good news. From these identity and payment acorns will grow some large invasive kudzu vines. The end game, in my opinion, is for Apple and Google to become the new financial system for the countries that are in the foxhole with US big technology companies.
Explanations of this future from these two firms will be firm. I think the companies will say, “We would never ever do that.” However, part of the freneticism of the crypto crowd is that US big tech firms will do exactly that. Once in the payment systems, the companies are going to be like that big IBM in a European bank in Switzerland. The gizmos are not going away any time soon.
Allow me to offer several observations to further disrupt your thinking about the significance of this alleged EU action:
- Apple- and Google-type outfits do not give back digital ground once it has been captured
- Apple- and Google-type outfits operate as countries and expect their decisions to be accepted, not discussed and modified by a committee of people not trusted by the leadership of these outfits
- Apple- and Google-type outfits understand that online services naturally lead to monopolistic controls; therefore, the ubiquitous mobile gizmos running these firms’ software have performed a robber baron play in full view of many informed people.
Say hello to lock in. How easy will it be to de-Apple or de-Google one’s digital life if the European digital ID wallets kick in? What’s ironic is that the beleaguered Pavel Durov of Telegram fame was correct. An alternative global financial system was a good idea. Who will give this idea some legs? Those firms are the ones to watch.
And lock in? Great stuff.
Stephen E Arnold, July 7, 2026
If an LLM Falls in a Company, Does the CFO Know?
July 3, 2026
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
The title of this essay in Shift Mag intrigued me: “CTOs Agree: Cognitive Debt Is the New Technical Debt.” Since technical debt is a bit abstract for the accountants, lawyers, Wall Street wolves, and their genetically-related bros — now there is a new “debt” about which a “leadership type” can fret. Unlike technical debt which is easily ignored, the cognitive debt may come with an invoice.
The write up captures this new debt with this headline-scale statement:
The CFO has no control once they’ve signed the contract over what the actual investment is going to be. And if you can’t tell me the investment, what does projected return even mean?
The context for this statement is smart software that cranks out code, engineering solutions to problems, or legal mumbo jumbo complete with made-up citations. But that software in most cases comes with a subscription or a license. Instead of a fixed annual amount or an agreed upon monthly fee, the cost of an LLM is a “token.” This is not an old fashioned New York subway token that leaves one fingers grimy with Hantavirus-related goodies, this token is notional. It is not a thing; it is part of the taxi meter pricing scheme that BAIT (big AI tech) outfits have embraced in a somewhat interesting decision to try and stop tossing cash into a blazing dumpsters behind an Ollie’s discount store and data center.
The rhetorical question in the quote is going to be difficult for the BAIT outfits, the blue chip consulting firms with slogans like “We’re AI experts,” and the customers themselves to answer. The write up makes another interesting statement:
…organizations that bought the tools, signed the contracts, and then realized there’s no financial model inside the company to manage what comes next
Okay, no big surprise. Technology. The alleged next big thing. Everyone is doing it. Figure it out.
How’s that working out?
The write up reports:
The more pragmatic response, shared by more than one team: stop the free-for-all, start standardizing. This doesn’t mean we’re telling people to use AI less, but nudging from “use everything” to “use the same things, smarter.”
What are engineers doing to stay ahead of cognitive debt? Actually not much. The write up says:
The most interesting take came from someone who’d shifted their interviews toward code review rather than coding, precisely because that’s what engineers actually do now. I changed our interview process to focus on code review, because that’s what we’re actually doing. And implementation is now AI-assisted, however you choose to use your agents. If your team can generate code faster than it can review it, you have a bottleneck. The constraint is human judgment, not output.
Where does informed judgment originate? Yeah, no answer.
The article does bring up a human question that smart software has not yet de-hallucinated; to wit:
One company ran an anonymous survey and found 90% of engineers at that organization actually want to use AI, higher than expected. What surprised him wasn’t the enthusiasm but what came next: questions about performance management, about promotion criteria, about how individual contribution gets recognized when anyone can now generate code. The adoption had outrun the enablement.
My hunch is that once some metrics are slapped on AI code generation and the code is “good enough,” the human engineers or at least most of them will be given an opportunity to find their future elsewhere.
Thus, the problem:
Cognitive debt is the new technical debt. The room [the AI coders participating in the research session] had seen the same pattern: teams adding features at a pace that would have been impossible two years ago, now dealing with the maintenance overhead that comes with it. Legacy code that was already hard to understand is now harder, because the people who wrote it aren’t being careful. They’re being fast. And internal tools that were never meant to be permanent are now permanent because someone shipped them with three prompts.
Do those wild and crazy leadership people understand what problems the code may toss up in the future? Nope. Leadership thinks about staying out of the weeds, not getting in the weeds.
With the cost of verifying AI generated code looking like a significant expense, the write up says:
One practical suggestion that came out of this: invest in evals now, not later. The cost of building AI features isn’t the hard part. The cost of verification is. If you build a solid eval suite today, you can swap providers, survive model deprecations, or move to open source without starting from scratch.
That sounds great, but to leadership who want to invest in AI in order to reduce costs and add efficiency, spending an unknown about of money on institutionalizing human code reviews means one thing: Let AI do it.
For me, we have now surface the AI problem: Code is cheap. It may be okay, but someone with “judgment” should check it out and fix an error if one is spotted. But as more code is generated, can AI check itself? Can an organization figure out how to tap into humans with the requisite skill set? Dumping expensive engineers is easy. Spending an unknown amount to make sure the code does what someone hopes it will do is another form of taxi meter pricing. It seems to me that technical debt increases as cognitive debt rises as well.
What’s the fix? How about this concluding statement from the essay?
The practical advice: build internal UI wrappers over generic model APIs now, before your teams are locked into specific product interfaces. It’s cheap to do, and it means you can swap the model underneath without rebuilding the interface your teams depend on.
What?
Stephen E Arnold, July 3, 2026
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