Yesterday I argued that AI will make human attention and judgement the scarcest resource in enterprise. Baseball already ran that experiment.

The first change is the one everybody knows. Billy Beane had one of the smallest budgets in the league. Payroll was the scarce resource he needed to allocate. He used data to find the players the market was mispricing because other team’s scouting departments made decisions on intuition and feel. He used this edge to make the playoffs four straight years.

This edge was soon eroded. Before the book even came out, Oakland’s front office staff were recruited by other teams. By 2004, teams were paying full price for on-base skill as quantified by analytics. Eventually, every team had an analytics department. The data, the models, the analysts all became commodities.

Data got cheaper and more plentiful. The stadiums were equipped to track everything that could be tracked. Today, every pitch, every swing, every spin rate is tracked and shared with all 30 teams. But a hitter can’t think about forty metrics in the batter’s box.

That’s when the second change began.

Teams recognized the competitive edge had quickly moved. Every team had an abundance of commoditized information and established analytical methods. The advantage became a team of humans capable of effectively distilling and executing on this information. The bottleneck moved from information to human attention and judgement.

So teams did something counterintuitive: they hired more people. Some teams now carry three or four hitting coaches, and their job is to translate the data into something a player will trust and act on. A manager can only have so many conversations per day. At every level, for every matchup, it’s someone’s job to only pass along the insight that matters.

That’s the version of the Jevons Paradox I pitched yesterday, playing out on the diamond. Cheaper data didn’t reduce the need for human judgement. It multiplied it.

Every company is about to go through both changes. But most may only be looking at the first. Building an advantage there will be short-lived. The second is the one that decides who wins.

If enterprise success has always come down to allocating scarce resources efficiently and effectively.

and if AI is making a whole class of once-scarce resources, such as analysis, code, content and expertise nearly free at the margin;

and if access to non-frontier models is becoming a commodity;

and if your competitors have access to the same intelligence as you do;

and if the same models, in two different companies, will only product different results depending on who’s asking the questions and who’s judging the answers;

and if the cost of AI output continues continues falling while demand for inference continues to rise (the mainstream view on Jevons Paradox);

and if more analyses, more drafts, more code, more agents are doing more things;

then human attention and judgement become both your only differentiator and your scarcest resource. This is the real Jevons Paradox.

The job of management hasn’t changed. It’s still resource allocation. The winners in the AI era won’t be the companies with the best models or fewest employees. They’ll be the ones that figure out how to deploy human attention and judgement most effectively.

You’ve probably heard this fun fact: giant ground sloths were responsible for spreading avocados because they were able to eat the pits and later disperse them miles away - something smaller mammals couldn’t do.

Just one problem. This is likely untrue!

This “fact” began its life as a speculative mention in a journal that slipped past peer review. But since then, it has been cited and mentioned so many times that the frequency alone has become an indicator of truth. It’s repeated in articles, podcasts and even by scientists. The very excellent educational YouTube channel SciShow recently published a correction episode explaining this mistake.

Right now, your engineering team is celebrating because they just plugged an LLM into your corporate knowledge base.

But if you work in AI Governance or Risk, you know your SharePoint is full of sloths: deprecated policies, draft memos from decades past and unvetted Slack opinions.

Here’s why that’s a problem, as outlined in the Crossfactor Atlas

1. Unearned Warrant - A Monday morning brain-fart from a 3-year-old project channel gets retrieved by your AI. Because it is being served up by an “intelligent” system, without any of the original context, it is granted unearned warrant. It is given the same corporate weight as formal, reviewed documents the system also queries.

2. Surface Credibility - Because the AI outputs obsolete information with perfect grammar and authoritative formatting, your employees blindly trust it. The cleanliness of the output hides the messy nature of the input.

3. Authority Capture - The AI’s output gets included into a new reference document. The document gets included into a new SOP. The dirty AI output has now accrued the ultimate trust and citational weight faster than your Governance team can establish accountability.

Your LLM doesn’t need to hallucinate to poison your institutional knowledge.

Does your team have the vocabulary to find these hidden failure modes?

(P.S. I map out over 200 of these exact sociotechnical risks in the Crossfactor Atlas. DM me if you want a copy).

We keep selling AI as a speed upgrade. Same work, done faster and cheaper.

But that’s not reality.

AI changes the shape of the work. It changes the distribution of human tasks. It removes some and creates others. It changes where information comes from and where it goes. It changes how judgment is used.

When you change the shape of the work, you change the shape of the failure.

The old organizational failures were loud. A missed deadline. A bad hire. A recall.

We spent the past two decades building dashboards to make them all painfully visible.

The new failures are quiet. They hide inside fluent, confident prose. They run in the background while slowly drifting. They plant hallucinations that won’t surface for twelve months.

They are also faster. AI can be scaled quickly within an organization. Everything it touches can become a surface for failure. Nothing short of a CEO has ever had this much reach within an organization.

And they are ownerless. When a person made the call, accountability was clear. But when the output is assembled from a variety of automated processes, only half-reviewed by humans, it’s not clear who is actually responsible.

So here’s the problem: every organization still measures itself by the old failures. The ones with dashboards and clear lines of accountability.

But the new failures don’t have dashboards yet. You may not even know what to call them.

Every email has a cost.

It costs something to generate, distribute, and store emails. These costs are understood and easily measured.

The cost of the email server is trivial. That was never the real cost of email.

The real cost is the cost of reading it, assessing it, acting on it, or choosing not to act on it.

This is the cost of human attention. There’s no dashboard for it.

Some organizations figured email out. Not by buying better servers. They did it by building discipline around the human side. Norms were set about when to send or forward and when not to. Rules about who actually needs to be copied. Cultural permission not to respond to everything instantly.

They managed the expensive, human component instead of optimizing the cheap part.

Right now, everyone is measuring AI by its IT costs. Tokens, compute, latency. The numbers on invoices and dashboards. But the organizations that will win with AI will be those that build discipline around the human and team elements.

When servers are cheap, it’s easy to let email bury your people.

The real cost of AI will never be the inference bill. It will be the cost of human attention, collaboration and judgment. These are the things no dashboard measures and no models replace.

Congratulations on your new AI rollout. Your team is now quickly becoming complacent instead of using their judgment.

Which of these AI solutions will bring you brand-new problems?

1. Prompt engineering.
2. Human-in-the-loop.
3. RAG.
4. API safety guardrails.
5. Model evals.
6. Red teaming.
7. Risk frameworks.
8. Dashboards.
9. soTA models.
10. Agents.
11. AI upskilling.
12. Copilots.
13. Context engineering.
14. Governance Committees.
15. Fine-tuning.

Don’t get me wrong; these can all be valuable.
But they all have problems.

Leave a comment if you disagree or think I missed something.

Check out the free 5-minute quiz on my profile to see where your team stands.

Follow me to learn why AI creates new problems for teams.

Despite the buzz and news releases, you shouldn’t run to build with the latest models.

Yes, they are fun to play with. No, you should not use them for serious work.

Here are three reasons why (how so very LLM-esque of me!):

First, SOTA models are the most expensive and the slowest. This means the moment someone figures out how to do the same thing with a cheaper model, or a faster one, you’ve lost your advantage. Jeff Bezos was famous for saying that “Your margin is my opportunity”. Margins exists both in terms of token costs and UX (in this case latency).

The best operators know that the best inference is the one that happens for free on the user’s device with near-zero latency.

Second, the areas where SOTA models offer improved functionality are, by definition, the bleeding edge. But this edge is brittle. This is where things break. You are now including failures into your operational domain. You might be able to make a case this is manageable. But whatever you do to fix it, it will quickly be undone by the next model update.

The best operators know that reliability is not found at the bleeding edge.

Third, organizations reach for SOTA models for the most complex workflows - presumably to augment or replace humans. LLMs in enterprise may be new, but history keeps teaching the same lesson about automation. It may at first seem straightforward to automate the visible but then soon discover that the real challenges lie in the context around it. But LLMs can be every more damaging to an organization because they can proliferate quickly and are non-deterministic.

The best operators know that an organization’s best human judgment becomes inaccessible once you automate away the context it relied on.

SOTA models won’t help you create a lasting advantage. They cost more, are slower and invite use cases that are detrimental to an organization’s long term health.

That’s what Ford just did. They should have learned from GM.

In the 1980s, GM poured $45 billion into factory automation in an effort to catch up with Toyota. Then CEO Roger Smith said “Automation came along just in time to save us”.

It didn’t. GM’s market share collapsed. Meanwhile the automated factories ran at 50% capacity.

Ford just rehired 350 experienced engineers after failing to replace them with AI.

The press is painting this as a story about AI failing, as a technology. It’s not.

This is a story about adopting a technology without first identifying its hidden failure modes.

VP of Vehicle Hardware Engineering, Charles Poon, admitted it:

“Mistakenly, we thought that by just introducing artificial intelligence and adjusting the design requirements that we had, that that would produce a high-quality product.”

A GM factory automation engineer who lived through it said it simply:

“…all you can do is automate confusion.”

Ford amplified weak inputs with AI. GM automated confusion. These are the same multi-billion dollar mistakes.

As AI proliferates in your organization, so are the weak inputs, the confusion, as well as a variety of other problems setting you up for expensive mistakes. Find them before they find you.

Twenty years ago, developers wrote code, threw it over a brick wall to Operations, and went home. Any efficiency gains were localized. The systemic friction was intractable.

Then came DevOps.

We look back and think DevOps as a tooling revolution:

-CI/CD pipeliens
-automated testing
-DORA metrics

The real triumph was taking a Human Factors Engineering lens to a processes filled with…humans.

DevOps succeeded in admitting a hard truth: the human brain is the most fragile component in the pipeline. Entire workflows were redesigned around human limitations.

We stopped asking humans to execute 5-step deployment runbooks at 2 AM, because human working memory is fallible (especially under fatigue and sustained stress!)

We build pipelines that failed in three minutes instead of size months, because tight feedback loops match the biological speed of human learning.

We confronted the Irony of Automation - the fact that automating routine tasks degrades human skill and destroys situational awareness. Chaos Engineering and Game Days serve to reverse this by deliberately breaking our own systems to keep our human operators sharp.

But now look at AI in the enterprise.

Marketing, HR and Sales are prompting bots, generating massive outputs and throwing them over the wall for someone else to review.

Individuals are getting faster, but we are completely ignoring human factors engineering in complicated processes and workflows.

We ask managers to review 50-page AI-generated strategy documents, pretending they have the attention span and time to catch the silent hallucinations. They don’t.

We sever the feedback loops of learning. A junior employee generates a complex financial model in seconds. They don’t struggle. They don’t hit a wall, they don’t learn.

We are doing all of this learning nothing from the Irony of Automation. We let AI handle all the daily cognitive lifting but expect the human to step in flawlessly when the system makes a catastrophic judgment call. It just doesn’t work like this.

DevOps didn’t win because it gave us better tools. It won because it forced us to build workflows that respected human factors engineering.

Uncoordinated AI adoption is creating the largest operational blind spot in corporate history because it’s forgetting human psychology exists.

Your team is juggling context windows.
Worrying about compaction.
Switching models.
A/B testing prompts.
Trying to keep track of what tools are being called.

That is not AI’s promise, nor its potential.

In the early days of computing (and I do mean early), moving text between applications was a dark art. Many simple operations placed a heavy burden on the user. You had to remember a command, commit to an action and then specify what it applied to. On some systems, this required the user to have intricate knowledge of the system’s memory. Error recovery was brutal. Sound familiar?

Then copy and paste was invented. Select the text first. Then decide what to do with it. Paste it once, twice or never. Let the software manage the memory implications, not the user.

Your people have a task in mind. But they are forced to think about their prompt, its style, the context, the model, the availability of tools and plugins. The intent is in their head. The interface is demanding something else entirely.

We are in the modal editor phase of AI interaction. Powerful, but asking too much of the humans using it. And squandering bandwidth that could be used to add actual human intelligence to their work.

AI will have its copy and paste moment. The question is whether your organization is building workflows around the current friction or preparing for what replaces it.

They say that those who don’t learn how to use AI will be replaced by those that do. But few are seeing ahead to what this implies.

If most business decisions are bounced of chat bots like ChatGPT, Claude, CoPilot and Gemini, what happens?

The responses will all converge to some maximally probable output.

They will be average.

They will be unremarkable.

What makes this worse is that the models have their own incentives.

They’re optimized to satisfy you.

To bring you back. To tell you what you want to hear.

The same engagement mechanics you hate in your Instagram feed are baked into that chat window.

The thing no one mentions about Moneyball is that it quickly stopped being a competitive advantage once everyone adopted it. The advantage moved to identifying where and why Moneyball was wrong.

Do you really want to bake average thinking into your company’s culture?

Now is the time to build a competitive advantage using AI, but only if you identify its shortcomings.

There’s no free lunch, not even with AI. Tesler’s Law stated this plainly for software. Complexity is conserved. You can’t remove it, you can only move it.

When your engineers ship 10X with AI, the complexity of writing the code vanish from their screen. Almost as if by magic.

But it’s not gone, it has only moved.

It moved to the code review, where reviewers are asked to approve more code than any human can read.

It moved to the architect who can’t track what’s changing this fast.

It moved to the teams who now maintain systems no human fully understand.

And when it slips past all of them, it moves to your customer.

Nobody decided this. Nobody chose where the complexity should land. It pools in surprising places all across your org and no dashboards are pointed there.

The velocity is real but so is the cost. Organizations are shipping and adopting new failure modes they can’t see.

The technical term for it is transactive memory. We know it as “who knows what”. It’s a mental directory, which exists both at the individual and organizational level, of which person to go to for which knowledge or problem.

When everyone asks the AI instead of each other, the team stops building the map of who knows what. Nobody learns that Sarah understands the legacy billing system or that Mark remembers why that architecture decision was made. The AI becomes the single point of contact for all knowledge, and the human network that used to route questions disappears.

Transactive memory makes teams resilient. When someone leaves, the team knows what knowledge walked out the door. The company AI will answer the questions. But it can only give you text. What’s gone are the relationship, the mentorship and the judgment that turns knowledge into wisdom.

There are no dashboards for this.

Not about the technology itself. It’s probably doing exactly what it promised.

What’s hard to see is what’s no longer happening. Two developers who would have had a technical argument. Now they are both quietly asking AI. Or not even thinking about it. The AI has already decided for them.

The senior engineer who identifies a common misunderstanding of your company’s systems based on a bug encountered by a junior developer. That gap in knowledge is never identified.

The person who walked into the standup and says “something feels off about this” before anyone had the data to prove it.

Your rollout metrics look great. Velocity is up. Tickets are down. Nobody’s flagging anything.

It’s the bystander effect applied to your organizational culture. It’s destroying organizational memory and muscle. Use it or lose it.

I built a diagnostic to find what your AI adoption is quietly breaking.

This wasn’t some abandoned pilot at select locations. This rolled out to over 11,000 stores. Initial pilots and testing, hardware, software licensing and support, internal project management costs, employee training. The cost ran into the tens of millions. Starbucks may not even the full number once you account for their employee’s vocal frustration.

NomadGo, the provider, called manual inventory “the most inefficient process imaginable.” They promised their system was up to 8x faster and 99% accurate. That sounds like magic, if you ask me.

It wasn’t. Product shortages and labor costs got worse, instead of better.

Not every AI project fails this visibly. The most insidious fail quietly while slowly eroding a company’s processes and culture. These are the ones nobody sees coming.

I ran the Starbucks rollout through the Crossfactors Framework. The failure points are exactly what you’d expect. Too bad most enterprise AI teams aren’t checking for them.

Magic wands, sparkles and bright color gradients. This is how AI is being presented to you. The value pitch follows from this imagery. They tell you AI can do nearly anything and it can probably do it better. If it cannot do it yet, it’s just a matter of time.

And what could be more magic than the miracle of life and human intelligence? AI uses our language, sounds like us and is even given human personalities. It thinks. It spends time reasoning. It even uses emojis.

This is all an illusion. The M in LLM stands for model, not magic.

For AI to work, it needs to be treated like a tool. With awareness of tradeoffs, precaution and knowledge of whether or not it is the right one. There are fantastic use cases. But there are also an countless ways to wreak havoc on your company, product and customers.

I’ve been itemizing these failure modes for years. I’ve now formalized them into a framework.

I invite you to look behind the curtain.

This is part 17 of “101 Ways AI Can Go Wrong” - a series exploring the interaction of AI and human endeavor through the lens of the Crossfactors framework.

View all posts in this series or explore the Crossfactors Framework

You’d think LinkedIn would be the one place where one would show caution in predicting the future of work.

But AI hasn’t replaced us yet, so labor dynamics is factor #17 in my series on 101 Ways to Screw Things Up with AI.

What is it?

Labor dynamics arise from the interplay of technology and human productivity. It refers to how the interactions of human work (both physical labor and knowledge work), organisational structures, financial incentives and technologies evolve over time.

Why It Matters

Labor dynamics are massively consequential at both the individual and societal levels. They have existed as long as technological progress has been happening - from agricultural fields to textile factories to the modern office cubicle. Effects are both positive and negative. Negative effects are often related to the anticipatory gap and the Collingridge dilemma, where unwanted consequences are difficult to observe until they are too prevalent to reverse. Labor dynamics affect both human fulfillment and happiness through the work that is available and its impacts on the economic climate.

Real-World Examples

Labor dynamics and automation played a role in the US and Canada’s (and Mexico’s) current trade war over the North American automobile manufacturing industry as a single production zone spanning international borders. This industry has been an early adopter and developer of automation, continuing on an early trend of improving processes since its inception. The use of manufacturing robots has aided geographic specialisation while also allowing to fill in specialized labor gaps. Labor rates were a driver of increased automation where they were higher. Robotic equipment can easily cross borders to work in another country while people can not. In the meantime, fewer people are required for each car, while the jobs that remain haven’t been automated because they require human dexterity or coordination.

Key Dimensions

Human fulfillment - human happiness is important but seldom considered - often to benefit other incentives.

Emerging workforce- young people are constantly entering the workforce, usually making career decisions early in life while they are the most impressionable. Shortages in many professions over the years can be in part blamed on our overconfidence in predicting these changes, an inability to limit the quantitative impact on this emerging workforce and on socioeconomic trends driven by their parents.

Skill decay - as automation removes certain tasks from the labor force, certain skills become rare or disappear completely - sometimes too quickly.

Take-away

Before you give someone career advice, ask yourself if you understand why we have a shortage of plumbers?

This is part 11 of “101 Ways AI Can Go Wrong” - a series exploring the interaction of AI and human endeavor through the lens of the Crossfactors framework.

View all posts in this series or explore the Crossfactors Framework

Every technology must undergo this process, even if it is niche - an this process is very hard to control.

I am speaking of technology adoption, #11 in my series on 101 Ways to Mess Things Up with AI.

What is it

Technology adoption is the process by which users, organizations and society at large begin to use and integrate new technologies into their personal and professional lives.

Why It Matters

The production and availability of a technology is not enough to guarantee adoption. Adoption can require a variety of behavioural, economic, cultural and system-level changes. We can all think of technologies that have shaped our world and we don’t often consciously realize how all these factors have aligned to enable their adoption - for better or for worse.

Real-World Examples

The adoption of Electronic Health Records (EHR) in healthcare has been tenuous with some doctors preferring to retire rather than changing their workflows. EHRs promise many things compared to paper records, including streamlining patient management and records, improving accuracy and enhancing care coordination. However, adoption has been slowed by many factors including complex interfaces, time constraints, and plain tech refusal.

Another example is the Humane AI pin. It was an internet connected wearable device that enabled interactions primarily via voice by its user. It failed to deliver in both functionality and as the next paradigm in human-machine interface.

Key Dimensions

Usability - Is the technology intuitive and easy to use? People generally have very limited patience to learn new technologies and workflows. Even for smartphone based technologies, users expect every new app to work like current ones.

Change management - Within organisations, change management are deliberate attempts at enabling the adoption of new technology by reducing friction and addressing issues that arise from transitions. In these instances, the change is usually being pushed from the top down, as it was with EHRs.

Network Effects - Many technologies require a sizable user base, if only to justify continued support and development. Others are two-sided marketplaces or platform plays. Virality in terms of marketing can also play a role, as we saw in the early days of ChatGPT.

Take-away

Adoption seems like a stupidly simple thing to take into account. Yet, people get it wrong all the time. It presents challenges across multiple dimensions, often unpredictably.