Sixteen percent.
Among young workers ages 22-25, in the occupations most exposed to AI, employment has declined sixteen percent since late 2022. The decline is concentrated in roles where AI is automating the work rather than augmenting the worker. The decline holds when researchers control for interest rates, for the pandemic, for remote work, for the tech sector. The decline is not a recession effect. It is not a cohort effect. It is not a hiring freeze effect that will resolve when the economy rebounds.
The decline is the consequence of a choice your organization is making right now, and almost no one in your leadership team has named that choice out loud.
This is the third witness in the series. Last week, the Amodeis testified to what it costs when an organization’s stated values do real work. This week, Erik Brynjolfsson is going to testify to what it costs when stated values are absent and the financial logic is allowed to run unchallenged. He has the data. He has been collecting it for three years. He is the witness whose testimony you cannot dismiss as soft, alarmist, or partisan, because he is none of those things. He is an economist at Stanford, the director of the Stanford Digital Economy Lab, and the closest thing the AI-and-labor conversation has to a referee.
What the referee is now saying is that the game is going badly, and the people losing are not who you think.
Erik Brynjolfsson is a useful witness for one specific reason. He has spent his entire career — including a long arc at MIT before Stanford — making the case that technology can be a positive force for workers. He’s not a doomsayer. He is on record arguing that AI could lead to less income inequality, not more. He has been the rare voice telling enterprise leaders that the productivity gains are real and worth pursuing.
That is why his current findings carry the weight they do. When a believer changes his mind even partially, it counts more than a hundred skeptics confirming what they already thought.
Brynjolfsson has not changed his mind on the productivity gains. He still thinks they are real. He thinks they will compound. He thinks the United States will be measurably more productive in five years than it is today.
What he has changed his mind on is the question of who benefits, and who gets hurt in the transition, and whether the choices being made inside enterprises right now are aligned with the version of the future he originally argued for. The answer, increasingly, is no. And the data is starting to show it.
The Turing Trap.
This is the framework that holds everything else in the piece together, and it is worth slowing down for.
Alan Turing, in 1950, proposed that the test of artificial intelligence was whether a machine could imitate a human well enough that another human could not tell the difference. The Turing Test, as it came to be known, set the agenda for the next seventy-five years of AI research. Build machines that act like humans. That was the goal.
Brynjolfsson argues this was the wrong goal. AGI has become a synonym for human-like intelligence or superhuman intelligence, he says, and I can see why that’s an easy benchmark, but most technologies complement humans. They don’t replace humans. AI is no different. The frontier of useful AI is not in machines that do what humans do. It is in machines that do what machines are good at — pattern recognition at scale, calculation, retrieval, synthesis — in support of humans doing what humans are good at, which is judgment, relationship, interpretation, and decision under genuine uncertainty.
The Turing Trap is what happens when an industry, and the enterprises buying from that industry, mistake the imitation goal for the value goal. The AI companies build models that approximate human reasoning. The enterprises measure success by how many humans the models can replace. The CFO sets the KPI as headcount reduction. The CIO scopes the pilots around substitution. The CHRO is asked to manage the transition.
And nobody asks the question Brynjolfsson keeps pressing on every executive he meets: what is the AI tool I can use to make Bob and Sally maximally efficient? He points to one CIO who pushed back and asked how to get workers to buy into AI more. Brynjolfsson’s answer: Stop telling them you’re going to use it to replace them.
That is the Turing Trap, in one exchange. The technology was designed to imitate humans. The enterprise was designed to replace humans. Nobody chose this consciously. Both choices were made by default. The workforce knows. The workforce has known for two years. And the workforce is responding rationally — to what they have heard, and to what nobody is bothering to deny.
The CFO conversation.
There is one moment from Brynjolfsson that I want every CFO and CSO reading this piece to sit with for a minute.
He was meeting with the CFO of a very large company. She told him the company wanted hard measures of AI performance. The next sentence: we are going to measure how much headcount reduction there is in each division.
Brynjolfsson’s response, paraphrased: Okay, that’s one measure you could have. I get why it’s the easiest to count and you like to count things. But you’re a CFO. Your job is to come up with new KPIs. How is customer satisfaction? How many new products? Which quality? I know it’s harder than counting headcount. But most of the value is on the other side.
I want you to read that exchange as the moment the Turing Trap closed around an enterprise in real time. The CFO is not malicious. She is doing her job. She is being asked to deliver financial returns on a major investment, and the easiest measurable return is the one she defaulted to. Nobody in her leadership team told her this was the wrong measure. Nobody had a better one to offer. She did not invent the headcount-reduction KPI. She inherited it from a hundred prior productivity initiatives where the measure worked well enough to defend in a board meeting.
The problem is that AI is not a prior productivity initiative. AI is the technology that lets the substitution choice be made at a scale and a speed no previous technology permitted. When the CFO of a Fortune 500 company sets headcount reduction as her primary AI KPI, she is committing the organization to substitution. She is not augmenting. She has chosen. And nobody around her table has named the choice as a choice.
Most enterprise AI strategies are currently being run by some version of this CFO. The choice is being made by default, in language no one has translated into the language of consequence. Your workforce is the consequence. They are already feeling it.
Canaries in the Coal Mine.
The sixteen percent number I opened with comes from a paper Brynjolfsson published last year with Bharat Chandar and Ruyu Chen at the Stanford Digital Economy Lab, since updated with a broader control set that sharpened the figure. They titled it Canaries in the Coal Mine, which is exactly the right metaphor: a small number of vulnerable workers showing early warning of a danger the larger system is not yet registering.
The method is careful. Brynjolfsson and his team partnered with ADP, the payroll processor, to get access to tens of millions of payroll records. They ranked every occupation in the US economy by AI exposure — how much of the work in that occupation can be done by current AI models. They tracked employment changes by occupation, by age cohort, by industry, by region.
When they first looked at the top-line data, they almost wrote a paper saying there was nothing yet to find. The aggregate numbers showed noise, not signal.
Then Chandar and Chen looked at subgroups. The pattern emerged. Among workers aged 22-25 — recent college graduates entering the labor market — in the most AI-exposed occupations, employment was down 16% since late 2022. In specific occupations like coding and call center work, the declines were steeper still; software developers in that age band fell nearly 20% from their late-2022 peak. Among older cohorts in the same occupations, the declines were negligible.
The critique came fast: the data starts in 2022, so this can’t be AI, it’s the pandemic, or interest rates, or remote work fallout. Brynjolfsson’s team tested every one of those hypotheses. They controlled for interest rates and the effect held. They controlled for the pandemic and the effect held. They controlled for remote work and the effect held. They controlled for the tech sector specifically — a sector with its own hiring dynamics — and the effect held. In some controls, the effect actually got stronger. Construction, which is highly exposed to interest rates, showed no AI-exposure decline. The two variables moved in opposite directions.
Brynjolfsson now believes the real onset of the AI employment effect started in 2024, not 2022, and that earlier data noise reflected pandemic and economic dynamics that have since resolved. The current trend, he says, is getting steeper. It is statistically significant. And it is concentrated, with surgical precision, in the workers who can least afford to lose their first job: the ones at the beginning of their careers, in the occupations most directly substitutable, with no prior employment history to fall back on.
This is Fear of Replacement, but it is no longer fear. It is the data. The young workers are not losing the jobs they have. They are not getting hired into the jobs they expected. The career ladder is having its bottom rungs quietly removed, and nobody has told the people standing at the base of the ladder that the rungs are gone.
You will not see this in your engagement survey or in your attrition report. You won’t see it in your AI adoption dashboard. You will see it in the slow shift of who is no longer in your hiring funnel — the candidates that used to apply and no longer do, the entry level requisitions that never got opened this year because we can just use AI for that now. The Canaries finding is what happens at the level of the entire economy when every individual enterprise makes the substitution choice independently. The aggregate effect is what nobody chose, and yet here we are.
Brynjolfsson’s team is not the only one seeing it. Revelio Labs, tracking job postings rather than payroll records, finds highly AI-exposed entry-level roles down more than 40% — the steepest decline of any category they measure, worse than low-exposed entry-level roles and far worse than any non-entry-level cohort. Two different data sources, two different methods, one economist and one labor analytics firm who did not coordinate. Same shape.
The rebuttal you will hear — and why it isn’t one.
Two days before this piece went out, a paper from Ramp and Revelio Labs started circulating with the kind of headline that travels fast: firms with the highest AI spending grew total employment by roughly 10% over two years, and entry level hiring at those firms grew 12%. Low-intensity adopters saw no change. The framing on the way out the door, in the coverage and on social media, was blunt — AI isn’t killing jobs, actually. Someone on your leadership team will forward you this study by tomorrow. Have your answer ready before they do.
Here it is. The Ramp/Revelio paper and the Canaries paper are not measuring the same thing, and the difference is the entire argument of this piece.
Canaries asks a question about occupations, across the whole economy: if a job is high in AI-exposed tasks, what happens to employment in that job, everywhere it is performed? The answer is a 16% decline for the youngest workers in it.
Ramp/Revelio asks a question about firms, inside a self-selected sample: among 21,559 companies that chose to spend heavily and consistently on AI, what happened to their total headcount? The answer is 10% growth — and even Revelio’s Chief Economist Lisa Simon will not call it causal. They flag it themselves: the heavy adopters in their sample were already larger, faster-growing, more technical, and more likely to be venture-backed before they spent a single dollar on AI tools. Fast-growing companies buy a lot of software. That correlation runs in a direction you would expect with or without AI in the picture.
Read the two studies side by side and you do not get a contradiction. You get the mechanism. Fast-growing, well-capitalized firms are hiring into the roles where AI augments rather than automates — engineering, sales, administration, customer service — and it shows up in their books as entry-level growth. Meanwhile, across the wider economy, the roles where AI automates rather than augments are the ones shedding the youngest workers first, and those losses land hardest at companies that don’t share that sample’s growth profile or hiring budget — the ones scaling their broken processes with AI instead of redesigning them.
Both things are true at once because they are the same choice, made by different companies, on opposite sides of the substitution-versus-complementarity fork this piece is about. The Ramp/Revelio paper is not evidence against the Turing Trap. It is a photograph of what the other path looks like when a company chooses it deliberately — the path this piece is asking you to choose too.
The call center finding everyone cites — and the follow-up nobody is talking about.
If you have read anything about AI’s effects on the workforce in the last two years, you have probably read about Brynjolfsson’s call center study. It came out in the Quarterly Journal of Economics last year, co-authored with Danielle Li and Lindsey Raymond. The headline was the most cited finding in the AI-and-labor literature: AI assistance produced a 15% average productivity gain in call center workers, with a 30% gain among less experienced and lower-skilled workers, and only small gains — with a small quality decline — among the most experienced agents.
This was the AI as equalizer story. It compressed the productivity gap. It helped the people at the bottom of the skill distribution most. It seemed to confirm Brynjolfsson’s original optimistic thesis: AI as a force for reduced inequality. Every consultant report, every vendor deck, every think piece arguing for the bright future of AI-augmented work has cited this study. It is the empirical anchor of the entire optimistic case.
Here is what Brynjolfsson told Nicholas Thompson in their conversation, almost in passing, about the follow-up paper he is now working on with the same company.
The company is mostly using AI agents to answer the questions directly. Only a few of them get escalated to humans... Then you end up having fewer people.
Read that sentence again. The same company. The same workforce. Two years after the celebrated study that showed AI compressing the productivity gap and lifting the lowest-skilled workers. And now the call center is mostly being handled by AI agents directly, with humans only on escalations. The compression effect was real. It was also a transitional phase. The endpoint, on current trajectory, is fewer humans, doing harder work, with less of the volume needed to keep the workforce employed at its prior scale.
Brynjolfsson did not bury this. He said it directly. But almost no one in the enterprise discourse is talking about it, because it complicates the optimistic story everyone has been telling each other. The egalitarian phase of an AI rollout, in this single most-cited case study, was the temporary phase. The substitution phase came next. And the substitution phase is the steady state.
This is the most important sentence in this piece, so I am going to say it twice:
The egalitarian phase of an AI rollout may be the temporary phase. The substitution phase is the steady state.
Your AI strategy is almost certainly modeled on the egalitarian phase. Your business case assumed it. Your communications to the workforce promised it. Your CHRO is currently celebrating modest adoption gains that mirror the early call-center findings. None of this is wrong. It is also, on current evidence, not durable.
The workforce knows. The workforce has been watching this pattern play out across every prior wave of automation. They know what comes next. The reason they are not engaging with your AI rollout with the enthusiasm you expected is not that they do not understand the tools. It is that they understand the pattern, and they are watching for the moment the augmentation language becomes the substitution language. In many companies, that moment has already come. In others, it’s six quarters away. None of your workers believes it will not come.
This is Loss of Mastery and Identity Disruption arriving in the same wave. The experienced practitioners watch their tacit knowledge get codified into the model. They see what their juniors see — that the codified version is increasingly being deployed without them in the room. They lose mastery to the codification. They lose identity to the substitution. And they have no language to say what is happening, because the official language of the AI rollout is still augmentation.
The four moves Brynjolfsson recommends.
He has been asked, by every interviewer and policymaker who has him in a room, what enterprises should actually do. He has a list. I am giving you his list, but with translations into the language of the People & Culture leaders this series is written for.
One. Better measurement. Brynjolfsson and the Stanford Digital Economy Lab are building a set of AI economic indicators — what he half-jokingly calls a shadow AI BLS — that will give organizations near-real-time visibility into AI’s effects on employment, wages, and productivity. The translation for your organization: stop measuring AI deployment by adoption metrics and time saved. Start measuring it by output quality, employee satisfaction, and workforce composition over time. The metrics you currently have are designed for a different question.
Two. More dynamism. The US economy has become less dynamic, not more, despite decades of technology change. Fewer workers move between jobs, companies, regions, or industries than twenty years ago. AI’s transition costs will be borne disproportionately by workers who cannot move. Brynjolfsson recommends portable benefits, retraining programs, and reduced friction in labor mobility. The translation for your organization: your AI strategy needs an internal mobility strategy. The workforce you have today is not the workforce your post-AI operating model needs, and the gap will not close on its own. Most enterprises have outsourced this question to the labor market. The labor market will not solve it on the timeline AI is moving.
Three. Lean into complements, not substitutes. This is the Turing Trap reversal at the operating level. Ask, of every AI deployment: does this make my best people 10x more effective, or does it remove the need for my best people? Brynjolfsson is explicit that he is mad at the AI companies for designing AI to imitate humans rather than complement them. The enterprise version of this anger should be directed at your vendor evaluation criteria. If your AI procurement scorecard rewards substitution capability and ignores complementarity, you are buying yourself into the Turing Trap. The vendors are responding to the demand you are creating.
Four. Centaur benchmarks. The current benchmarks measure how well AI performs alone. They should measure how well AI plus human performs together. Brynjolfsson cites medical imaging: the right benchmark is not how well does the AI recognize cancer in this scan but how well does the radiologist plus the AI recognize cancer in this scan, with an explanation the radiologist can verify. The translation for your organization: your AI tools should be evaluated on how much they improve the work of the humans using them, not on how much they automate the work of the humans they replace. If your vendor cannot show you the centaur benchmark, they have not done the work.
These four moves are not theoretical. They are concrete. They are also, in most enterprises, unaddressed. The CFO with the headcount-reduction KPI is not measuring any of them. The CIO scoping pilots for substitution is not measuring any of them. The CHRO running the change management program is not measuring any of them, because the metrics she has are downstream of the choice that has already been made elsewhere.
The question Brynjolfsson keeps asking.
There is a line Brynjolfsson uses that names the deepest question in the entire AI-and-labor conversation, and it’s the line I want to leave with you for the rest of this week.
He was describing the difference between the optimistic and pessimistic AI futures, and he said this: What is the speed of change that we can absorb as a society, and how do we make society more resilient so we can absorb a higher speed of change?
That question — what speed of change can the system absorb — is the question your organization is also failing to ask, at a much smaller scale. Your workforce has a speed of change it can absorb. Your culture, your operating model, your customer relationships all have a speed of change it can absorb.
You are deploying AI at a speed substantially exceeding all of those limits, and you are calling the resulting friction change fatigue. This is not resistance to change. It is the predictable response of a system being pushed past its absorption capacity.
This is Decision Fatigue at the organizational level. Your workforce is not refusing to adopt. They are running out of bandwidth to absorb. Your senior people are not refusing to mentor. They are running out of capacity to translate. Your culture is not failing to evolve. It is being asked to evolve faster than any culture has ever evolved, with less support than any prior transformation provided, in service of an outcome no one in the workforce was asked to validate.
The speed of change question is the operational version of Daniela Amodei’s we can only diffuse this at the speed of trust. Both are asking the same thing: what does the human side of the equation actually require, and is anyone protecting it?
In most organizations, the answer is no.
Two CEOs who named the choice.
I want to introduce you to two CEOs who did something almost nobody else in enterprise leadership has done. They named the choice out loud, in public, in writing. They are not anonymous. They are not composite, and they are not theoretical. They are running real companies, in 2026, and they made opposite decisions about the same question.
The first is Jack Dorsey, the co-founder of Twitter and CEO of Block. In late February 2026, he announced that Block was cutting 4,000 employees — approximately 40% of its workforce — and tied the decision directly to AI. “We’re already seeing,” he wrote, “that the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working which fundamentally changes what it means to build and run a company.” In a separate letter to shareholders, he predicted the rest of the industry would follow. “I think most companies are late. Within the next year, I believe the majority of companies will reach the same conclusion and make similar structural changes. I’d rather get there honestly and on our own terms than be forced into it reactively.”
Read that quote again. I’d rather get there honestly and on our own terms. Dorsey did not drift. He chose. He chose with intention, in writing, on the record, with full awareness that the cost was being paid by four thousand of his employees. He did the substitution math and he stood behind it. He framed his approach as bravery — we’re already seeing it, the rest of you are late, let’s stop pretending. Block’s stock went up on the announcement. Investors rewarded the clarity. The 4,000 employees who lost their jobs did not get a vote, but transparency is respect too, even if it comes late and not early.
I want to be precise about something. Some industry observers have argued that Dorsey’s AI framing was partly retrospective — that Block over-hired during the pandemic and that a significant portion of the cuts would have happened regardless of AI. One former Block employee called it organizational bloat wearing an AI costume. That critique deserves to be on the record. But it does not change the broader point: Dorsey publicly chose to brand the cuts as the AI-enabled future of work, and other CEOs heard him, and several have already begun describing their own restructurings in the same language. The signal he sent into the industry was the substitution choice, made boldly and with transparency, framed as competitive necessity. Whether his particular math fully held is less important than the fact that he gave permission. Within weeks, AI governance consultants reported a wave of mandates from boards: every employee must be using AI, every team must show efficiency gains, the substitution clock is ticking.
The second CEO is Himanshu Palsule of Cornerstone, the global talent and learning technology company, recently rebranded as an intelligence platform for workforce readiness. He posted on LinkedIn just weeks ago, and the post is the cleanest counter-anchor to Dorsey I have seen from a CEO at scale.
“The greatest irony of our time?” he wrote. “We built this generation of AI native thinkers and now we’re turning them away at our corporate doors. We handed them an iPad at age five. We bragged at dinner parties when they figured out smart phones before they could ride a bike. We watched proudly as they navigated four different AI tools to finish a homework assignment that would have taken us a full weekend at the library. We literally engineered their minds around technology. Let’s stop calling them ‘Gen Z’ like it’s a diagnosis. We made them. And now, now that the workforce needs people who think that way, we’re rejecting them?”
He closed with the operational commitment: “At Cornerstone, we’ve been doubling down on hiring college graduates — not out of charity, but out of conviction. Because we believe the skills that look like liabilities in a traditional interview are exactly the capabilities this next chapter of work demands. What would it look like if we stopped interviewing for the jobs of yesterday and started hiring for the work of tomorrow?”
Palsule made the opposite choice from Dorsey. He chose complementarity. He is choosing to hire the workers Brynjolfsson’s Canaries data shows are getting locked out of the economy. Which means choosing to read the AI-native cognitive profile of young workers as an asset rather than a liability. He doesn’t frame it as charity or social responsibility; he frames it as competitive advantage. And he did it in public, on the record, with his company’s name attached.
Two CEOs. Same question. Same evidence about where AI is heading. Opposite decisions, publicly committed.
The substitution choice is not a hypothetical you can avoid by staying quiet. It is being made every quarter, in every enterprise, by every leader who has not interrupted the default. The two CEOs above are the rare ones who made the choice visible. I’m not here to judge the choice, I’m here to point out that most leaders are making it without naming it. The naming is the difference between Dorsey’s honesty and your organization’s drift. The naming is also the difference between Palsule’s conviction and your organization’s good intentions.
You will not be in either of these CEOs’ positions exactly. Your scale is different. Your industry is different. Your workforce is different. But you are making the same choice they made. Right now, this quarter. The version of the choice you are making is currently invisible to your workforce and probably to your board. It is visible only to the systems that count and the people who carry the cost.
Witness three, testimony complete.
The choice has been made. The question is whether you are going to name it.
The substitution path was chosen, by default, in the language of efficiency, by leaders measuring what was easiest to count. The Turing Trap closed around your enterprise the moment the CFO set the headcount KPI and nobody offered an alternative. The Canaries finding is the early data on what that choice produces. The call center reversal is the late data on what that choice produces. The speed-of-change question is the warning the system is sending you that the current trajectory exceeds the absorption capacity of the humans inside it. And Dorsey and Palsule are the public bookends of the choice, demonstrating that the two paths are not theoretical. They are operational, and they are being chosen by real CEOs at real scale, in opposite directions, this year.
You have a choice. The question is whether you are going to name the choice, surface the alternative, and design the AI deployment around the complementarity goal rather than the substitution goal. That choice does not require new technology. It does not require new investment. It requires new measurement, new vocabulary, and the willingness to interrupt the financial logic that has been making the choice for you.
Brynjolfsson said one more thing I want you to hold. It is the most generous sentence I have heard him use, and it is the sentence that should anchor your AI deployment conversation for the next year.
We have incredible agency, but we are squandering that because we don’t understand well enough what our choices are.
You have agency. You have not been told that you do. The choice your CFO is making by default can be made differently. The headcount KPI can be replaced. The vendor scorecard can be rewritten. The AI deployment can be designed around the question: how do we make our best people maximally effective instead of the question: how do we replace them. The two questions produce entirely different technology, entirely different change management, entirely different workforce experience, and entirely different economic outcomes.
Most enterprises will drift into the substitution choice. A few — Dorsey is one — will choose it on purpose. A few more — Palsule is one — will choose complementarity on purpose. The drift is what most CHROs and CSOs and CAIOs are doing right now, whether they realize it or not. The drift is reversible. The cost of reversing is the friction of naming the choice out loud, in the rooms where the choice is currently invisible, with the people who don’t yet know that they are the ones being asked to choose.
Or rather, the drift is reversible right up until it isn’t. Ask 4,000 former Block employees.
Next week, witness four. The first witness from inside the enterprise. The first witness I can speak for in my own voice, drawn from two recent engagements where the patterns the prior three witnesses have described are showing up — not in academic papers or interview transcripts, but in conference rooms and post-session interviews with the people doing the work.
What you’ve read so far is the case from outside. Next week is the case from inside. The Five Wounds in the rooms where they are happening. The vocabulary your workforce is using when no executives are listening. The patterns I have watched recur in every engagement we have run, in every industry, at every scale.
The piece is called The View from Inside the Room.
The question carries forward.
What would you do differently if you actually believed the people inside these decisions deserved co-authorship?
I’ll see you next week.
Jess Von Bank is the co-founder of Now to Next, a transformation firm specializing in the human dimensions of enterprise AI deployment. Her book, Work Like a Mother, will be published in the fall of 2026.
This is the fourth in a seven-part series. Read Piece 1, Piece 2, and Piece 3 here.
when you are in traffic - you ARE traffic. We have more agency than we realize but we have to see it, thanks Jess for helping people recognize what is happening. I would add one more point at the leadership lens of this topic and that is value creation vs. value extraction. Similar to the question of augmentation on purpose (when/where/why) we should ALSO be looking for value creation opportunities that grow new opportunities for both the business AND AI augmentation.
Jess,
Thanks again for the analysis. Great job.
One of my major concerns with executives who look at staff reduction is that they look at areas AI can replace, but they are not considering their customers.
Due to a medical issue, I have to have supplies sent to me. I usually have to order every 3 to 4 months. They have been rolling out AI recently. I called to place an order. The order was delivered. The order was wrong and was not the product my doctor had authorized. I used to be able to talk to someone at customer service almost immediately. Now it took over 2 hours, and they had to call me back. The first call back never happened and I had to call a second time.
I do not know if my shipment got sent to someone else (they do not seem to have the technology to track that). I do not know if my personal information, which is on the order packing slip, was sent to someone else. This would be a violation of HIPAA. They seem to have the technology to make me wait to get to a person, but not to track things not been done correctly. I have filed a complaint to my medical provider about their lack of service.
Executives are forgetting that all the cost savings could come at their own reputation. I have watched countless numbers of companies go out of business when they look entirely at staff cost and not what that cost provides to the customer.
I live near the ocean, and I view AI implementation like the waves of the ocean. Those AI implementations done correctly and with staff and customers in mind are like the gentle movement of the waves. Coming onto the shore and providing nourishment. Those done without a big picture or research on customer impact are like a tsunami after an earthquake. It rushes in covering everything and then swoops back out to sea taking good and bad with it. What is left is destruction.
After reading your article, I decided to research Block and see their customer satisfaction online. Before their staff reduction was done the overall view was mixed but tended toward the positive. After they reduced their numbers, customer support appears to have been impacted negatively. People say the product is not as good, difficult to use, and poor customer service. Yes, their stock prices are up and they showed a profit for the first quarter after their RIF; however, long-term impacts do not show up in a quarter. Many people are on contracts which may only renew annually. The true impact of their move will not be known for 9 more months.