We happened to catch fellow Florida State alum Sara Blakely giving the commencement speech at our alma mater this past Saturday. In that speech, she told a story about how her dad gave her a book (on cassette tapes!) by Dr. Wayne Dyer. She said that book, How to Be a No Limits Person, changed her life. What caught our attention, though, was that her dad said, “I wish I were your age instead of at the age of 40 when I listened to this because it would have changed my life.”

That got us to thinking about the work we’ve been doing and how AI is going to transform not only work but also individuals’ lives. Reinvention used to require a runway most people couldn’t afford once they were adults.

For a select few, it could be a sabbatical or perhaps years of nights and weekends to get a new degree. The marketing director who wanted to move into product, the accountant who saw herself in operations, the engineer drawn to design, they could see the destination clearly. Unfortunately, we created a system of work that did not align with this type of thinking. The cost of the bridge to get there was usually the reason people stayed put.

We believe that AI tools eliminate that cost.

We’re watching it happen inside our own work at NICH as we transform into an agentic agency. Skills that would have taken months to develop now take days. Capabilities that used to require hiring a specialist can be built up in real time, by people who already understand the business. Reinvention that used to demand a leave of absence is happening on a Tuesday afternoon between meetings.

It’s not just us. We see this happening across three layers all at once: people, roles, and organizations.

People are reinventing themselves. They are picking up new functional skills, exploring adjacent disciplines, and discovering that the careers they assumed were closed to them are suddenly within reach. Workers who’ve spent twenty years in one lane are looking around and seeing options they didn’t see two years ago.

Roles are being reinvented. The job a person had eighteen months ago is not the job they have now, even if the title hasn’t changed. The recruiter is doing strategic talent intelligence. The analyst is doing modeling that used to live two levels up. The marketer is shipping work in a day that used to take a team of four weeks to do.

Organizations are reinventing themselves. The companies that are leaning in are remaking entire functions, not by tearing them down, but by giving them new shapes. A communication team of ten can now be a comm team of two and a strategy team of eight. What we see is that ideas and execution are now the focus as the tasks are handled by machines.

The opportunity is now speed.

Those days where reinvention used to take a sabbatical or a degree are gone. Now it can happen at work while the work gets done. The organizations that build for that, that design jobs with room for people to grow into something different, that treat reinvention as growth instead of risk, that give their people permission to become someone new without making them leave first…those organizations win. They will keep their best people and attract the ones quietly looking. They get to be the place where reinvention happens. Imagine that as your next employer brand.

The leaders telling the better story about AI are going to attract the people who want to write a better next chapter for themselves. It’s a competitive advantage hiding in plain sight.

We were recently looking at the Anthropic Claude Partner Network, a program for organizations helping enterprises adopt Claude. One detail caught our attention: there’s a certification attached to it. That got us thinking about how many AI certifications have appeared in just the last year, and what kind of economy is forming around them. Here’s the rabbit hole we went down.

First, AI benchmark and certification completions are up 994 percent year over year. Not a surprise. Like with any new technology, this is the early shape of a new industry forming.

We have seen credentialing markets form before, and the pattern is consistent. The Project Management Institute issued its first PMP credential in 1984. By the mid-1990s, fewer than ten thousand people held one. Today, the count is over 1.4 million, and there is a multi-billion-dollar training industry built to feed it. Same story with cloud. AWS launched its first certification in April 2013, back when “cloud architect” was barely a job title. As of January 2025, there were more than 1.42 million active AWS certifications and a global testing infrastructure that did not exist twelve years earlier.

AI credentialing is somewhere around year three of that arc. We can see the rough shape of the ecosystem now, even if the specific winners are unclear.

The first layer is tool fluency. Certifications for ChatGPT, Claude, Copilot, Gemini, and Perplexity already exist on the major learning platforms, with demand splitting between general use and enterprise deployment. These credentials will commoditize quickly because the tools themselves change every few months. A certification on a 2025 model interface is already partially obsolete.

The second layer is more durable: domain-specific AI fluency. AI for legal review. AI for medical coding. AI for HR analytics. AI for financial modeling. These credentials sit on top of professional expertise that already takes years to develop, which makes them harder to fake and more useful to hire against. This is where the most serious credentialing investment is going right now.

The third layer is governance, ethics, and oversight. AI auditing, model evaluation, bias testing, and compliance review for AI systems. None of this existed as a discipline three years ago. It has its own conferences now, its own certifying bodies, and hiring pipelines that run through legal, risk, and compliance functions rather than IT.

A fourth layer is starting to take shape, and it may end up being the largest. Credentials for the people who design, deliver, and assess the other credentials. AI training designers. Internal AI literacy program managers. Certification body assessors. Consultants who advise organizations on which credentials to recognize and which to treat as noise. This is the meta-layer that built itself around PMP and AWS, and it is already forming around AI.

So the question for business leaders is not whether to participate. Participation happens with or without you, often through individual employees earning Coursera certificates on their own time. The real question is whether the credentials your workforce is collecting are the ones that will actually pay back for your organization.

A few useful filters. Credentials tied to specific tools should be treated like software training: useful, perishable, and budgeted accordingly. Credentials tied to durable professional domains are worth a heavier investment because they hold value as the underlying tools change. Governance credentials are still in their early stages, so being among the first organizations to formalize them is itself a positioning advantage. And the meta-layer roles, the people who build and assess your internal AI training programs, may be the highest-leverage hires you make in the next two years.

The 994 percent number will keep climbing. What sits underneath it is the institutional plumbing that will decide how AI work gets recognized and rewarded over the next decade. The leaders who think carefully now about which parts of that plumbing matter for their organization will spend better than the ones treating every certification as equivalent.

Links

994% YoY spike in AI benchmark completions (HR Dive): https://www.hrdive.com/news/organizations-employees-racing-to-prove-ai-expertise/818295/

PMP certification holder count (Breeze, 2026): https://www.breeze.pm/blog/project-management-statistics

AWS certification program launch, April 2013 (AWS): https://aws.amazon.com/about-aws/whats-new/2013/04/30/announcing-amazon-web-services-global-certification-program/

AWS active certification count, January 2025 (AWS): https://aws.amazon.com/certification/

Accenture AI training partnership announcement, December 2025 (Accenture): https://newsroom.accenture.com/news/2025/accenture-and-anthropic-launch-multi-year-partnership-to-drive-enterprise-ai-innovation-and-value-across-industries

Nikki started her career in the marketing department of the Atlanta Braves. It was in the unglamorous execution work behind events that thousands of people showed up for without ever thinking about how they came together.

She will tell you that the job taught her more about how organizations actually function than anything that came after it. Not because the work was glamorous. Because it wasn’t. You learn how decisions are made by being close to the people who make them. You learn the distance between a plan and reality by living in the gap between them every day.

That’s what entry-level work was always for. Not cheap labor. Apprenticeship.

Now, many organizations are quietly redesigning their entry-level hiring because AI agents can now do the analytical work that first-year associates used to do. Bloomberg reported this month that the big consulting firms are reshaping what they need at the bottom of the pyramid. PwC has a name for the org structure emerging on the other side of this: the diamond. Small at the top, strong in the middle, narrow at the bottom.

Efficiency is the priority and certainly delivers immediate (short-term) financial results. We are afraid, though, that in the long term something important is missing.

The bottom of the pyramid was never about output. It was the layer where tacit knowledge moved from one generation of workers to the next. Where junior people sat in rooms they weren’t running yet and watched how things got done. Where they made small mistakes with small consequences and built the judgment that would eventually let them make big decisions.

You cannot automate your way to that. An AI agent can produce a first-year analyst’s deliverable. It cannot absorb what that analyst would have absorbed by being in the building.

PwC’s diamond model assumes the middle stays strong. But the middle was built by people who came up through the bottom. Ten years from now, organizations that eliminated their entry layer will be promoting people into senior roles who never had that critical entry-level experience.

That’s a leadership pipeline problem that will come to fruition across organizations. The risk is that leaders optimize for productivity and forget to optimize for growth. The diamond is a productivity structure. It is not a development structure.

So what does apprenticeship look like when the traditional entry layer is gone?

That’s the question organizations are going to have to figure out immediately. The ones that do will design it deliberately, treating early-career development as an intentional investment rather than an organic byproduct of having junior people around. Structured mentorship, judgment-building roles, deliberate knowledge transfer.

The ones that don’t will find out in five to seven years, when they go looking for the next generation of senior leaders and realize they don’t exist.

We know that the pyramid wasn’t perfect. But it’s an investment. The diamond model doesn’t consider that critical outcome that the people at the top got there by spending time at the bottom. And that time matters.

Links

AI Influences How McKinsey, BCG, Bain Hire for Entry-Level Consulting Jobs | Bloomberg The big consulting firms are quietly reshaping entry-level hiring as AI agents take on the analytical work first-year associates used to do.

No More Pyramids: Rethinking Your Workforce for the Agentic AI Era | PwC PwC makes the case for a diamond-shaped workforce and surfaces the question of what happens to the development layer that used to live at the bottom.

What Leadership Looks Like in an Agentic AI World | HBS Working Knowledge As AI agents take on more autonomous work, the human leadership skills that matter are shifting, and the gap between organizations preparing for that and those that aren’t is growing.

Focus on AI for Growth, Not Just Productivity | HR Executive IBM’s CHRO on why optimizing for productivity alone is the wrong AI frame, and what the growth-oriented alternative actually requires.

How AI Is Changing the Nature of Entry-Level Work | World Economic Forum Entry-level postings in the US are down 35% in 18 months, and that number understates what’s actually being lost.

AI is Transforming Work and Talent Strategy Must Keep Up | Fortune The organizations winning on AI talent aren’t just hiring for current skills — they’re redesigning how they build the leaders they’ll need in five years.

The Megamanager Era: 12 Direct Reports Is the New Average | Fortune If the middle is where the diamond’s strength is supposed to live, the megamanager trend raises a sharp follow-on question: how strong can that layer actually be?

Before we get to the good stuff, Friday’s jobs report looked like a step in the right direction. 178,000 jobs added in March, a rebound from February’s losses. Unemployment held at 4.3%. On the surface, the narrative was recovery.

Of course, as in previous months, the deep dive tells a different story. Health care added 76,000 of those jobs. Construction added 26,000. The sectors doing the hiring are largely insulated from AI disruption. The knowledge work categories where AI is already reshaping how people operate told a quieter story.

Also late last week, Oracle laid off 30,000 employees. Roughly 18% of its global workforce. They did it via 6 a.m. termination emails, with no prior warning from HR or direct managers. Access to company systems was cut immediately. (Even if the layoffs make financial sense, the execution strategy is a long-term culture killer, from our experience.)

Oracle is not a company in distress. Revenue is up 22%. Net income jumped 95% last quarter to $6.13 billion. Remaining performance obligations sit at $523 billion. Oracle isn’t cutting people because business is bad. It’s cutting people to fund AI data centers because the infrastructure bet it requires capital that it can’t generate while keeping people on payroll.

It’s a completely different kind of layoff. It clearly points to a future where the workforce is smarter and smaller.

The Power Users Surprise

On a much more positive note, Gensler just published data regarding AI utilization that’s promising.

Their 2026 Global Workplace Survey covered 16,400 workers across 16 countries. They identified 30% of employees as AI “power users,” people who use AI regularly in both their professional and personal lives. Then they looked at how those workers actually spend their time.

Power users spend 37% of their workweek working alone. Late adopters spend 42%. The most AI-forward workers in the study spend less time in isolation than everyone else. They also spend 12% of their time learning, versus 8% for their peers. They report stronger workplace relationships.

The workers most embedded in AI tools are also the most human-connected.

The story most people are telling about AI at work concludes that more automation means more isolation. Fewer ways to connect. Hollowed-out teams. Less reason to collaborate because the machine handles the handoffs. The Gensler data tells a different story. The people who are best at using these tools are using the time they get back to be with their teams, to learn, to go deeper on work that actually requires them.

What’s driving the difference? It’s not the tools. Every worker in that study had access to AI. What changed is what they had to come back to when the AI handled the routine.

Power users didn’t become collaborative because they adopted AI. They were already in places where collaboration meant something. They had colleagues worth learning from and problems worth thinking harder about. AI gave them more hours, and they knew what to do with them. Workers who hadn’t adopted AI either didn’t have that, or hadn’t built it yet, and more time just meant more of the same.

This is the finding that should matter most to leaders right now.

Smarter, smaller, AND more engaged.

The jobs report question, is AI making my workforce better or just smaller, is actually two separate questions. Whether your workforce gets smaller is partly out of our hands. What people do with the time AI returns is not. That depends entirely on the culture you have within the organization.

If people have real reasons to collaborate and grow, AI will amplify that. If they’re disengaged, working in silos, unclear on what good work looks like, AI amplifies that too. The data on “workslop” makes this concrete: 40% of workers say they’ve received low-quality AI-generated work from a colleague, and 53% admit they’ve sent it. That’s what happens when productivity tools land in a weak culture. The Gensler data is what happens when they land in a strong one.

The fundamental question driving it all is whether your people actually want to be in the same room with each other. Whether they’re learning from one another or just tolerating one another. Whether they have something worth protecting in how they work, or whether AI just exposed that they never really did.

That’s a culture assessment. And it’s one that every organization should be running right now.

The jobs report will tell you what happened to headcount. Oracle will tell you where this is going. Neither one tells you whether the people who stayed are doing anything worth doing.

The difference isn’t the software. It’s the culture.

Links

2026 Global Workplace Survey

Gensler Research Institute The full report behind the AI power user findings — 16,400 workers across 16 countries on how AI adoption is shifting how people work, learn, and connect.

Oracle Lays Off Up to 30,000 to Fund AI Data Centers | CNBC

A profitable company with $523 billion in contracted revenue cut 18% of its workforce to fund AI infrastructure, and that distinction matters for how leaders should read this moment.

Why People Create AI “Workslop” and How to Stop It | HBR

40% of workers say they’ve received low-quality, AI-generated work from a colleague, and 53% admit they’ve sent it, costing roughly $186 per employee per month in rework and eroded trust.

How AI Is Changing the Nature of Entry-Level Work | World Economic Forum

Entry-level job postings in the US are down 35% in 18 months, and that matters because entry-level work is where future leaders build judgment.

Reinvention of the CHRO in an AI-Driven Enterprise | BCG

BCG makes the case that CHROs need to become architects of a hybrid workforce, and if they’re not leading the AI conversation, someone else will.

AI Will Rewrite Employee Experience, and Deep Listening Shows How | Forrester

Forrester argues that traditional engagement surveys can’t keep up, and organizations building toward real-time AI-driven listening now will have a meaningful head start.

March 2026 Jobs Report: A Bumpy Road and a Moving Finish Line | Indeed Hiring Lab

The deeper read on Friday’s numbers: job growth rebounded, but it’s been flat for young workers, college-degree holders, and Black workers over the past year.

You’ve heard of brain rot. Let’s talk brain fry.

Here’s the scenario.

A new AI tool shows up that solves a real problem. You adopt it. A month later, another one does something different and equally useful. You adopt that too. Before long, your team is running six, seven, eight platforms, each one valuable on its own but collectively creating a level of cognitive noise that makes focused work harder than it should be.

BCG recently studied this and put a name to it: AI brain fry. Their survey of nearly 1,500 workers found that productivity increases with up to three AI tools. After four, it declines. Not because the tools stop working, but because the cognitive load of managing them overtakes the value they provide.

We know the feeling

We’ve made our agency AI tool power users over the past three years. We use these tools every day and productivity is jamming. But a few weeks ago, we realized we had seventeen different AI tools active across our workflows. Some overlapped. Some we barely used. A few were creating more work than they saved because of the constant switching between platforms.

Output is up, but so was the mental cost of sustaining it. So we stepped back and asked three questions about every tool we were using:

Does this help us complete work, or does it mostly help us start more work? Does this tool connect to our other tools, or does it live on its own island? If we removed it tomorrow, would we notice?

We ended up with a smaller, more focused set of tools that we use deeply. The output hasn’t changed. The experience of producing it has.

What we tell leaders

Most companies are not where we are now, but they will be over the coming years. They will be adding AI tools reactively, responding to vendor pitches and competitive pressure, without stepping back to evaluate the system as a whole.

We advise making a strategic AI stack roadmap before you add anything new. Less is more because the tools are so powerful. Designate fewer tools and go deeper with them. Three used at full capacity will outperform eight used superficially every time. And ask your people how it feels. Adoption metrics will tell you who’s using AI. They don’t tell you whether the experience of using it is sustainable.

Flat-out AI adoption is not the goal. Strategic AI adoption is. You get that through what we call AI readiness before turning on the hose at full throttle. The organizations that figure this out will get the productivity gains everyone is chasing without burning out the people delivering them.

Worth reading this week:

• Fortune: AI brain fry is real BCG survey finding that four or more AI tools decreases productivity. 34% of workers experiencing brain fry are actively looking to quit.

• HBR: AI doesn’t reduce work, it intensifies it UC Berkeley research on how AI increases task volume without reducing cognitive load.

• Fortune: AI is having the opposite effect it was supposed to Workers expected AI to free up time. Instead, every minute saved gets filled with more work.

In 1999, I was a 23-year-old lawyer walking into my first corporate job at Celotex Corporation. The IT department was one guy. He’d ask me to help him fix the copier because I’d worked as a runner at a law firm and knew my way around a Minolta. That was corporate technology support in the late ’90s. One person, a copier, and a prayer that the internet connection held.

Obviously, it looks a lot different today. A mid-size enterprise has an IT department with network engineers, cybersecurity analysts, cloud architects, data scientists, DevOps teams, compliance specialists, a CTO, and often a CIO reporting directly to the CEO. The title of Chief Information Security Officer didn’t exist in 1995. Neither did “cloud architect.” Or “DevOps engineer.” Or “data scientist.”

None of these jobs were predicted. They grew organically as the technology matured and organizations realized they needed entirely new kinds of expertise to manage what it made possible.

The pattern.

This weekend, Nikki was at a social gathering and ended up in a conversation with a software developer. All she wanted to talk about was how AI is going to ruin everything. Jobs gone. Industry destroyed. The future looked bleak from where she was standing.

It’s hard to blame her because the headlines are relentless, and the perspective is narrow. We’ve spent the past few editions of this newsletter writing about real problems: the one-dimensional worker, the wage paradox, the cultural friction of the two-speed workforce. We’re not pretending those challenges don’t exist.

But we’ve been sure to include what we see as the opportunity this change presents. Every major technology shift has followed the same arc. The technology arrives, and fear follows. People focus on what’s being lost. And then an entire ecosystem of new work grows up around it that nobody saw coming.

The automobile eliminated the horse-drawn carriage industry. It also created mechanics, gas station operators, highway engineers, traffic planners, insurance adjusters, and suburban development. The internet wiped out entire categories of clerical work. It also created web developers, e-commerce managers, digital marketers, cybersecurity professionals, and an infrastructure industry worth trillions.

The new jobs weren’t the old ones dressed up in new language. They were genuinely new. And in most cases, they were more interesting, better paid, and more human than the ones they replaced.

The opportunity

New roles are already forming. Companies are hiring AI governance officers, creating positions for people who design human-AI workflows, and building teams that train AI on organizational context and culture. These roles didn’t exist two years ago.

The IT department in 1998 gave no indication that it would become one of the most critical functions in every enterprise. AI will follow the same path. We just can’t see all of it yet.

The people who will do well in this transition are those who view AI as the early IT professionals did the internet. Not as a threat to their relevance, but as the foundation of an entirely new kind of work that will need their expertise, their judgment, and their willingness to learn.

The organizations that invest in their people right now, that build cultures where growth and adaptability are the expectation, will be the ones that create the jobs we can’t imagine yet.

We’ve seen this story before. And it ends with more opportunity, not less.

Links:

• PwC Global AI Jobs Barometer Job numbers are rising even in highly automatable roles. AI-skilled workers command wage premiums up to 56%.

• BLS: Computer and information technology occupations 317,700 new openings projected each year in computer and IT occupations.

• St. Louis Fed: Tech sector growth IT employment shot up 36% in the 1990s and reached 4.6 million workers by 2015.

• WEF: Invest in the workforce for the AI age 170 million new jobs projected by 2030. 92 million displaced. Net positive, but only if companies invest in people.

We’re seeing a new workplace culture challenge that didn’t exist a year ago.

Inside companies that have invested in AI tools and made them available across the workforce, a split is forming. A small group of employees has changed how they work entirely. They’ve moved past using AI to draft emails or summarize documents. They’re building automations, writing code, compressing entire project timelines, producing work that would have required a full team not long ago. For them, AI is how they operate. They talk to their computer and it executes complex tasks behind the scenes. They focus on decisions and outcomes instead of process.

Then there’s the majority of their coworkers who have the same tools, but for a variety of reasons, they’re using AI at a surface level or not at all.

Most organizations aren’t experiencing this conflict yet. Most companies haven’t equipped their workforce with the kind of AI tools that create this gap. But that’s going to change. We expect that within six to eight months, the tools now available to a small number of organizations will be widespread. And when they are, every company will face this issue.

That’s why we’re writing about it now.

The Conflict

When someone discovers they can produce a competitive analysis, a first draft, or a full project plan in an hour, their sense of what’s reasonable shifts permanently. Six months ago, a two-week turnaround on a deliverable would have felt normal. Now it feels like a bottleneck. Not because the deadline changed, but because their capabilities did.

The employee operating at AI speed starts to experience everything around them differently. Meetings feel slower, and handoffs take longer than they should. Projects that depend on contributions from colleagues who haven’t made the same shift start to stall in ways that feel avoidable. The frustration builds. And it changes how that person views the people around them, the pace of the organization, and whether this is still a place where they can do their best work.

On the other side, the colleague with the same tools on their desktop watches all of this happen and doesn’t know what to make of it. Some feel threatened. Others get defensive or quietly disengage. None of this is because they lack access. Something else is in the way: maybe it’s uncertainty about where to start, maybe a manager who hasn’t signaled that this is how work should be done now. Or maybe they are just modern-day Luddites, but instead of smashing weaving machinery, they just refuse to use AI tools.

The gap compounds quickly and research backs this up. Studies show that as AI-powered employees produce more, implicit pressure spreads across the team. Workers report that AI has increased their workload, not reduced it. Data from ADP Research shows that daily AI users report the highest engagement and motivation at work, but also weaker connections to their coworkers. They love the work, but they’re drifting from the people.

We asked John Trainor, President of Four Technologies, what this dynamic looks like from where he sits.

“I need to figure out how I can unlock the minds or activities of the people who have not yet figured out how their work can be better and faster with AI. On the flip side of that coin, I will also say that I need to know who is using AI and make sure that they are using it responsibly. I need to take that into consideration when I rely upon work that has been produced by someone who is leveraging AI, but maybe is not yet skilled in understanding what the inherent risks of AI might be.”

Leaders like John are managing in both directions at once, moving non-adopters forward while ensuring power users produce work and stay engaged. That’s the leadership challenge coming soon to every organization.

What Leaders Can Do

The instinct for most will be to treat this as a training problem. Run a workshop. Send a how-to guide. Encourage people to experiment. That helps, but it doesn’t address the cultural dimension of what’s happening.

The single biggest predictor of whether an employee crosses the threshold from surface-level AI use to real adoption isn’t access or aptitude. It’s their direct manager (as it has always been for almost every workplace challenge). When managers actively use AI themselves and make it clear that this is how work gets done now, adoption follows. When they don’t, it doesn’t. That’s the lever.

Here’s what we’re telling leaders who are starting to see this split form:

Call it out. The two-speed workforce is real, and pretending it will resolve on its own gives it time to harden. Acknowledge to your teams that adoption is uneven, that this creates tension, and that you’re paying attention.

Make AI part of how you lead, not something you’ve approved and moved on from. If your people see you using these tools to make decisions, prepare for meetings, and move work forward at incredibly new speeds, they’ll follow, we assure you. This is what we’ve done internally at NICH, where we’ve fully transitioned to an AI-first work methodology.

Rethink timelines and workflows. This is the practical one, and it’s harder than it sounds. If some of your people can now do what used to take weeks in hours, your project planning needs to reflect that without punishing the people who aren’t there yet. You’re not managing a performance problem. You’re redesigning how work moves through your organization.

Your AI power users are your most curious, growth-oriented employees. They’re also the most likely to leave if they feel held back by the pace around them. They know they are creating an entirely new skill set that will make them highly marketable to other power-user organizations. If you’re not paying attention to how they’re experiencing work right now, you could lose them before you realize there was a problem.

This is Culture Work

We’ve spent a decade at NICH helping organizations see the gap between how leaders think their culture is functioning and what employees actually experience. The two-speed workforce is a new version of something we’ve seen before: a shift happening on the ground that leadership hasn’t fully registered yet.

The difference this time is the speed. This gap will widen faster than anything we’ve encountered in our work. The organizations that get ahead of it will keep their best people and bring everyone else along. The ones that wait will be reacting to a problem that’s already set.

We’re building something that we think will help organizations see these dynamics clearly. More soon.

Links:

• OpenAI: The State of Enterprise AI The report that quantified the gap between AI power users and everyone else, even inside the same companies with the same tools.

• HBR: AI doesn’t reduce work, it intensifies it UC Berkeley research on what happens when AI-powered productivity creates pressure across the organization.

• Gensler Global Workplace Survey 2026 AI power users spend less time alone and more time learning. Challenges the assumption that AI isolates workers.

• WEF: AI is becoming your new work colleague ADP data showing daily AI users are more engaged but less connected to coworkers.

On Friday, we learned that the U.S. economy lost 92,000 jobs in February. Economists expected a gain of 50,000. December was revised to a loss of 17,000. With those revisions, 2025 recorded five months of labor market contractions… the most since 2010.

Labor force participation dropped to 62%, its lowest since December 2021. Long-term unemployment hit 25.7 weeks, a four-year high.

And yet despite all of that, wages rose. Up 0.4% for the month and 3.8% year over year, both above expectations.

Fewer jobs. Higher pay. Those two things aren’t supposed to happen at the same time.

Let’s look at what’s actually going on.

The Quiet Split

Information services, the sector most directly exposed to AI, has lost jobs for twelve consecutive months, averaging 5,000 per month. Manufacturing lost 12,000. Federal government employment dropped 10,000, part of a 330,000-job slide since October 2024.

Meanwhile, research from the Federal Reserve Bank of Dallas found that wages in AI-exposed sectors are rising fast. Since late 2022, average weekly wages in computer systems design are up 16.7%. The national average is 7.5%.

AI isn’t just eliminating jobs. It’s starting to split the labor market in two.

Codifiable vs. Tacit

The Dallas Fed draws the line clearly. Codifiable knowledge, which is rules, procedures, templates, processes, is the kind of work that follows a pattern. AI can learn it fast and efficiently. Tacit knowledge, which is judgment, context, and the ability to navigate ambiguity, comes from experience. AI can’t replicate it.

We are seeing that workers with codifiable skills are being squeezed out. On the other hand, we see workers with tacit knowledge are commanding premiums. The same technology is automating one group and making the other more valuable.

The One-Dimensional Worker, in Real Numbers

Last week we wrote about the one-dimensional worker, the person organizations trained to execute tasks without developing judgment, adaptability, or a growth mindset. The kind of worker a machine can replace. There’s a LOT of them in enterprise-level organizations right now.

Friday’s report is that thesis in real numbers. The workers losing ground aren’t random casualties. They’re the ones whose value was built on doing predictable things predictably well. The ones commanding wage premiums bring something a model can’t: experience, relationships, and the ability to make decisions when the data is messy, and the stakes are high.

Organizations that spent years rewarding compliance and narrow execution built workforces full of codifiable knowledge. They optimized for efficiency and got brittleness.

The Compensation Problem

As we’ve mentioned before, as AI continues compressing headcount the compensation model most companies are running also breaks.

Traditional structures were designed for large workforces doing standardized work. Pay bands. Salary ranges. Annual percentage increases. Equity spread thin. That model made sense when scale meant headcount and roles were roughly interchangeable within a level.

It doesn’t work when a team of eight produces what fifty used to. The person who combines deep experience with AI fluency isn’t a 10% raise above the person who can’t. They’re a different category of contributor. And if you’re paying them like a line item in a traditional comp structure, someone else will pay them what they’re actually worth.

The wage data is already showing this. AI-exposed sectors paying more than double the national wage growth while shedding headcount. Organizations that don’t rethink how they compensate and retain their most valuable people will watch them leave. We can’t stress this enough.

The Real Report

Friday’s jobs report tells you the economy lost 92,000 jobs. It doesn’t tell you which ones are coming back.

We believe that there’s an opportunity in this data. The organizations that come out ahead won’t be the ones cutting headcount and calling it AI strategy. They’ll be the ones who recognize that the value of their people just went up, and build cultures, structures, and compensation models that reflect it.

That means investing in the tacit knowledge AI can’t touch. Developing people who can exercise judgment, not just follow processes. Creating environments where growth isn’t a perk, it’s the operating system.

For the past decade, we’ve been helping organizations build cultures where people develop, adapt, and become the kind of talent that no technology can replace. And right now, we’re building toward something new that we think will change how organizations understand their people in this moment. More on that soon.

The paradox isn’t really a paradox. It’s the market pricing in what we’ve been saying: the human advantage isn’t about doing more. It’s about knowing things a machine never will.

The companies that bet on people will win this era. The data is already proving it.

Links:

· The February jobs report: Payrolls fell by 92,000 — CNBC’s full breakdown, including the twelve-month trend in information services losses.

· AI is simultaneously aiding and replacing workers, wage data suggest — The Dallas Fed research on codifiable vs. tacit knowledge. The most important piece of economic research on AI and work we’ve read this year.

· Companies are laying off workers because of AI’s potential — not its performance — HBR on why the job losses are real even though AI hasn’t fully delivered on its promises.

Last week, a financial research firm called Citrini published a thought experiment on Substack imagining what 2028 looks like if AI-driven job displacement goes unchecked. Unemployment at 10%. Consumer spending in freefall. A “human intelligence displacement spiral” where AI replaces white-collar workers so fast the economy can’t absorb the blow.

It was clearly labeled as fiction. Three times.

The Dow dropped 800 points anyway. Software stocks hit 52-week lows. IBM had its worst day in 25 years.

Days later, Block (aka Square), the payments company run by Jack Dorsey (who was at Paris fashion week), cut nearly half its workforce, citing the rise of “intelligence tools.” One of the largest reductions in S&P 500 history.

This came on the heels of AI executive Matt Shumer’s essay on X, now viewed over 85 million times, comparing this moment to February 2020: a crisis approaching while most of us aren’t paying attention.

Fear and anxiety is loud right now. And we’re not here to tell you it’s wrong.

We believe that there will absolutely be pain. White-collar roles will shrink. Some will disappear. Companies are already making workforce decisions based on what AI might do, not what it’s actually doing today. That’s real, and pretending otherwise doesn’t help anyone.

But we think the concern is missing something important. The threat isn’t just the technology. It’s what the technology is exposing.

The Comfortable One-Dimensional Worker

This is something we’ve been talking about for years in our culture transformation work, long before AI entered the conversation.

For decades, organizations (and our society for that matter) have built systems that reward specialization and compliance over adaptability and growth. Show up. Do your job. Stay in your lane. Get your paycheck. Many workers, understandably, settled into that deal. They got comfortable. They bought a house. They had a few kids. They stopped growing. Not because they’re inherently lazy, but because they are a part of a system that didn’t require them to do anything different. And nothing in the culture challenged them to. The biggest stress is possibly losing that comfortable job and having to find another comfortable job.

As workplace culture experts, we’ve seen this pattern hold organizations back time and again. Companies with cultures that don’t foster curiosity, development, and a growth mindset end up with workforces that are efficient but brittle. They can execute today’s tasks, but they can’t adapt when the ground shifts. We’ve always treated this as an urgent problem. AI just made it undeniable.

Because AI didn’t create the one-dimensional worker. It’s revealing how many organizations have been building their workforce around one.

The workers most at risk right now aren’t simply the ones whose jobs overlap with what AI can do. They’re the ones who stopped developing, who haven’t built new skills, new ways of thinking, new ways of adding value beyond the repeatable task they were hired to perform. A one-dimensional worker is exactly the kind of worker a machine can replace.

For all our sci-fi nerds out there, this is Frank Herbert saw sixty years ago.

A Lesson from Dune?

In his 1965 novel Dune (yes, the blockbuster film series with Timmy Chalamet), Herbert imagined a future where humanity had already been through its AI crisis and come out the other side. Humanity once handed its thinking over to machines. Not because the machines forced it. Because it was easier. More efficient. Gradually, the machines took over more and more of the cognitive work humans used to do for themselves. Sound familiar?

Eventually, humanity pushed back and wrote a single commandment into their most sacred text: “Thou shalt not make a machine in the likeness of a human mind.”

But here’s the part most people miss. The uprising was really about, “The target was a machine-attitude as much as the machines. Humans had set those machines to usurp our sense of beauty, our necessary selfdom out of which we make living judgments.”

Not a machine. An attitude. The belief that human thinking and human judgment are inefficiencies to be automated away.

And here’s where it gets hopeful.

After the machines were gone, humanity didn’t collapse. It invested in people. Entire disciplines emerged around developing extraordinary human capability. People trained as “Mentats,” essentially human supercomputers who could process vast amounts of information, but who brought something machines never could: judgment, intuition, conscience. Others developed deep expertise in reading human behavior, navigating complex systems, understanding people at a level no algorithm could touch. You’ve seen the movie!

Herbert’s point wasn’t that technology is bad. It’s that when you stop investing in human capability, when the machine-attitude convinces you that people are the bottleneck rather than the advantage, you lose something you can’t get back.

The Growth Mindset Is the Differentiator

We believe we’re going to come out the other side of this with more opportunity, not less. But not for everyone. For the workers who adopt a growth mindset, who treat this moment as a reason to develop new capabilities rather than a reason to freeze.

The data supports this. The World Economic Forum’s latest research shows workers with AI skills now command a bigger wage premium than those with a Master’s degree. Candidates who demonstrate AI fluency are 8 to 15 percent more likely to land interviews. A study from the National Bureau of Economic Research surveyed 6,000 firms and found that employees, the people closest to the actual work, expect AI to create opportunity. The fear is concentrated at the top of the org chart. The people doing the work see possibilities.

And here’s what shouldn’t get lost in the noise: a 2026 study by Conduent found that 79% of employees say their interactions with real human beings directly influence their loyalty. The things people want most, competence, convenience, and caring, haven’t changed in three years. The tools keep evolving. What people need from other people stays constant.

AI will replace one-dimensional workers. It will not replace multi-dimensional humans who think critically, build relationships, adapt to new challenges, and keep growing. Those people are about to become the most valuable talent in any organization.

This is what our culture work has always been about: helping organizations build the kind of environment where people don’t stagnate. Where growth isn’t a perk, it’s the expectation. Where the culture itself becomes the thing that makes a workforce adaptable enough to thrive through any disruption, including this one.

The Real Question

For the record, the Citrini scenario isn’t impossible. Neither is the opposite, a future where AI raises the bar and the people who rise with it find themselves in a better position than before.

Which one we get depends less on the technology and more on two things: whether organizations invest in developing their people instead of just replacing them, and whether workers themselves decide that growth is no longer optional.

Herbert’s warning from sixty years ago wasn’t about the rise of the machines. It was about the rise of a mindset, one that treats human potential as a cost to be cut rather than a capability to be unleashed. (We are obviously big fans of the book!)

The machines are here. The question is whether we’ll adopt the machine-attitude along with them, or do what Herbert imagined, and bet on people.

We’re going on record for the latter.

Okay, we want you to do something. Open an incognito browser. Go to ChatGPT. Type this:

“Tell me what it’s like to work at [your company name].”

Seriously, it will only take six seconds.

What comes back is the unfiltered, algorithmically compressed version of your employer brand. Not the version from your careers page. The real one. Built from Glassdoor reviews, Reddit threads, LinkedIn commentary, news articles, and every digital breadcrumb your current and former employees have left behind.

AI will hand back a tidy summary: pros, cons, employee sentiment, leadership reputation, compensation perception, cultural themes. It will be blunt. And it’s what your candidates are doing right now before they ever visit your website.

This is the new front door of recruiting. And for companies that have done the work to build a great employee experience, it’s actually really good news.

Smoke and mirrors don’t survive true sentiment

For years, employer branding operated like a real estate listing. Stage the house, take the photos from the right angle, write the description that makes 1,200 square feet sound like a mansion. Just don’t talk to the neighbors, because we don’t want to know what it’s really like to live there.

AI doesn’t take your career site at face value. It cross-references it against everything else it can find. If there’s a gap between what you’re selling and what employees are saying, candidates will see it instantly.

But here’s the flip side: if what employees are saying is genuinely positive, AI becomes the most powerful recruiting tool you’ve ever had. Earned reputation at scale.

You no longer control the narrative.

AI isn’t creating a narrative about your company. It’s compressing one that already exists. If AI says your leadership has a communication problem, it’s because the pattern is unmistakable across enough sources. If it says you don’t pay well, that’s what your current former employees believe.

The hardest example is always leadership communication. It’s the one thing that touches everything else. When leaders communicate clearly, consistently, and with genuine care for their people, it shows up everywhere: engagement surveys, retention numbers, Glassdoor reviews, and now AI summaries. When it breaks down, everything downstream suffers.

You can’t prompt-engineer your way out of a culture problem. But you can build your way out of one.

Understand the real experience, then build from there

This is where most organizations get stuck. They know something needs to change, but don’t have a clear picture of where the gaps actually are.

That’s why we built the NICH +Culture Employee Experience Framework. It measures eight categories that shape how people feel about their work and their workplace: Fundamental Needs, Meaningful Work, Leadership, Positive Workplace, Recognition + Appreciation, Health + Wellbeing, Development + Growth, and Trust in Organization. We are in the process of adding one more: AI Readiness + Adoption.

These aren’t abstract concepts. They’re the exact dimensions that surface in AI summaries, Glassdoor reviews, and candidate research. When you have a clear, data-driven picture of where you stand across all eight, you stop guessing and start building. You can identify where leadership communication is breaking down. You can understand if employees feel recognized, developed, and genuinely connected to the work.

And when those foundations are strong, your employer brand takes care of itself. Because AI is just reflecting what your people already know.

The opportunity

Companies that invest in the real employee experience gain an enormous advantage right now. While competitors spend money on employer brand campaigns that get contradicted by AI reality, you can build a workplace that speaks for itself.

The question isn’t whether AI is summarizing your employer brand. It already is.

The question is whether you’ve built something worth summarizing.

Links

The A.I. Disruption We’ve Been Waiting for Has Arrived

“What if all of that immense bureaucracy, the endless processes, the mind-boggling range of costs that you need to make the computer compute, just goes poof?”

How to Ace a Job Interview With an AI

“As the use of artificial-intelligence tools in hiring continues to expand, it isn’t unusual for job candidates to be interviewed by an AI platform rather than a human—at least initially.”

Why Some People Thrive on Four Hours of Sleep

“Short sleepers, who make up less than one per cent of the population, spend significantly less time snoozing without any apparent health consequences”

This Is No Miracle—It’s Team USA History to Remember Forever

“Outgunned by their Canadian rivals, an inspired U.S. Olympic men’s hockey team roars to a historic sudden death victory in Milan”

https://gradient.horse/

SpeedStudio Podcast:

Ep. 128 – Santa Vall race report with Michael Garrison

This week’s special episode is a post-race analysis of Santa Vall, where Michael started the season with a Top 5 finish, beating most of the world’s top gravel racers in the process. In the race, he debuted the new Basso Palta 3 and unreleased Tune gravel wheelset (not a bad debut if we say so ourselves). We chat how gravel is becoming more like road racing, what it’s like racing against guys you watched as a kid, comparing Europe vs US racing (it’s way more chaotic and requires more skill), and how growing up in Atlanta prepares you for that chaos. We chat how CARBS Fuel has completely changed his nutrition strategy and how all our partners are the best at what they do. Lastly, we talk through what it takes to build a sustainable program: sticking to it and lots of trust.

The headline number looked good. The U.S. economy added 130,000 jobs in January, beating expectations. Unemployment ticked down to 4.3%. If you stopped there, you’d feel fine.

But underneath was a revision that tells a different story. The Bureau of Labor Statistics corrected total 2025 job creation down from 584,000 to just 181,000. That’s15,000 jobs per month in an economy of 158 million workers. That’s the largest downward revision in a decade, and it makes 2025 the weakest year for job growth outside of a recession since 2003.

And the gains that did exist were remarkably concentrated. Healthcare and social assistance accounted for nearly all of January’s job creation. Construction added 33,000, driven largely by data center building. Financial activities lost 22,000. Federal government shed 34,000. Strip out healthcare, and the number tells a very different story.

The AI signal is getting louder. Challenger, Gray & Christmas attributed over 54,000 job cuts directly to AI in 2025. Goldman Sachs found that unemployment among 20- to 30-year-olds in tech-exposed occupations rose nearly three percentage points. Entry-level postings are down 35% in two years. Amazon, Salesforce, Microsoft, and IBM all explicitly cited AI when reducing headcount. Salesforce’s CEO said AI is handling 30 to 50 percent of their workload, not as a projection, but as a current reality.

Obviously, AI isn’t the only factor here. Tariffs, immigration shifts, federal cuts, and post-pandemic corrections all contribute. But AI is now a visible part of the equation, and the labor market is starting to reflect it.

What we are thinking about with our clients.

If AI continues compressing headcount, something deeper has to change: the way companies are designed. For decades, growth meant hiring. Scale meant layers: more people, more managers, more tools to manage the coordination. That model is already under pressure.

Let’s be honest, many leaders use those layers and headcount to justify their own compensation structure. It’s not a great system.

When smaller teams can produce what used to require much larger ones, organizations don’t just need fewer people. They need a different structure entirely. Flatter. Faster. Fewer layers of approval. The roles that remain become more cross-functional, more autonomous, and significantly harder to replace.

And that’s the opportunity, especially for individuals. If companies are moving toward fewer, higher-leverage people, then the person who knows how to work with AI isn’t just employable. They’re essential. The individual who can use AI to multiply their output becomes the new definition of top talent.

For anyone investing in AI right now, like learning how to integrate it into real workflows, not just experimenting casually, this is a massive personal advantage. In a market where organizations are getting leaner and entry-level roles are thinning out, the ability to operate with AI is a skill that compounds. You become harder to replace, more valuable to your team, and better positioned for the roles that will define what comes next.

The companies that figure this out will be smaller, faster, and disproportionately effective. The people who drove that shift will be the ones everyone’s trying to hire.

Links

January 2026 Jobs Report: Revisions to 2025 Data Made an Already Bad Year Worse

Indeed’s Hiring Lab calls 2025 “an exceptionally weak year by almost any standard.” The sector concentration is striking.

Revised Economic Numbers Inject Uncertainty Into Jobs Market

A good breakdown with former Labor Department chief economist Harry Holzer on what the revisions actually mean and why the headline number is misleading.

OpenAI Unveils Frontier, a Product for Building ‘AI Co-Workers’

“The new platform, launched amid market fears over AI’s disruption to software, is aimed at helping businesses develop AI agents that work alongside humans”

Breaking down the business of the Milan-Cortina 2026 Winter Olympics

SpeedStudio Podcast:

EP. 127 Bad Bunny at Super Bowl, Michael Garrison at Casa Basso

This episode finds Nikki and Chad in the Studio after a huge week and a day behind on the pod. They chat about how Bad Bunny won the Super Bowl, whether people actually like Kid Rock, the pro team launch week, the Culture of Speed campaigns, how bad things can lead to good things, (from Chad’s broken bones), and how Nikki’s feeling positive about 2026 the year of the Fire Horse. They check in with Michael at Casa Basso in Girona, the new Palta III, new Tune wheels, and what tires he’s running (he has no idea). They also get into the diabolical strategy of Pop Up Bagels in the ATL, why welcoming competition is the answer, looking a gift horse in the mouth, getting bricked up and locked in, and the fact that Tylenol simply does not work.

First, happy post-Super Bowl PTO day to all who celebrate.

We were not planning on continuing our coverage of AI adoption this week, but then Claude went crazy and blew up the markets in the process.

In case you missed it, Anthropic’s latest updates, including expanded Claude Cowork capabilities and deeper interaction with everyday work tools, signaled a shift many have been predicting for months. It undoubtedly proved that AI is can be the interface, the workflow, and increasingly, the platform.

In short, Cowork operates across files, systems, and multi-step tasks. Suddenly, entire categories of paid software start to look optional.

The market reacted accordingly. Software, data, and professional-services stocks in both the U.S. and Europe sold off sharply, as investors absorbed what this means for seat-based SaaS models and “visibility premium” platforms.

We tested it ourselves. Using Claude Cowork, we’ve already built:

· a lightweight project management tool

· a functional CRM

In a traditional setup, those would be separate paid platforms, each with onboarding, admin overhead, and renewal cycles. Here, they’re workflows. Good enough, flexible, and owned by an operator rather than the vendor.

That’s the shift leaders should pay attention to. AI doesn’t just automate tasks. It collapses categories.

Of course, this has us immediately thinking about what this means for the workforce. The signal is getting harder to ignore.

Over the past week alone, major companies announced large workforce reductions:

· UPS: 30,000 roles

· Amazon: 16,000

· Dow Chemical: 4,500

· Pinterest: nearly 1,000

· OpenAI: dramatically slowing hiring

The big picture:

· 1.2 million job cuts were announced overall in 2025, the highest since the pandemic

· Entry-level job postings are down 35% in two years, and 67% in tech

· More than two-thirds of enterprises report slowing entry-level hiring

· Several large employers have publicly committed to keeping headcount flat

You can debate how much of this is “because of AI.” What matters is that AI is now a credible justification for headcount decisions, and that changes organizational behavior.

Put these threads together and we see an undeniable a pattern emerging.

When AI becomes the interface, software becomes optional.
When software becomes optional, teams shrink the stack.
When the stack shrinks, roles tied to maintaining it compress.

We are moving from “tools that support teams” to “operators who replace layers.” It’s not going to be immediate because, candidly, so many organizations aren’t paying attention. But it’s fast enough that the gap between organizations that can truly operate AI and those that can’t will show up in cost, speed, and output quality.

That’s why AI adoption is so critical.

So, back to last week’s newsletter: it’s all about leadership.

Leaders need to create three conditions quickly:

1. Safety: AI learning must feel developmental, not evaluative.

2. Ownership: adoption cannot live in a task force, it has to live in leadership routines.

3. Clarity: people need to know where AI is expected, where it’s discouraged, and what “good” looks like.

With that AI becomes leverage. Skip it, and you’ll still be paying for tools you don’t need while competitors quietly rebuild their own operating model.

Links

Anthropic’s new AI tools deepen selloff in data analytics and software stocks, investors say

“AI developer Anthropic launched plug-ins for its Claude Cowork agent on Friday that would automate tasks across legal, sales, marketing and data analysis.”

SaaSpocalypse Now? AI Is Disrupting SaaS — But Not All Software Is Doomed

This Is Why It’s So Hard to Find a Job Right Now

“A ‘deep freeze’ has enveloped the U.S. labor market. A whole bunch of factors are at play.”

What Photographers Saw In The Super Bowl Archives

How Miss Piggy Went From Minor Muppet to TV’s Top Hog

SpeedStudio Podcast:

Ep. 126 – Maggie Coles-Lyster, Pro Cyclist, Human Powered Health

On this week’s episode, we have former Canadian National Champion, Olympian, and world tour cyclist Maggie Coles-Lyster on the podcast. We discuss her big win at the Tour Down Under, her fightback after battling a career-changing injury, current racing schedule at the Tour of UAE, and her plans for Flanders, Roubaix and more exciting races. Welcome to our new Canadian listeners!

Across this series, we’ve explored why AI adoption is uneven inside organizations. We’ve looked at foundational employee experience gaps, trust in the organization, and the basic capacity problem. All of those factors can have uneven weight depending on where they are in their AI journey. But what ultimately determines whether AI takes hold is how well leadership behavior aligns with the culture the organization says it wants.

In our work, three leadership dynamics show up most often.

AI Feels Risky When Safety Isn’t Clear

In many organizations, AI still lives in undefined territory. Employees are unsure what good use looks like, how mistakes will be treated, or whether learning will be evaluated. In that environment, caution is a rational response.

When experimentation feels risky, adoption stays shallow. Where leaders clearly signal that learning is supported and expected, employees engage differently.

Adoption Drifts When Ownership Is Unclear

AI initiatives are often introduced through functions or committees. That structure makes sense, but employees take their cues from leadership behavior. When senior leaders remain abstract or distant, adoption tends to slow.

Organizations see more traction when leaders are visibly involved in shaping how AI fits into real work. Ownership signals priority. Priority shapes behavior.

Ambiguity Slows Behavior Change

Most organizations talk about AI in broad, optimistic terms. What’s often missing is specificity. Employees are left guessing where AI should be used, what’s expected, and how it connects to their role.

Without clarity, people default to existing habits. Not out of resistance, but because ambiguity increases risk. Clear expectations reduce friction and make change feel achievable.

Taken together, these patterns point to a larger issue. AI adoption cannot be layered on top of a misaligned culture or an inconsistent employee experience. It only accelerates when leadership behavior, cultural signals, and the day-to-day reality of work are aligned.

When employees trust leadership, have the capacity to learn, and experience consistency between what leaders say and what they do, adoption follows. When those conditions are missing, even the best tools struggle to gain traction.

Perhaps you’ve noticed, but that is the through-line of this series. AI adoption reflects the health of the employee experience and the clarity of the culture supporting it.

Technology may enable change, but culture determines whether it sticks.

Where Is A.I. Taking Us?

“Eight Leading Thinkers Share Their Visions.”

U.S. Companies Are Still Slashing Jobs to Reverse Pandemic Hiring Boom

“Many corporations binged on labor during the pandemic. Now, facing economic uncertainty and threats from AI, they are slimming down.”

U.S Employee Well-Being Hits 5-Year Low: Young Workers and Individual Contributors Suffer Most

These 3 ‘addictive’ social media UX features are on trial

“New lawsuits say that these key features are purposefully designed to keep kids scrolling.”

Annual working hours per worker

A chart showing how working hours has declined over time.

SpeedStudio Podcast

Ep. 125 – Our Pro Racing Schedule, Atlanta Group Ride Culture, and The Perfect Tucker

This episode finds Nikki in Chad in the Studio, weathering the winter apocalypse that didn’t happen. They chat about how working while Zwifting makes you feel less guilty, Rapha’s new US National team kit, and the SpeedStudio professional racing program schedule for the first half of the year. They then do a deep dive on why Atlanta has such a strong cycling culture, its history and characters, the Buckhead Gran Prix, how South Downtown has potential to host one of the world’s biggest bike races, and The Perfect Tucker. It’s all about remembering to appreciate the special moments and experiences you have when you have them. Enjoy.

We have already covered two major barriers to AI adoption: the fundamentals of the employee experience and the trust employees place in their organization. The next barrier is simpler and relatable to almost everyone. Employees cannot adopt new technology when they do not have the capacity to absorb anything new because they are already stretched too thin.

AI requires a lot of upfront effort. Learning new tools, experimenting, adjusting workflows, and rebuilding habits all take time. But most employees are already operating at or beyond capacity. Their day is a steady stream of tasks, messages, and meetings (on meetings on meetings). There is no margin left for experimentation, even with tools that promise to save time later.

This is where the cultural disconnect shows up. Leaders see AI as an efficiency unlock. Employees experience it as additional work stacked onto a full load. And when capacity is tight, even well-intentioned change feels like a burden.

In our employee experience framework, perceptions of workload and capacity sit near the center because they determine whether employees can absorb change at all. When teams are stretched thin, adoption slows. Not because employees are resistant, but because they are exhausted.

AI will not take hold inside organizations that do not create room for people to learn and adapt. Capacity is not a soft variable. It is one of the structural conditions required for change. Without it, even the smartest tools stall on impact.

Why People Create AI “Workslop”—and How to Stop It

“Our research points to an uncomfortable answer: The proliferation of workslop is a management failure.”

An A.I. Start-Up Says It Wants to Empower Workers, Not Replace Them

“The founders said their goal was to build software that facilitated collaboration between people — like an A.I. version of an instant messaging app — while also helping with internet searches and other tasks that suit machines.”

AI Took Control of My Life and I Love It

Solid real life examples on how to use AI.

The future belongs to the creative generalists

“The more AI can do, the more creatives need to develop a generalist mindset towards building skills”

SpeedStudio Podcast:

Ep. 124 - Peta Mullens at the Tour Down Under

Nikki and Chad are in the Studio on MLK Day, where we get an update on why you should never wear rings while trail running. Yikes. They call recurring guest Peta Mullens to discuss why “Dry January” is out and “Jealous January” is in. She’s there working commentary for the Tour Down Under, so they get the low down on the 30-minute time zone of Adelaide, what it’s like talking to someone from the future, how it’s actually the Tour of Adelaide, and why it’s the favorite race of the year for World Tour pros.

They talk brand activations, local punters, hands-and-knees, wine tasting as a job, racing RADL GRVL, and a little grog at weddings never hurt anyone. They also chat about whether Dylan Johnson is enjoying Australia, a breakdown of the 3-on-1 finale of the women’s race, how women’s racing is so unpredictable, and how the TDU provides opportunities for new riders.

Peta gives insights on how she prepares for the commentating job (podcasts!), her plans for 2026, coming back to the US, trail racing, why wearing gloves while running isn’t cool, her kit design preview, chasing Summer, how to stay in Australia for 6 months, bakery recommendations, why to watch out for Kangaroos, and Dylan’s delusional confidence regarding surfing and driving a stick. As always with Peta, this one is a wild ride.

Everywhere you get your pods.

If the first barrier to AI adoption is an uneven employee experience, the second is even more fundamental. Employees will not change how they work if they do not trust the organization asking them to do it.

Trust is the quiet variable leaders underestimate. In the NICH Employee Experience Framework, the single most important dimension we measure is Trust in the Organization, which includes transparency, communication quality, leadership integrity, and the belief that the company invests in its people in meaningful ways. When trust is high, employees stretch, experiment, and take risks. When trust is low, they retreat into self-preservation.

This dynamic shows up immediately in AI adoption.

Employees are being told that AI will make their jobs better, easier, more efficient. But many employees have heard these promises before. They lived through technology rollouts that created more work instead of less. They endured restructurings framed as “empowerment.” They remember when last year’s three-year strategy quietly disappeared. In low-trust environments, every new initiative enters the workplace with a credibility deficit.

AI is no exception. In fact, AI amplifies the trust issue, because it brings a layer of uncertainty that employees cannot resolve on their own. They want to know:
Will this help me or replace me?
Will my workload actually get lighter, or will expectations just increase?
Will leadership support the learning curve, or will they judge the people who struggle?

If employees cannot answer these questions confidently, they will not adopt AI no matter how powerful the tools are or how many training sessions you offer. They may experiment around the edges, but they will not integrate AI into the core of their workflow.

In high-trust cultures, adoption spreads organically. Employees believe leadership will support them, not expose them. They believe the investment in AI is part of a broader investment in them. They believe the organization is honest about what is changing and what is not.

In low-trust cultures, the opposite happens. AI becomes a symbol of instability rather than opportunity. Employees brace instead of build. Leaders confuse compliance with buy-in and wonder why nothing sticks.

When it comes to AI adoption, organizations that earn trust will move quickly. Organizations that ignore trust will keep announcing AI initiatives that never make it past the slide deck.

Next week, we will get into the mechanics of trust: how employees read signals, what leaders unintentionally communicate, and what it looks like to rebuild trust inside a workforce that has seen too many promises come and go.

SpeedStudio Podcast

EP. 123 - Cole Townsend, Running Supply

In this episode we dive deep into the world of running gear with Cole Townsend. We chatted how running gear stack up to cycling kits, who is leading the game in innovation, the influx of label slapping, why runners don’t wear matching team kits, the influence of Atlanta Track Club and Atlanta Run Club and the different cultures of running and riding in Atlanta vs. Boston.

Make sure to subscribe to Cole’s substack at Running Supply with Cole Townsend Running Supply to get his latest predictions, reviews and recommendations.

Links

OpenAI Is Asking Contractors to Upload Work From Past Jobs to Evaluate the Performance of AI Agents

THE EVENT TRENDS SHAPING IRL CULTURE

220,000 Fewer Workers: How Trump’s Cuts Affected Every Federal Agency

The Downside to Using AI for All Those Boring Tasks at Work

Organizations are asking why their people are not using AI. There is no single reason. There are several, and some are genuinely complex. But one sits underneath all of them. Employees do not adopt new technology when the culture around them has not created the conditions for adoption in the first place.

If you want to understand resistance to AI, you have to start with the employee experience. The NICH +Culture Employee Experience Framework outlines eight categories that shape how people feel about their work and their workplace:

1. Fundamental Needs,

2. Meaningful Work,

3. Leadership,

4. Positive Workplace,

5. Recognition + Appreciation,

6. Health + Wellbeing,

7. Development + Growth,

8. Trust in Organization

The first category, Fundamental Needs, is the foundation. Market-competitive compensation and benefits, the right tools and systems, schedules that make sense, effective onboarding, and basic training. These are the things employees expect an organization to get right before it asks them to take on something new.

When these fundamentals are inconsistent or neglected, everything else becomes harder. Employees become protective of their time and skeptical of new initiatives, especially ones that promise transformation but arrive in a workplace where even the basics feel shaky. And this is where culture asserts itself. Culture is not posters and mission statements. Culture is how consistently the organization delivers on the fundamentals.

If an employee does not trust the company to fix outdated systems, provide clarity, or staff their team appropriately, why would they feel inspired to embrace AI? Why would they spend discretionary effort learning new tools when the existing ones barely get the job done? Why would they believe this initiative will last any longer than the others that quietly disappeared?

This is the piece many miss. AI adoption is not primarily a technology issue. It is a cultural one. It requires stability, clarity, and trust, and those conditions only exist when the employee experience is healthy at its foundation.

So before leaders worry about training plans or prompt-writing workshops, the more honest question is: Have we created a culture where employees want to engage with new technology?

If the answer is no, AI adoption will be slow, uneven, and frustrating. If the answer is yes, employees will lean in because they believe the organization is invested in them, not just the tools.

Next week, we take on another obstacle.

This CEO laid off nearly 80% of his staff because they refused to adopt AI fast enough.

Their website gives vaporware vibes, TBH.

New research from Gartner, Phenom and others reveals AI’s ROI problem.

“The disconnect between AI adoption and business results has reached a critical point.”

U.S. payrolls rose 50,000 in December, less than expected; unemployment rate falls to 4.4%

And the Golden Globe for Best Podcast Goes To … Wait, Who?

It was an honor just to be nominated.

SpeedStudio Podcast:

Ep. 122 – Circle-Back Season and Communities for Everyone

A one-on-one with Nikki and Chad in the Studio, where they subconsciously revert to Culture Edit mode: they chat “circle-back” season, trapped in fake-days, how WFH has increased the extended holiday break in the corporate world, and Nikki learns about the interoffice envelope. They also chat about training for UTMB via ChatGPT (lots of acronyms), being impressed by the Meta Oakley glasses, a knitting deep dive, and how there’s a community for everyone. Speaking of knitting…Jamiroquai’s big hats, they are using Brick’s to focus, and dog park culture: it’s not a free petting zoo for kids (drama). (They aren’t beating the anti-natalists’ accusations in this one.)

Most importantly, they remember Kirk Corsello, a huge loss to the Atlanta peloton, and how the bike connects the community.

We usually open the year with a flurry of workplace predictions. Not this time. There is only one that matters, and yes, it involves AI. At this point, unless you’re in a blue-collar job, AI is the main character, whether we like it or not.

Listeners of our podcast will note that this was our guest John Trainor’s top prediction. He would know, as he’s on the front line watching it happen every day in real time.

2026 will be the year organizations begin evaluating their workforce through one dominant lens: readiness for AI, adoption, and real utilization.

That means assessing, evaluating, monitoring, judging, and making hiring, firing, and layoff decisions based on a single capability. Think about that for a second. We have not seen a technological shift this tightly bound to talent decisions in modern work. The closest parallel takes us back to the late 1990s when email arrived and instantly divided the office into two camps: the people who figured it out before lunch and the people who thought it was a waste of time.

Every office had that guy. The one who asked their assistant to print out emails “because it’s easier to read on paper,” then dictated handwritten responses with arrows, circles, and the occasional Post-it note reading “pls respond.” Entire forests died so this man could reply, “Sounds good.” He would not survive 2026. He might not survive the first all-hands meeting where someone says “machine learning workflow” out loud.

The reason this matters now is simple. AI is not creeping into the workplace; it is hitting the gas for full-throttle. Most had not even heard of it in November 2023. Now, every white-collar organization is staring at the same inflection point Blockbuster faced when Netflix was still shipping DVDs. Move quickly or get very good at explaining to future historians why you missed the most obvious transformation of your era. This is every sector, every function, every team.

Over the next few weeks, we will explore what this actually means. Not theoretically, but in practical terms: how organizations will struggle with evaluating AI adoption. Why organizations will be perplexed that their workers aren’t that jazzed about it.

In summary, it’s because they have never built the employee experience muscles required to support it. If people do not understand expectations, do not trust leadership, and do not feel equipped to learn new skills, there is no AI adoption strategy. There is only a press release with a robot graphic.

More next week.

SpeedStudio Podcast

Ep. 121 - Predictions with John Trainor

In the final episode of 2025 we give the people what they want. Predictions, podcast stats and the one and only John Trainor. We chat AI, technology, sports, fashion and much more. At the risk of giving any spoiler alerts, you’re just going to have to give it a listen to find out more. Happy New Year!

Americans Hate AI. Which Party Will Benefit?

“Party insiders are divided on how to channel Americans’ growing fear of AI.”

US employers face labor-shortage headaches amid declining fertility rate, immigration crackdown

Huh?

Welcome to the Future of Noise Canceling

In Japan, the Philosophical Stance Against Having Children

The Analog Life: 50 Ways to Unplug and Feel Human Again

With Christmas Eve falling on a Wednesday and Christmas on Thursday this year, the entire workweek lands in a no-man’s-land. It’s a week suspended in midair.

Some companies may pretend this week functions like any other. It doesn’t. It’s the most politely unproductive stretch of the year. People show up, answer a few emails, and keep the lights on, but no one is grinding.

And there’s nothing wrong with that. This week has a different purpose.

Instead of forcing momentum, it can be the perfect window for the things that always get pushed aside:

· cleaning up administrative clutter

· closing out loose ends that clog the system

· paying vendors before year-end (wink wink)

· tightening plans and priorities for 2025

· taking a breath long enough to see the larger picture again

It’s a natural reset button. January tends to hit fast. Leaders who use this quiet week to reflect, recalibrate, and outline what actually matters next year arrive with a level of clarity that others won’t.

A dislocated holiday week isn’t a productivity challenge. It’s an opportunity.

Culture Predictions for 2026

“Scrolling will be viewed as a vice, in the same impropriety quadrant as vaping. In an attempt to mitigate brainrot, which has already become the norm, expect an appetite for more satisfying, longer forms of consumption as we move away from shortform video and AI slop: longform narratives, books, and other media that make our smooth brains work a little harder.”

US jobless claim applications fell by 13,000 last week as layoffs remain low

Shaky data, though.

Trump ordered 2 new federal holidays. What does it mean for Americans?

It really only impacts federal worklers. The private sector has been taking these off for years.

Teens, Social Media and AI Chatbots 2025

“Roughly 1 in 5 U.S. teens say they are on TikTok and YouTube almost constantly. At the same time, 64% of teens say they use chatbots, including about 3 in 10 who do so daily”

A $600 Suckling Pig? Wagyu for All? On Menus, It’s a New Gilded Age

“In Manhattan and across the country, restaurants are trotting out ever-pricier dishes and luxury upgrades to meet the demand from affluent diners.”

The Art of Dailiness

The esteemed graphic designer shares an essay and drawings from his legendary 100 Day Project, showing what vital roles routine, practice, observation and dedication play in the creative process.

Why Video Podcasts Multiplied Beyond the Man Cave

“Whether you’re a pundit, a politician, or an A-list comedian, the best media strategy these days is a D.I.Y. stage set and a microphone.”

Glamour, thrill and alien-core: Pinterest predicts 2026 trends

A big year for cabbage in on the way!

Where people moved to (and from) in 2025

SpeedStudio Podcast:

Ep. 119 – Out of Retirement, Knitting, Marathon Fakes, and the Holidays

On this one-on-one episode, Nikki and Chad discuss the podcast unicorns, athletes coming out of retirement, new hobbies, Super-Halves, people faking their marathon time and the IG account calling them out, holiday lists, Kipchoge possibly moving to Saudi Arabia, and experiences over gifts.

The forgotten generation. Maybe not so much these days. By our rough calculations, over the past year, more than thirty feature stories in major publications have focused on Gen X. It’s been culture pieces, identity essays, economic reports, and, of course, fashion editorials. The slacker generation is suddenly being acknowledged as a force shaping culture, consumer behavior and creative direction.

We think that a big part of the renewed interest comes from how this generation engaged with culture in the first place. For Gen X, “cool” wasn’t something handed to you by your feed. It required intention. A lot of it. You had to search, wander, trade, and discover. Music, art, fashion and ideas didn’t arrive via algorithm. They lived in record stores, book stores, independent magazines, midnight broadcasts (120 Minutes), and scenes that weren’t always easy to find. That process created a different relationship with culture. It wasn’t disposable.

We also note that Gen X also had the advantage of growing up during a very specific window of innovation. Technology opened creative doors, but it didn’t overwhelm them. Cable television, early gaming, cassettes, CDs, zines and niche publications introduced new worlds without drowning out individuality. The internet hadn’t flattened everything yet.

That balance built a certain creative muscle. The generation learned how to make things without templates, edit without algorithms, and generate ideas without relying on data to confirm them. It was a training ground for originality.

Today? Yes, we are advocates and believers that AI offers incredible tools. But they also increase the risk of sameness. People are starting to sense the difference between content created for reach and ideas created with intention. The instincts Gen X developed long before digital overload, like discernment, independence, and a willingness to experiment, create an advantage in this environment. They allow for creative choices that don’t feel manufactured by the system they’re competing within.

The interest in Gen X right now reflects this shift.

It’s “prediction season” after all, and ours is that influence is tilting back toward substance, originality, and taste. The traits that shaped Gen X long before anyone called it a trend. Algorithms can replicate patterns, but they can’t produce edge, depth or timing. Creators who developed those instincts in the analog world still stand out in the digital one.

Our goal is to understand where culture is heading, and this offers a clear signal. In a landscape searching for something genuine again, it makes sense that the spotlight has landed here.

Sweet.

Is Gen X Actually the Greatest Generation?

“It’s a moment, a mood, an ethos and an enduring way of being, the hallmark characteristics of which — anticorporatism, anti-authoritarianism, ironic detachment, artistic independence, an existential horror of selling out and a live-and-let-live philosophy of life — feel like the antidote to a lot of what’s currently wrong in our culture.”

Is Gen X Nostalgia Just Trauma-Bonding?

“There is something a little sad in all the memes that try to capture the essence of my generation of latchkey kids turned cynical slackers.”

What If Gen Xers Are Actually the Cool Ones?

“But, look, some of the coolest people to ever grace the planet are Gen X: Chloë Sevigny, Alexander McQueen, Winona Ryder, Naomi Campbell, Kate Moss.”

How Gen X is leading the world in consumer spending

“Gen X’s outsized influence on consumer spending can be attributed to the fact that many members are at the centre of business and family life.”

Gen X Characteristics in 2025: Insights & Data for Brands and Creators

“Gen X is a planner. Unlike younger consumers who browse frequently and make impulsive purchases, this generation tends to schedule purchases around key seasons and life events.”

Justice for Gen X

“The ‘whatever’ generation is ‘whatever’ about being overlooked”

Make Culture Weird Again

“For all the energy society invests in culture today, little has emerged that feels new, and certainly nothing revolutionary enough to properly outmode the past.”

Layoff announcements top 1.1 million this year, the most since 2020

“Despite signs of weakness elsewhere, Labor Department data has yet to reflect a surge in layoffs.”

This Spotify Wrapped season, don’t outsource your love of music to AI

You can tell who lets the algorithm drive.

The Boom Before the Bust

“From caviar and Dom Pérignon at Mach 2 to the fatal Air France crash of 2000, former members of the Concorde crew revisit the era of supersonic flight”

The top running shoe on Strava in 2025 isn’t the Pegasus, Clifton or Vaporfly

What’s your guess?

Duplantis and McLaughlin-Levrone named World Athletes of the Year

SpeedStudio Podcast

Ep. 117 – Flattening Culture, Design Impact, and Performative Sobriety

A one-on-one with Nikki and Chad in the Studio finds them contemplating end-of-year perspectives, the top 5 best steaks ever, a Staplehouse re-discovery, what to tip for counter service, and judging Chad’s 18%. They also chat the flattening of pop culture, resisting fame by not wanting to sell out, how it impacts design in the endurance world, the upcoming SpeedStudio kits, aligning Basso Bikes’ design, and how Nikki’s into white bibs. They somehow then transition to drinking culture, how today’s “sober” doesn’t mean actual sober, how it impacts relationships, and a hot take: sober culture is performative because Americans need to party more.

Everywhere you get podcasts.

Here are some questions that move things into “real” territory quickly. Use them to open a team offsite, kick off a leadership session or reset a group that’s gotten a little too tactical for too long.

No need for icebreakers or gimmicks.

Just ask and listen.

The answers will tell you everything you need to know.

1. Name three people outside your immediate family who have influenced your life.

2. What are three moments or events that genuinely shifted how you think or who you are.

3. If you had ten years left, what would you do differently tomorrow.

4. Tell what part of you have you sidelined. (The thing you wished you had done).

Purposeful Work Boosts Engagement, but Few Experience It

“Only 13% of employees with strong work purpose report feeling burned out “very often” or “always,” compared with 38% of those with low purpose.”

PwC’s Global Workforce Hopes and Fears Survey 2025

“With trust, cultural support, and clarity about workplace changes in an age of AI, leaders can boost employee motivation while igniting reinvention and growth.”

They Came for Timothée. They Left With $250 Windbreakers.

The Marty Supreme PR masterclass is in session.

The Oxford 2025 Word of the Year Is ‘Rage Bait’

We are furious.

5 Trends That Will Shape Workplace Culture In 2026

Here come the prediction lists!

Playlist

Class of ‘96

Inspired by a late afternoon charcuterie at Staplehouse.

SpeedStudio Podcast

Ep. 116 – Aaron Gouw, Carbs Fuel

This week we are in the studio and joined by Aaron Gouw, Co-founder & Chief Product Officer of Carbs Fuel, a new sports nutrition company making a huge impact in the endurance world. The intro finds Nikki and Chad in the Studio pre-Thanksgiving where they chat house parties, un-massaged kale, the Marty Supreme PR strategy, and the protein craze v carbs revolution.

They chat with Aaron about riding in Golden, founding Carbs remotely, the importance of simplification and affordability, grams of carbs per dollar, girl math and athlete math, washing gels, branding process, developing product, pitching to manufacturers, and why you don’t ask for MOQ’s. They also go deep on what causes GI distress, runners versus cyclists fueling views, what elite level marathoners require, short crank arms (155’s!), the future of CARBS, and why they won’t be doing a cocktail line (Chad’s idea).