Something Big Is Happening, & Most Aren’t Paying Attention
When Comfortable Assumptions Meet an Uncomfortable Reality
By: Michael K. Warne, AAMS®
There’s a version of reality that exists almost exclusively on X, or what I still call Twitter. It’s not fake, exactly. But it is a different planet from the one most people are living on. And the gap between those two planets is becoming one of the most important stories nobody is covering.
On one planet, AI researchers, startup founders, and a small but intense online community track every model release the way meteorologists track a forming hurricane. They post at 3 AM about their autonomous agents. They thread together lists of “crazy AI articles this week,” and every week is the most insane week in AI yet. On the other planet—everywhere else, in boardrooms, city halls, fire stations, and living rooms—most people think AI is “like a better Google” and haven’t given it much thought beyond that.
The gap between those two planets is where the consequences are going to land. And it’s closing faster than almost anyone on the second planet understands.
I recently read a whitepaper that I haven’t been able to stop thinking about. It’s called “Something Big Is Happening,” written by Matt Shumer, a six-year AI startup founder, investor, and CEO of HyperWrite.¹ He wrote it not for the tech world, but for the rest of us: his family, his friends, and the people who keep asking “so what’s the deal with AI?” and getting the polished cocktail-party answer. He decided it was time for the unvarnished one.
My gut reaction was a jolt of unease. Not the paralyzing kind, but the kind that sharpens your focus. Then something else kicked in, something that comes from having watched markets, technology, and human adaptability for a long time. Perspective.
What’s Actually Happening Right Now?
Shumer describes a moment earlier this month when he gave AI a detailed description of what he wanted built, walked away for four hours, and came back to find it finished. Not a rough draft. The actual finished product, done better than he could have done it himself.¹ This is a guy who builds AI tools for a living, and even he was shaken by how fast this is moving.
Three years ago, AI couldn’t reliably multiply seven times eight. Two years ago, it could pass the bar exam. Last year, it was writing software that actually works. Today, it can sit down and do real, complex work, by itself, for nearly five hours straight, and that number is roughly doubling every few months.² The gap between “AI is a fun toy” and “AI just did a full day’s work” is closing faster than almost anyone outside the industry expected.
What really caught my attention: the companies building the latest AI have acknowledged that the technology helped create itself.³ Each version is now smart enough to help build the next version, which is smarter, which helps build the next one faster. The CEO of Anthropic, one of the top AI companies in the world, believes this self-reinforcing cycle has already begun.⁴
Shumer compares this moment to February 2020, right before COVID upended daily life. He believes we’re in the “this seems overblown” phase of something much larger.¹
We’ve Been Here Before—But Not at This Speed
Here’s where Shumer’s piece, as compelling as it is, deserves a broader lens. This is a moment of real disruption, but it’s not our first one, and history suggests we’re better at adapting than we give ourselves credit for.
When the internet went mainstream, entire professions were declared dead. Travel agent positions dropped roughly 70%.⁵ Newspaper classified revenue collapsed. Brick-and-mortar retail was permanently altered. But the internet didn’t just eliminate jobs. According to the McKinsey Global Institute, it created 2.6 new jobs for every one it displaced.⁶ The automobile did the same thing a century earlier. The pattern is consistent: transformational technology disrupts, then creates, and society on the whole ends up better off.
That doesn’t mean the transition is painless. But historically, we’ve had time to adjust. Travel agents had two decades to evolve. Newspapers had years to build digital strategies. The automobile took a full generation to reshape how Americans lived and worked. This time, the compression is different. AI capabilities that took months to develop a year ago are now arriving in weeks. The adjustment window is narrower, which means the people who engage early aren’t just better positioned. They’re operating on a fundamentally different timeline than those who wait.
A Healthy Dose of Skepticism
This is where my decades of market experience inform my thinking. When everybody crowds onto one side of a trade, the trade often goes the other way. I’ve watched it happen too many times to ignore that pattern.
Shumer’s piece is compelling, and I believe the trajectory he describes is real. But predictions about timing are notoriously unreliable, and transformational technologies rarely unfold in a straight line. The reality is probably somewhere between “this is overhyped” and “the world changes next Tuesday.”
And here’s the part that doesn’t get said enough: many of the loudest voices in the AI conversation have very specific incentives to sound certain. Founders need you to believe their AI startup is inevitable so they can raise their next round. Venture capitalists need you to believe $660 billion in AI capital expenditure is rational because they’re already deployed into it. Employees at major labs are brilliant, but they also hold stock options tied to the perception that their model is the one that changes everything. None of these people are necessarily lying. But they are all operating within incentive structures that reward confident prediction and punish nuance.
The honest answer to “what happens next” is: we don’t know. The models are exhibiting behaviors their creators didn’t predict. The economic effects are showing up in places nobody was watching. And the interaction between AI and existing systems—legal, bureaucratic, cultural—is producing outcomes that no forecast model captures.
What This Already Looks Like in Our World
I don’t have to rely solely on Shumer’s account, because I’m already experiencing this firsthand. Tracy and I launched Synergy Capital two years ago after a combined three decades at large international banks. I knew how to run a practice, manage portfolios, and communicate complex concepts in plain language. What I didn’t know was how to lead a firm or market one. Over the past two years, through trial and error, wise counsel from experienced friends, and the deliberate use of AI, I’ve been through an intensive education in both. AI compressed that learning curve in ways I wouldn’t have thought possible.
At Synergy Capital, we use AI to compile research, review contracts, maintain regulatory compliance, and distill lengthy whitepapers that used to consume an entire weekend. When we were suddenly shorthanded last year, AI was part of what enabled a three-person team to keep operations running until we could rebuild. That’s not hypothetical. That’s a regular Tuesday.
What This Means for You
If you lead a team, manage a budget, serve on a board, or carry responsibility for people and outcomes, this piece is for you. I know many of you are already stretched impossibly thin. You’re juggling operations, personnel, compliance, board presentations, and community expectations, often simultaneously. The last thing you need is another item on the list. But AI isn’t an item on the list. It’s the thing that’s about to reshape the list itself.
AI is not going to replace a firefighter making entry into a burning structure. It’s not going to replace an operator running a water treatment plant at 2 AM during a main break. It’s not going to sit in the chair at a board meeting and explain a rate increase to concerned residents. The physical, relational, and leadership work that defines public service isn’t going anywhere.
But here’s where the real world gets messy. The department head in Omaha whose workflow is about to be restructured around an AI tool she’s never heard of? She’s not on X reading about it. Her organization’s IT policy hasn’t been updated since 2019. The gap between what’s technically possible and what actually happens inside bureaucratic reality is enormous, and that gap is where the real adjustment pain will be felt.
The work that surrounds the core mission? That’s already shifting. AI is being deployed for predictive maintenance, real-time monitoring, administrative automation, demand forecasting, and budget modeling across fire, water, sanitation, and municipal operations.⁷ ⁸ ⁹ For leaders already wearing multiple hats, AI is the kind of force multiplier that buys back the one thing none of us can manufacture: time.
The leaders who engage with these tools early, who experiment and build institutional knowledge before the pressure is on, will be the ones best positioned to serve their communities. Not because AI replaces their judgment, but because it frees up more of it for the decisions that actually require a human being in the room.
What I’m Telling My Daughters
Shumer urges parents to rethink the standard playbook: good grades, good college, stable professional job. He argues it points directly at the roles most exposed to disruption.¹ My daughter Sadie is at CU Boulder studying Statistics and Data Science, already using AI to augment her coursework. I still encourage my middle daughter toward cosmetology school. Cosmetologists earn a strong living, and AI isn’t displacing them anytime soon. My advice to all three of my girls is the same: learn a skill that AI can’t replace, or master one that AI can dramatically amplify. Either path works. Standing still does not.
Prepare, and You’ll Be Fine
If history teaches us anything, it’s that the people who leaned into technological change didn’t just survive it. They thrived. The pattern repeats because human beings are remarkably good at adapting when they choose to engage rather than resist. The difference this time is the speed. The window for gradual adaptation is narrower than anything we’ve experienced before, and the people who wait for the knock may find the door already open.
Shumer closes by saying the future has already arrived. It just hasn’t knocked on your door yet. I think he’s largely right, with the caveat that when everyone is leaning the same direction, it pays to keep your eyes open. But I’d rather be early and curious than late and scrambling. And if we prepare, as we always have when faced with transformational change, we should be just fine. We might even look back and realize this was the moment things got significantly better.
If you’re interested, Shumer’s full piece is worth the time: Full Paper
Stay curious. Stay adaptable. And don’t wait for the knock.
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Sources
1 Matt Shumer, “Something Big Is Happening,” February 9, 2026. shumer.dev/something-big-is-happening.
2 METR (Model Evaluation & Threat Research), AI task autonomy benchmarks, as cited in Shumer (2026). metr.org.
3 OpenAI, GPT-5.3 Codex technical documentation, February 5, 2026, as cited in Shumer (2026).
4 Dario Amodei, CEO of Anthropic, public statements on AI development feedback loops and timeline projections, as cited in Shumer (2026).
5 U.S. Bureau of Labor Statistics data, as analyzed by TravelPerk, “How Online Booking Has Changed the Travel Agent Landscape,” 2023. travelperk.com.
6 McKinsey Global Institute, research on internet-era job creation and displacement, as cited in CareerFAQs, “10 Jobs That the Internet Created.” careerfaqs.com.au.
7 U.S. Bureau of Labor Statistics, Occupational Outlook Handbook: Travel Agents. Projected 20% employment growth, 2021-2031. bls.gov/ooh/sales/travel-agents.htm.
8 World Travel & Tourism Council (WTTC), global tourism employment data. wttc.org.
9 Sources include: Western Fire Chiefs Association, “New AI Technology in the Fire Service,” October 2025 (wfca.com); Fire Engineering, “From the Firehouse to Fireground: How AI is Reshaping the Fire Service,” January 2026 (fireengineering.com); FireRescue1/IAFC Technology Summit, “Summit Explores New Tech, AI and the Shift to Data-Driven Models,” July 2025 (firerescue1.com).
10 TD SYNNEX Public Sector, “AI for Water: 10 Ways AI is Changing the Water Industry,” January 2025, citing EPA data. dlt.com. See also: Xylem Vue, Water Technology Trends 2025, xylem.com.
11 U.S. Environmental Protection Agency, municipal water and wastewater energy consumption data, as cited in TD SYNNEX (2025).