Which camp are you in with AI?
2026 is likely the year for a ‘tectonic shift’ in AI adoption. Are you ready?

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If you have been around technology long enough, you develop a sixth sense for when something is a fad, when something is a feature, and when something is the next tectonic plate shift that quietly rearranges your entire career while you are still arguing about the user interface. AI is in that third category. Not “because I saw a demo.” Because the money, the regulation, the infrastructure, and the adoption curve are all lining up at the same time. That convergence is why 2026 is not just “another AI year.” It is likely the year that dictates direction and velocity, meaning what gets built, who gets to deploy it, and how fast it moves from clever pilot projects to the thing your boss assumes should work by next Tuesday.

And yes, I can already hear the groan from the back row. “Fletch, you have been calling every year a turning point since dial-up.” Fair. But this time we have receipts. Microsoft CEO Satya Nadella wrote, “there’s no question 2026 will be a pivotal year for AI.”  You do not have to agree with his optimism to recognize the significance of a major platform vendor publicly framing 2026 as the pivot. When the people selling the shovels say the gold rush is changing phases, you pay attention.

The facts that make 2026 look like the report card year

Here is the simplest way to justify the premise without hand-waving or sci-fi dramatics:

• Regulation stops being theoretical. The EU AI Act entered into force August 1, 2024 and becomes “fully applicable” on August 2, 2026, with staged obligations leading up to it. 
• Infrastructure spending is already committed. JLL reports hyperscalers allocating $1 trillion in data center spend between 2024 and 2026. 
• The capital markets are bracing for AI-driven buildout. Reuters reported analysts projecting higher U.S. corporate bond issuance in 2026, driven largely by hyperscaler AI infrastructure financing needs. 
• AI spend forecasts are exploding into “board-level” numbers. Gartner forecast worldwide AI spending at $2.52 trillion in 2026, up 44% year over year. 
• Policy people are treating 2026 as consequential. CFR framed 2026 as a year that could help decide AI’s future because implementation of new rules collides with urgent debates about autonomous systems. 
• Risk management is no longer optional for grown-ups. NIST’s AI Risk Management Framework exists for a reason, and it is increasingly the backbone for “prove you did this responsibly” conversations. 

That is not hype. That is the collision of enforcement timelines, infrastructure commitments, and corporate adoption pressure. In plain English, 2026 is when the world starts asking, “Show me the value, show me the controls, and show me who is accountable when it breaks.”

We have seen this movie before;
and the VHS tape is labeled – VoIP

Let me drag you back to the tail end of the 1990s. Voice started getting digitized on networks. Some innovators realized you could break voice into ones and zeros, move it as data, then reconstruct it on the far end. Around the same time, Ethernet and IP networking were becoming the default plumbing. Two technologies got “slammed together” and we called it VoIP.

Cue the backlash. Career TDM phone engineers (aka ME) scoffed. They had an entire catalog of reasons why IP was not the way forward. Some of the concerns were legitimate. Early VoIP had jitter, packet loss, bad QoS, flaky power designs, and the general vibe of a science fair project duct-taped to a production environment.

Then the industry did what it always does. It matured. Networks improved. Engineering adapted. The excuses went away, not because the critics were dumb, but because the ecosystem got better. Most systems today are VoIP-based. Does it have limitations? Absolutely. Is it as inherently resilient as old-school TDM in every scenario? No, it is close, but not identical. The point is that it works, and the benefits outweighed the shrinking list of negatives.

AI is sitting in that same awkward adolescence right now. A very vocal group of non-believers is armed with horror stories. Here is the annoying part: many of those horror stories are true, or at least plausible, which means they are emotionally effective. But “possible failure” is not the same as “inevitable failure.” It just means you need guardrails.

The human race also split into three camps when computers showed up

This is the part people conveniently forget. It was not long ago that computers were literally large iron boxes living in dedicated rooms, making decisions for us, or at least spitting out answers we were supposed to treat like gospel.

Back then, the population broke into three rough categories:

1. The non-believers. They did not trust the machine, did not want to understand it, and therefore never experienced the direct benefit.
2. The builders and operators. The experts who built, programmed, and maintained those machines. This group excelled because they understood power and limitations.
3. The practical users. The people who did not care how it worked, but learned how to apply it to get better outcomes for themselves.

Those early systems ran on Boolean logic. IF, THEN, ELSE, and, OR. Predefined rules. Programmed references. Very crisp. Very deterministic. Also very limited, because the world does not always fit in a clean if-then box.

But the winners were not the people who worshiped the machine. The winners were the people who understood which tool to use for which job. Like a laborer knowing when to use a flat shovel for scooping and a pointed shovel for digging. Either shovel can move dirt, but each is designed for specific efficiencies that make it the better choice.

That is the heart of the AI debate today. Not “is AI good or evil.” The real question is whether you understand the tool well enough to apply it appropriately, and whether you are going to be the person who benefits from the results, or the person whose work and life change because you learned to use it.

Guardrails are not censorship; they are seatbelts

We put steel guardrails on curves for a reason. Most drivers don’t need them. They take the curve at a safe speed and never touch the rail. But physics doesn’t negotiate with optimism. If a vehicle enters that curve too fast, traction is lost and the car slides to the outside. The guardrail arrests that movement, ideally preventing a much worse outcome.

AI needs the same concept, especially when new, and especially during training and early deployment. In the AI world, guardrails look like: defined scope, known data sources, audit trails, human oversight, fallback modes, and clear responsibility. NIST’s AI Risk Management Framework exists because real organizations need a structured way to manage AI risk, not vibes and motivational posters. 

And while we are talking about tools, let’s get this out of the way:

• Hammers drive nails.
• Calculators do math.
• If you use your hammer for math, you can count to one on it.
• If you use your calculator as a hammer, the answer you get is, “calculators don’t make good hammers.”

A lot of “AI is dangerous” stories are actually “someone used the wrong tool for the job” stories. Or worse, they used the right tool with no guardrails and then acted shocked when physics showed up.

AI is empowering. I am living proof

Over the past 2-years I have increased the frequency of my blogs and podcasts with the help of AI. The key phrase is “with the help.” I still do the work. It is still my voice. Still my ideas. But instead of hiring a warm-blooded researcher to hunt the internet for sources, trends, and background, I can use an AI tool to generate candidate topics, outline structures, and prompts in minutes, at minimal incremental cost.

Can the output contain wrong data? Absolutely. That’s why cross-checking exists. Anyone who treats AI output as scripture is not “an early adopter.” They’re an accident report waiting to happen.

I also use AI for spelling and grammar, and I train it on what is good and bad content in MY unique world. After two years of training, I can dictate rough thoughts in digital shorthand and get back an outline that is often 75% of the way there. Then I edit, digest, and decide if it matches what I want. After a round or two, I hit record. I edit on the fly while recording. Sometimes I get 90% through a project and toss it because it does not feel right. That is not AI writing for me. That is me using a better shovel.

So why does this matter to 9-1-1, and why do I keep poking the bear?

Because public safety is where bad assumptions go to die. If you hate AI, you are probably imagining it doing something it should not be doing. Like being the primary answer mechanism for 9-1-1 calls, right now, as a full replacement for a trained call taker. In 2026, that is still a risky idea in most environments because failure modes are unacceptable.

But let’s talk about reality. When the snow is flying, you are already short-staffed by 40%, and two people are covering the workload of five or six, AI can be a godsend as an assistive layer. Not as a replacement for judgment, but as a support system that helps with structured intake, language translation, callback validation, location hints, and rapid triage prompts. If you build the guardrails, define the scope, and keep a human in control, the “AI in 9-1-1” conversation stops being science fiction and starts being operational math.

And that circles us right back to 2026. This is the year where the world is going to demand facts, not vibes. The EU is moving from staged obligations to broad applicability in 2026.  The money being poured into AI infrastructure is measured in numbers that make normal budgets look like lunch receipts.  Capital markets are already modeling the borrowing needs tied to AI buildouts.  Policymakers are openly framing 2026 as a consequential year for governance and autonomy debates. 

So here is my snarky, fact-demanding bottom line. AI is a tangible shift in evolution that sets the pace for the future, not because the demo is impressive, but because the ecosystem is committing to it. The question is not whether AI will be used. The question is whether you will be in the camp that refuses to understand it, the camp that builds and governs it, or the camp that learns to use it properly and benefits from the results.

Pick your camp.

Mother M-AI isn’t waiting for permission.

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Thanks for spending time with me; I look forward to next time. Stay safe and take care.

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