Bite: Ohhh, here we go again—buckle up!
Tweak: Say, do ya remember that glorious Fry’s Electronics down on Bascom Ave? Ohhh yeeeaaaa!, what a palace! That place was like Disneyland for gearheads back in the dot-com craziness. You’d walk in and—bam!—wall-to-wall motherboards, glowing CRTs stacked to the ceiling, and those wild themed aisles that made ya feel like you’d wandered into ancient Egypt or a pirate ship just to grab a new hard drive. Great store, huh?
Bite: The best! yeah… loved that joint. We’d roll in after midnight coding sessions, pockets stuffed with stock-option dreams and just enough cash for a power supply and a jumbo bag of gummy worms. And down the street—practically spitting distance—was eBay HQ, pumping out millionaires faster than Fry’s could stock floppy disks. We’d joke that if your startup didn’t tank, you’d upgrade from swapping an 8 megahertz crystal to a 16 megahertz one on your IBM 8088 just to double the speed… and celebrate with a new joystick from Fry’s!
Tweak: Those were the days, eh? When “going public” meant something, and “going to Fry’s” meant you were about to build the future… one overpriced cable at a time. Ahh, youth. Pass the Cheet’s and Dew , will ya?
If you were born in the 90s or later, this whole AI surge probably feels like something brand new—tech that’s always been part of life, no memory of a world without instant digital everything. Calculations? Done on screens, not paper or in your head. I still crack the joke about “finding the on-switch for a pencil” and watch the blank stares roll in. It’s a reminder of the generational divide: younger folks grew up with tech as a constant, while those of us from earlier eras remember the real shifts—vinyl to 8-tracks to cassettes to CDs, each promising the future until the next one made your investment feel wasted. Exciting? Absolutely. Infuriating when formats died overnight? Often. But living through those transitions taught a core lesson: tech evolves in predictable cycles, not straight lines.
Here in January 2026, we’re deep into one of those cycles with generative AI. Gartner’s latest analyses confirm GenAI has moved past the Peak of Inflated Expectations and into the Trough of Disillusionment—where organizations confront real limits, modest ROI in many cases, and the hard work of scaling beyond pilots. Reports from sources like Deloitte, PwC, and Forbes highlight a shift: adoption is widespread (over 70% of companies using AI in some form), but the focus is moving from broad hype to measurable outcomes—efficiency gains, cost reductions, and targeted applications rather than revolutionary overhauls for everyone. Small businesses, in particular, are leaning in practically: LinkedIn and U.S. Chamber data show AI powering growth engines through automation in marketing, operations, and customer service, with many owners reporting positive impacts without massive spends.
But this isn’t unprecedented. It echoes the 90s dot-com boom vividly. Back then, the internet promised to remake everything—billions poured into infrastructure, startups with big ideas and little revenue, valuations detached from reality. Governments and institutions saw strategic value, much like today’s backing for AI compute races and policy support for big players. Most early entrants burned through cash and collapsed in the bust; survivors (Amazon, Google) built on fundamentals and endured. Parallels today include massive infrastructure investments (data centers, chips), soaring valuations for AI-labeled companies, and warnings from figures like Michael Burry or even OpenAI’s Sam Altman about overexcitement. Yet, differences matter too: today’s leaders (Microsoft, Google) have proven revenue models and profitability paths the dot-com era often lacked.
The key takeaway from having seen this before? Cycles have phases—hype builds, reality tempers it, shakeouts happen, and winners emerge—but the infrastructure and tools that survive create lasting foundations everyone can use. Governments backing the giants (via strategic compute investments and regulations) means the endgame likely favors entrenched players who can sustain the burn rate. Most new heavy-spenders and speculative players won’t. But small businesses aren’t in that race.
That’s where strategic planning comes in—and why experience across cycles gives a clearer view of plausible success. Instead of reacting to daily headlines (“Adopt AI or die!”), step back and recognize the pattern: Let the hype play out while positioning pragmatically for whatever landscape settles. The future isn’t about winning the AI arms race; it’s about building resilience so your business thrives no matter who dominates the foundational layer.
Practical steps informed by those who’ve watched tech waves rise and fall:
- Assess fundamentals first —
- Start narrow and measure —
- Build incrementally with existing tools —
- Plan for the long game —
- Stay grounded in cycles —
What does that all mean!?!… All covered in more detail in private consultations or small/mid company seminars.
Small businesses have an edge in agility—no bureaucracy slowing decisions. Use it to implement AI where it solves real problems today, while keeping an eye on the cycle’s progression. The giants may own the rails, but you own your path.
If this perspective resonates and you’re thinking about how these patterns apply to your own operations, the site has more on grounded AI approaches worth exploring.