AI Hype Cycle Bingo: A Field Guide to Spotting Tech Industry Snake Oil

This is part of a series of articles exploring artificial intelligence (AI) and its impact on our lives, told from the perspective of a technology industry veteran, though not an AI expert, yet. If you want to start at the beginning check out the series page.

Every AI announcement these days sounds like those 1950s radio ads for miracle tonics. "Guaranteed to revolutionize your business! Transform entire industries! Achieve human-level intelligence in months!" The only things missing are the earnest voiceover and the claim that it'll also cure your sciatica.

We're deep into a hype cycle so predictable you could set your calendar by it. The pattern repeats with remarkable consistency: breathless announcements, eye-watering valuations, frantic investor pile-ons, and eventually, the sobering realization that reality doesn't match the demo. If you've lived through the dot-com bubble, the blockchain revolution, or the metaverse moment, this script should feel familiar.​

Here's the uncomfortable truth: AI is both genuinely transformative and wildly overhyped simultaneously. Sorting signal from noise requires developing a sophisticated BS detector. Consider this your field guide.

The Hype Cycle Playbook: A Predictable Performance

The Gartner Hype Cycle describes five stages that technologies pass through: innovation trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. By mid-2025, AI sits squarely at the peak—possibly starting its descent.​

The signs are unmistakable. AI funding surged to over $200 billion in 2025, dwarfing the $135 billion SaaS bubble of 2021. For context, at the height of the dot-com boom in 2000, internet companies raised just $10.5 billion (about $20 billion adjusted for inflation). We're not in familiar territory—we've entered territory that makes previous bubbles look quaint.​

Market concentration mirrors past bubbles with eerie precision. The "Magnificent Seven" tech firms now represent over one-third of the S&P 500—twice the concentration of top tech companies during the 2000 bubble. Nvidia alone has been valued nearly equal to Canada's entire economy. When single companies achieve nation-state-scale valuations, alarm bells should ring.​

The capital flows reveal investor psychology. In Q1 2025, AI startups raised $73.1 billion globally, representing 57.9% of all venture capital funding. That's not diversification—that's mania. Bryan Yeo, chief investment officer at Singapore's sovereign wealth fund GIC, summarized it perfectly: "Any startup with an AI label tends to be appraised at extraordinarily high multiples relative to their modest revenues".​

The hype is cooling, but not because problems are solved—because investors are remembering that technology needs to generate actual returns. By mid-2024, AI funding had declined nearly 30% year-over-year. The market is narrowing from moonshots to domain-specific applications that demonstrate clear business value.​

Buzzword Bingo: Decoding the Hype Dictionary

The AI industry has developed its own dialect, where everyday words acquire magical properties:

"Revolutionary"– Translation: We added AI to an existing product and hope you don't notice it's basically the same thing with a chatbot interface.

"Game-changing"– Translation: Our marketing team needed a superlative, and this one tested well with focus groups.

"Human-level AI"– Translation: Our system can sometimes produce output that looks human-generated if you squint and don't check the facts too carefully.

"Transformative"– Translation: We genuinely believe this will change things, but we're not exactly sure how or when.

"Disrupting [insert industry]"– Translation: We're losing money faster than our competitors, but calling it "market expansion."

The tells are consistent. When companies use multiple buzzwords in a single sentence—"Our revolutionary AI platform delivers game-changing, transformative solutions"—you're witnessing marketing, not substance. Legitimate breakthroughs rarely require promotional overkill.​

Watch for qualifiers too. "Up to" (as in "improves productivity up to 10X") means most users see far less. "In controlled settings" means it won't work in your messy real-world environment. "Early results suggest" means we don't have actual data yet, but we needed something for the press release.

The Demo vs. Reality Gap: Theater of the Technically Possible

Tech demos have always been performance art. They showcase what's theoretically achievable under perfect conditions with unlimited resources and careful cherry-picking of examples. Real-world deployment is where ambitious claims meet stubborn reality.​

The pattern is well-documented. Demos are built for ideal conditions(I've built a lot of them over the years): clean data, clear problems, perfect environments. Businesses have messy data, siloed systems, and evolving needs. That's where tools consistently fall short.

One YouTube consultant captured it perfectly: " Demos are set up for the perfect use case. Perfect data environment, perfect problem, perfect solution. But your data is messy, your sources are disparate, the documentation isn't clear, everything isn't set up perfectly". Companies get sold on solutions based on 90-second videos, then discover implementation requires six months and three times the budget.

OpenAI's October 2025 launch of the ChatGPT Atlas browser illustrates this gap beautifully. The company demoed impressive browsing capabilities, but TechCrunch testers found only marginal efficiency improvements in practice. The demo looked revolutionary; the reality was incremental.​

Historical precedent reinforces this pattern. During the early 2000s, graphics card companies produced stunning tech demos that showcased theoretical capabilities—visuals games wouldn't match for years. These demos created unrealistic expectations but made customers feel good about purchases. Sound familiar?

The key question when evaluating AI demos: How closely does this demo environment resemble your actual operating conditions?If the answer is "not very," expect substantial gaps between promised and delivered value.

Investor FOMO: How Fear Creates Artificial Urgency

Perhaps nothing fuels hype like the fear of missing out. VCs watching competitors pour money into AI feel compelled to follow suit, creating self-reinforcing momentum.​

The numbers are staggering. Seed-stage valuations for generative AI startups have increased fourfold—a classic bubble indicator where investment decisions are driven by momentum rather than fundamentals. Silicon Valley now sees AI companies with $5 million annual revenue demanding $500 million valuations—100 times revenue multiples that would have seemed absurd even during the zero-interest-rate environment.​

Behind this betting-style logic is collective FOMO. Samir Dholakia at Bessemer Venture Partners explains: "AI is a technology that can add a zero to everything".That's not analysis—that's hope dressed up as an investment thesis.​

The structural dynamics amplify irrationality. Special Purpose Vehicles (SPVs)—temporary financial shells allowing retail investors to pool money for one-off deals—have proliferated as investors scramble for shares in hot companies like OpenAI and Anthropic. Many are legitimate, but some feature sky-high fees, opaque structures, and complicated middleman layers. In the frenzy to claim pieces of AI's trillion-dollar promise, less sophisticated investors may be buying into schemes.​

Early-stage companies now see valuations of $400 million to $1.2 billion per employee. That's not sustainable economics—that's speculation hoping someone else will pay more later. When valuations disconnect entirely from revenue, cash flow, or any traditional metric, you're not investing—you're gambling.​

Practical BS Detection: Your Skeptic's Toolkit

Enough diagnosis. How do you actually evaluate AI claims without succumbing to hype or dismissing genuine innovation?

1. Demand Specifics, Not Generalities

Ask for concrete metrics. "Improves productivity" means nothing. "Reduced average task completion time from 45 minutes to 30 minutes in a controlled study of 50 users" means something. If vendors can't provide specifics, they're selling vapor.

2. Request Customer References in Similar Contexts

Not cherry-picked success stories—actual customers operating in environments resembling yours. Talk to them directly. Ask about implementation challenges, ongoing costs, and whether the solution delivered promised value.If vendors resist connecting you with customers, that's your answer.

3. Evaluate the Business Model, Not Just the Technology

How does the company make money? Is revenue growing? Are customers renewing or churning? A sophisticated technology with no viable business model is a research project, not an investment. Companies spending $5 for every $1 in revenue need a credible path to profitability, not just exciting demos.

4. Check for Circular Relationships

Watch for vendor financing arrangements where companies invest in each other, creating artificial demand. Nvidia investing $100 billion in OpenAI while OpenAI purchases Nvidia chips creates an "increasingly complex web of transactions" that looks suspiciously like the circular financing of the late 1990s. When vendors and clients mutually reinforce valuations without generating external value, question the fundamentals.​

5. Test in Your Environment Before Committing

Run pilot projects before enterprise deployments. Use your actual messy data, your siloed systems, your unclear documentation. If performance degrades significantly from demo to reality, negotiate accordingly or walk away.

6. Watch Adoption Patterns, Not Announcements

Consumer adoption can race ahead of enterprise value. ChatGPT reached 100 million users faster than any prior app, but MIT found enterprise productivity gains elusive at scale. Stanford's AI Index shows business adoption rose from 55% in 2023 to 78% in 2024—but companies remain tentative, citing privacy, reliability, compliance, security, and financial risk.​

The percentage of companies abandoning most AI pilot projects soared from 17% in early 2024 to 42% by year-end. That's not progress—that's mass disillusionment. When abandonment rates triple in a year, the gap between promise and reality is vast.​

7. Follow the Money's Second Moves

VCs made initial bets. Watch what happens next. Are they doubling down in follow-on rounds, or quietly writing down investments? Early-stage funding may be driven by FOMO, but Series B and beyond require demonstrating actual traction. Companies struggling to raise subsequent rounds despite flashy launches are signaling problems.

8. Separate Hype From Capability

AI genuinely excels at pattern recognition, content generation, data processing, and automation of routine tasks. It struggles with novel problem-solving, causal reasoning, contextual understanding, and anything requiring genuine creativity or judgment. If vendors claim capabilities in the latter category, demand extraordinary proof.

The Eventual Reality: What Survives the Hype

History provides a useful perspective. The internet bubble burst spectacularly, but Amazon and Google emerged stronger. The technology was real; the timing and valuations were wrong. AI likely follows the same playbook.​

Serious analysts note that AI stock rallies have been driven largely by earnings growth rather than P/E expansion—the latter being a hallmark of bubbles. For most major players, valuations align with actual and expected earnings growth. The bubble concerns are strongest in private markets, where startups with small teams and unproven models secure multi-billion-dollar valuations.​

When the hype inevitably cools, three types of companies will survive:

1. Infrastructure Players With Real Revenue– Companies providing the computational backbone, data infrastructure, and development tools that AI requires. These are selling shovels during a gold rush, which historically proves more profitable than mining.

2. Domain-Specific Applications With Proven ROI– Not generalized AI assistants, but focused tools solving specific problems in healthcare, finance, legal research, and other verticals where value is measurable and customers are willing to pay.

3. Efficiency Multipliers That Augment Rather Than Replace– Tools that make human workers more productive rather than attempting wholesale replacement. MIT research consistently shows that augmentation delivers returns while replacement struggles with edge cases.​

What won't survive? Companies built purely on hype, those burning capital without paths to profitability, and vendors whose demos can't translate to real-world deployments.

Closing Thoughts: Navigating the Series

We've covered substantial ground across these seven essays, examining AI from multiple angles:

Essay 1 explored how current AI systems are sophisticated pattern-matchers, not intelligent entities—a critical distinction for understanding both capabilities and limitations.

Essay 2 revealed how AI detectors are unreliable security theater, creating more problems than they solve while discriminating against non-native English speakers.

Essay 3 demonstrated that prompt engineering is less wizardry and more trial-and-error communication with literal-minded systems prone to spectacular failure.

Essay 4 showed that AI's employment impact is real but nuanced—neither the apocalypse nor the utopia promised by extremes, requiring adaptation rather than panic.

Essay 5 exposed how corporate AI ethics policies are largely performance art, with accountability gaps and regulatory vacuums allowing harmful deployments.

Essay 6 detailed how AI content farms are flooding the internet with synthetic mediocrity, threatening both search quality and the creator economy.

And now, Essay 7 has equipped you with tools for spotting hype and evaluating AI claims critically.

The through-line? AI is simultaneously transformative and oversold. The technology delivers genuine value in specific contexts while falling catastrophically short in others. Success requires developing sophisticated judgment about where AI excels and where it flounders.

This demands two things:

Corporate Responsibility– Companies developing and deploying AI must move beyond ethics theater to actual accountability. That means rigorous testing, transparent limitations, clear liability frameworks, and genuine oversight. The Air Canada chatbot case established that you can't deploy AI and disown its decisions. More such precedents will follow. Organizations treating AI as "move fast and break things" technology will face legal, financial, and reputational consequences.

Personal Responsibility– Consumers, workers, and citizens must develop AI literacy. That means understanding how these systems actually work, recognizing their limitations, verifying AI-generated information, and seeking out genuine human expertise. In an internet drowning in algorithmic content, the ability to identify authentic human knowledge becomes increasingly valuable.

The hype cycle will correct.

Inflated valuations will deflate. Marginal players will fail. Overpromised capabilities will be exposed. But the underlying technology is real and will continue advancing. The companies and individuals who thrive will be those who see AI clearly—neither dismissing its potential nor accepting its hype uncritically.

We're not witnessing AI's end, but its maturation. The miracle tonic phase is ending. The useful medicine phase is beginning. The question isn't whether AI will transform industries—it already is. The question is whether we'll manage that transformation wisely, responsibly, and with eyes wide open to both possibilities and pitfalls.

That's the challenge ahead. The tools exist. The hype is clearing. Now comes the harder work of building something sustainable, accountable, and genuinely valuable. And unlike the promises of snake oil salesmen, that transformation will be boring, incremental, and real.