The AI hype cycle is in a more complicated phase than the simple "peak of inflated expectations" framework captures. Categories within AI are at very different points — foundation models with substantive deployments, applied AI products with mixed adoption results, AI-adjacent positioning that mostly is not actually AI. Tech communications work has to operate across these gradients without falling into the patterns that produce backlash.
The brands handling this well are doing specific things. The brands handling it poorly are paying what could fairly be called a hype tax: lost credibility, journalist skepticism, customer wariness, and regulatory attention that compound as costs over time.
What the hype tax actually looks like
Several visible costs to AI overclaiming.
Journalist relationships have shifted. Major tech reporters at outlets like Bloomberg, The Information, Wall Street Journal, TechCrunch, Wired, and others have become substantially more skeptical of AI capability claims than they were 18 months ago. The same pitch that earned coverage in early 2024 increasingly produces no response or skeptical follow-up in 2026.
Enterprise buyer skepticism has hardened. Gartner and other analyst firms have documented growing buyer caution around AI capability claims, with longer evaluation cycles, more rigorous proof-of-concept requirements, and lower acceptance of marketing-led claims. The shift is particularly visible in enterprise software where buyers have been burned by underdelivering AI features.
Regulatory attention has increased. FTC has made explicit that AI-washing is in scope for enforcement. SEC scrutiny of AI-related disclosures by public companies has continued. The regulatory cost of overclaiming has risen.
Investor narratives have tightened. Public market reception of AI claims has grown more discriminating. Companies with substantive AI capabilities are still rewarded; companies positioning themselves as AI-related without substance face skepticism that affects valuation.
What's separating credible tech communications from hype
Several patterns visible in technology communications that holds up.
Specificity beats abstraction. Claims that explain what the product actually does, with specific examples, technical depth where appropriate, and acknowledgment of limitations, build credibility. Abstract claims about "leveraging AI" or "powered by machine learning" without specifics produce diminishing returns.
Customer evidence beats vendor claims. Substantive customer case studies with specific outcomes, named customers (where the customer permits), and verifiable details outperform vendor-narrated capability claims. The investment in customer-facing case study work pays back.
Technical depth matters more, not less. As reporters and buyers have grown more sophisticated, communications materials that engage with technical substance — architecture, training data approaches, evaluation methodology, limitation analysis — earn credibility that surface-level marketing does not.
Founder and technical leadership communication has become important. CEO and CTO public engagement that demonstrates substantive understanding of the technology produces credibility that polished communications staff cannot replicate. The pattern favors companies with founders who can speak credibly about technical substance.
Acknowledgment of failure modes builds trust. Companies that explicitly discuss what their AI products do not do well, where users should be cautious, and where the technology has limitations build more durable trust than companies that present AI as universally capable.
What does not work
Approaches that have produced poor outcomes consistently.
"AI-first" or "AI-native" positioning without substance. The framing has been overused to the point that it produces skepticism rather than credibility. Companies that lead with AI positioning need substantive backing or face hype-tax consequences.
Generic "powered by" language. Claims that products are powered by, leverage, or enabled by AI, machine learning, or large language models without specifying what this actually means in practice produce diminishing returns.
Hyped capability demonstrations. Carefully-curated demos that show best-case behavior without representing typical performance create expectation-reality gaps that surface as disappointment in deployment.
Aggressive forward-looking claims. Statements about what AI products will do in the future, particularly when paired with current product communications, create credibility risk and SEC exposure for public companies.
Press tour heavy on access, light on substance. Press tours that offer journalists access to executives without substantive technical engagement increasingly produce coverage that is critical or non-existent.
What this means for communications strategy
A few practical implications.
The technical substantiation work is communications work. Communications teams in technology categories, particularly AI, need to operate with substantive understanding of what their products actually do and how they actually work. The communications work that holds up is informed by technical reality.
Customer story development is foundational. Investment in identifying, developing, and supporting customer story telling is the single highest-ROI communications activity in current technology categories. The cases where this investment has compounded over time are visible across the most successful technology brand communications.
Founder and senior technical leader development. Time invested in preparing founders and CTOs for substantive media engagement — including practice with hostile or skeptical questioning — produces communications outcomes that no amount of polished spokesperson preparation can replicate.
Analyst relations have grown in importance. Gartner, Forrester, and other industry analysts have substantial influence on enterprise buyer perception. Substantive analyst relations work — not just briefings, but ongoing engagement with substantive product input — pays back across multiple sales cycles.
Owned content that takes itself seriously. Technical blog content, white papers with real research, case studies with substantive depth — owned content that holds editorial standards comparable to trade publications produces both buyer engagement and AI tool retrieval value.
The category-specific dynamics
A few specific areas worth understanding.
Enterprise AI software. The hype-correction phase has been particularly visible. Buyers are evaluating more carefully; communications work needs to support longer evaluation cycles with deeper substantiation.
AI infrastructure and developer tools. This category has had more durable buyer interest, helped by clearer ROI demonstration. Communications work that engages with developer audiences directly produces results that consumer-channel-style communications does not.
Consumer AI products. Volatile demand, with category-defining successes (ChatGPT, Claude, others) coexisting with high-profile flops. Communications strategy in consumer AI requires careful expectation management.
AI applied in specific verticals. Healthcare, legal, finance, and other regulated verticals have produced both substantive applications and significant overclaiming. The hype-tax cost is highest in regulated verticals where overclaims trigger compounding regulatory issues.
The trajectory
The hype tax will not disappear. Tech communications work that accepts the more skeptical environment and adapts will produce results. Tech communications work that continues operating on 2023-era assumptions will increasingly underperform.
The category-defining success cases are not those that hyped most aggressively. They are those that built substantive product, communicated specifically about what the product does, and earned credibility that compounded across product cycles. This pattern was true before the AI cycle and will remain true after it.
