By John Mecke, Managing Director, DevelopmentCorporate LLC
The headlines suggest generative AI has achieved mainstream adoption. ChatGPT reached 100 million users faster than any consumer technology in history. Microsoft, Google, and Adobe have embedded AI capabilities across their product suites. Venture capital firms poured over $25 billion into AI startups in 2024 alone.
But for enterprise software founders, investors, and M&A advisors, the critical question isn’t whether AI has captured mindshare—it’s whether it has crossed Geoffrey Moore’s infamous chasm between early adopters and the pragmatist majority that drives sustainable revenue and enterprise value.
After thirty years in enterprise software M&A and having lived through the CASE tools revolution of the 1990s, I see striking parallels between that cycle and today’s AI transformation. Understanding where we are in Moore’s adoption curve isn’t academic—it fundamentally affects fundraising strategies, product roadmaps, competitive positioning, and ultimately, exit valuations for early-stage SaaS companies.
Understanding Moore’s Chasm Framework
Geoffrey Moore’s “Crossing the Chasm” introduced a crucial refinement to the technology adoption lifecycle. While earlier models showed a smooth bell curve from innovators to laggards, Moore identified a treacherous gap—the chasm—between early adopters (technology enthusiasts and visionaries) and the early majority (pragmatists).
The chasm exists because these groups have fundamentally different buying criteria:
Early adopters seek competitive advantage through innovation. They’ll tolerate incomplete products, integration challenges, and implementation risk if the potential strategic benefit justifies it. They’re buying a vision of what’s possible.
Pragmatists demand proven solutions with measurable ROI, references from similar companies, and what Moore calls the “whole product”—not just core technology but the complete ecosystem of support, integration, and risk mitigation necessary for production deployment.
The chasm is where promising technologies die. Companies that successfully cross it do so by focusing on a specific “beachhead” segment, delivering the whole product for that use case, and using those reference customers to expand into adjacent markets. Those that fail remain stuck selling to early adopters while burning through capital waiting for mainstream adoption that never arrives.
The Split Reality: Consumer vs. Enterprise GenAI Adoption
When examining whether generative AI has crossed the chasm, we must recognize that we’re actually looking at two distinct markets with radically different adoption curves.
What the AI Leaders Are Saying
Before diving into the adoption dynamics, it’s worth hearing from the CEOs building the infrastructure that’s supposed to cross Moore’s chasm. Their rhetoric suggests they believe mainstream adoption has not only arrived—it’s accelerating exponentially.
Sam Altman frames OpenAI’s trajectory in civilization-scale terms. Speaking to reporters in August 2025, he projected: “If you project our growth forward, pretty soon, like billions of people a day will be talking to ChatGPT. ChatGPT will say more words a day than all humans say, at some point, if we stay on our growth rate.” The numbers back his confidence: ChatGPT reached 800 million weekly active users by October 2025, with 3 million paying business customers and explosive API adoption. When your CEO is contemplating whether to acquire Chrome and discussing trillions in infrastructure spending, you’re not talking about early adopter territory anymore.
Dario Amodei at Anthropic presents equally staggering growth figures that suggest enterprise adoption has arrived. “Anthropic’s revenue every year has grown 10x,” he stated in July 2025. “We went from zero to $100 million in 2023. We went from $100 million to a billion in 2024. And this year, in this first half of the year, we’ve gone from $1 billion to I think, as of speaking today, it’s well above $4 [billion], it might be $4.5 [billion].” The company tripled its eight and nine-figure enterprise deals in 2025 versus 2024, with business customers spending 5x more on average. Claude is now powering development at Fortune 500 companies and major coding platforms like Cursor and GitLab—exactly the kind of broad enterprise deployment that signals chasm crossing.
Aravind Srinivas at Perplexity focuses on the transformation of information access itself. “In a world where you can easily create fake content with AI, accurate answers and trustworthy sources become even more essential,” he said, positioning Perplexity as infrastructure for the AI age. His company processed 780 million queries in May 2025 alone, with over 20% month-over-month growth. Perplexity’s $100 million in annualized revenue and partnerships with platforms like Shopify signal that AI-powered search has moved beyond technology enthusiasts to mainstream commercial deployment.
These leaders speak the language of market dominance and ubiquity, not early adoption. Their metrics—billions of users, hundreds of millions in monthly revenue, exponential enterprise growth—suggest the chasm has been crossed. But a closer examination reveals a more nuanced reality.
Consumer GenAI: Across the Chasm
Consumer-facing generative AI applications have undeniably reached early majority adoption, validating much of what the AI leaders claim. The evidence is compelling:
ChatGPT’s explosive growth demonstrated consumer demand for conversational AI. Within two months of launch, it became the fastest-growing application in internet history. Today, GenAI capabilities are embedded in tools millions use daily—Google’s AI Overviews in search, Microsoft Copilot in Office 365, Adobe Firefly in Creative Cloud, and Grammarly’s writing assistance.
These aren’t experimental features relegated to beta programs. They’re production capabilities that pragmatist consumers rely on for everyday tasks. The whole product exists: the technology works reliably enough for general use, it’s integrated into familiar workflows, and switching costs are minimal.
Enterprise GenAI: Still in the Chasm
The enterprise market tells a starkly different story, despite the impressive revenue figures cited by AI leaders. While Anthropic’s 10x annual revenue growth and OpenAI’s 3 million business customers sound like pragmatist adoption, the underlying dynamics reveal classic early adopter patterns.
Consider what those numbers actually represent: Most “enterprise adoption” is concentrated in technology companies and early-stage startups—the innovators and early adopters of Moore’s framework. When Anthropic cites customers like Cursor, GitLab, and Replit, or when OpenAI talks about API growth, they’re largely describing adoption by companies that are themselves building AI products. This is horizontal adoption within the technology sector, not vertical penetration into mainstream industries.
The pragmatist majority—the Fortune 500 manufacturers, healthcare systems, financial services firms, and retail chains that drive sustainable enterprise software markets—are still overwhelmingly in pilot mode. Ask a Chief Information Officer at a traditional enterprise about their GenAI deployment, and you’ll hear about proof-of-concepts, limited rollouts to specific departments, and ongoing evaluation of ROI.
The symptoms of chasm dwelling remain unmistakable:
Pilot purgatory dominates. Enterprises are running dozens of GenAI experiments but struggling to move them to production. A 2024 Gartner survey found that while 55% of enterprises have AI pilots underway, only 15% have deployed AI applications at scale. This is classic early adopter behavior—testing the vision without committing to the reality.
ROI remains speculative. CFOs approve AI spending based on competitive pressure and FOMO, not proven returns. Ask enterprise buyers for quantified productivity gains or cost savings from their GenAI deployments, and you’ll typically get vague responses about “strategic positioning” rather than hard metrics. Pragmatists don’t buy based on strategic positioning—they buy based on calculable value.
The whole product doesn’t exist. Moore’s framework emphasizes that pragmatists won’t adopt until they can buy a complete solution. For enterprise GenAI, critical components remain missing: robust governance frameworks, compliance certification for regulated industries, reliable accuracy guarantees, predictable cost models, and integration with legacy systems. Companies are assembling these pieces themselves, which is how innovators and early adopters operate, not pragmatists.
Reference selling isn’t working yet. Pragmatist buyers rely heavily on references from similar companies facing similar challenges. But in AI, reference customers are either technology companies (not relatable to mainstream enterprises) or are sharing carefully curated success stories about pilots rather than production deployments. The peer pressure that drives pragmatist adoption hasn’t materialized.
Why the disconnect between CEO optimism and enterprise reality? The revenue growth that Altman, Amodei, and Srinivas cite is real—but it’s being driven by a relatively narrow base of technology-forward companies and developers, not by the broad pragmatist majority that Moore describes. When 10% of potential customers increase their spending by 10x, it generates impressive revenue growth but doesn’t indicate mainstream adoption. This is the classic pattern of early adopter expansion before chasm crossing: deepening engagement with innovators while the pragmatist majority watches from the sidelines.
Why This Matters for Early-Stage SaaS Companies
If you’re a founder building on or competing against GenAI, accurately diagnosing where we are in the adoption curve is critical for strategic positioning and fundraising.
Fundraising Implications
Venture investors are flooding capital into AI-enabled SaaS companies, but they’re making very different bets depending on where they believe the market sits. As I’ve written about in “The AI Funding Apocalypse,” AI companies at Series A stage now raise at valuations 40% higher than comparable traditional SaaS companies:
Early-stage investors betting on vision are funding companies building for a post-chasm world that may be 18-36 months away. These companies need sufficient runway to reach a market that doesn’t fully exist yet. Your burn rate assumptions and milestone planning must account for a longer path to pragmatist adoption than your pitch deck suggests.
Growth-stage investors demanding traction want evidence that you’ve found pragmatist buyers willing to sign enterprise contracts, not just early adopter logos doing pilots. If you’re raising a Series A or B claiming AI-driven growth, investors will scrutinize whether your revenue comes from repeatable sales to mainstream buyers or one-off deals with technology enthusiasts.
The valuation multiple you can command depends heavily on whether you’re selling to early adopters (valued on vision and potential) or pragmatists (valued on proven, scalable revenue). Mischaracterizing where you are in this journey leads to either overvaluation risk or leaving money on the table.
Competitive Positioning Strategy
Understanding the chasm dynamics should inform your go-to-market approach:
If targeting early adopters: Emphasize innovation, flexibility, and strategic advantage. Expect smaller initial contracts, higher touch sales cycles, and customers who want to shape your product. Your competitive advantage comes from being first and most capable, not from being safe and proven.
If targeting pragmatists: You need the whole product strategy Moore describes. This might mean partnerships for enterprise features you can’t build yourself, vertical market focus to deliver complete solutions for specific industries, or customer success programs that de-risk deployment. Your competitive advantage comes from references, repeatability, and reduced implementation risk.
Many early-stage SaaS companies make a fatal mistake: they market to pragmatists (emphasizing ROI, proven results, enterprise features) while only having a product that appeals to early adopters. This creates a credibility gap that kills deals.
Product Development Priorities
The chasm framework should influence your product roadmap dramatically:
Pre-chasm products can prioritize capability over reliability. Early adopters will tolerate hallucinations, inconsistent performance, and manual workarounds if the underlying technology delivers breakthrough results. Your engineering resources should focus on core AI capabilities.
Post-chasm products must prioritize the boring but essential features pragmatists demand: audit trails, role-based access controls, SOC 2 compliance, cost predictability, monitoring and observability, graceful degradation, and enterprise integration. These aren’t differentiating features—they’re table stakes.
I consistently advise seed-stage clients to be brutally honest about which features serve their current market (early adopters) versus which features they’re building for a market that doesn’t exist yet (pragmatists). Building for pragmatists too early burns cash; building for early adopters too long leaves you stranded when competitors cross the chasm first. Understanding how AI is fundamentally changing SaaS economics is essential for making these prioritization decisions.
The CASE Tools Parallel: Lessons from the 1990s
Having spent the 1990s at KnowledgeWare and Sterling Software during the CASE (Computer-Aided Software Engineering) tools era, I see uncomfortable parallels between that cycle and today’s AI transformation.
CASE tools promised to revolutionize software development through automated code generation, visual modeling, and AI-powered assistance. Sound familiar? The vision was compelling, and early adopters at technology-forward companies achieved impressive results. Venture capital flooded the space. Valuations soared. Then reality hit.
The whole product never materialized. CASE tools required complete retooling of development processes, integration with multiple legacy systems, and organizational change management that most enterprises couldn’t execute. The technology worked in controlled environments but failed when deployed broadly.
ROI proved elusive. While vendors showcased productivity improvements at early adopter sites, these gains didn’t replicate at mainstream companies. The difference wasn’t the technology—it was organizational readiness, process maturity, and the expertise required to extract value.
The market consolidated brutally. As pragmatist adoption stalled, the CASE tools market underwent massive consolidation. Sterling Software acquired over a dozen companies in the space, often for cents on the venture dollar. Only companies with substantial services revenue or niche vertical dominance survived independently.
The AI parallel should be obvious: transformative potential, early enthusiasm, genuine technical achievement, but a yawning gap between demonstration and production deployment at scale.
The difference—and it’s crucial—is that AI has consumer adoption creating sustained investment and capability development that CASE tools never achieved. The technology continues advancing rapidly, potentially shortening the chasm crossing timeframe. But for enterprise SaaS companies building on GenAI, the CASE tools experience offers cautionary lessons about the perils of overestimating adoption timelines.
Agentic AI: Not Even Close to the Chasm
While we debate whether GenAI has crossed the chasm, agentic AI—autonomous systems that take actions rather than just generating content—remains in the early adopter phase, possibly even still in innovator territory.
Recent announcements from Anthropic (computer use), OpenAI (Operator), and Salesforce (Agentforce) have generated enormous buzz. Venture firms are racing to fund agent startups. But let’s be clear about where we actually are:
Demonstrations dominate over deployments. Most “agentic AI” examples you see are carefully controlled demos, not production systems handling real customer workflows. The gap between demo and deployment for autonomous systems is measured in years, not quarters.
Trust and control issues remain unsolved. Enterprises struggle with giving AI systems autonomy to take consequential actions—financial transactions, customer communications, data modifications—without human review. The governance frameworks, liability models, and rollback mechanisms required for pragmatist comfort don’t exist yet.
Reliability isn’t remotely close. Agents fail unpredictably in ways that content generation doesn’t. A hallucinated sentence in a draft document is annoying; an agent that processes refunds incorrectly or misconfigures production systems is catastrophic. The error rates pragmatists will tolerate for autonomous actions are orders of magnitude lower than what current systems deliver.
For early-stage SaaS founders, the implication is clear: if you’re building agentic capabilities, you’re building for innovators and early early adopters. The pragmatist market probably won’t materialize until 2026-2027 at the earliest, possibly later. Your burn rate, runway, and fundraising strategy must reflect this reality.
Strategic Implications for M&A and Valuation
As an M&A advisor focused on early-stage SaaS companies, I’m watching how the chasm dynamics affect acquisition strategy and valuations. Understanding SaaS valuation fundamentals is critical in this environment:
Acquirer motivations differ by adoption stage. Strategic acquirers buying pre-chasm AI companies are making talent and technology acquisitions, not revenue acquisitions. Valuation multiples reflect this—you’re selling potential, not proven business models. Post-chasm acquisitions command revenue multiples because the market has validated the business model.
Integration risk weighs heavily. Enterprises acquiring AI startups face enormous integration challenges if the target’s technology requires the whole product components to function in production. This integration risk compresses valuations or kills deals entirely. Companies that have solved integration challenges command premiums because they’re immediately deployable.
Market timing creates urgency. The sweet spot for exits is often just before or just after chasm crossing. Pre-chasm, strategic acquirers pay premiums to avoid being left behind. Post-chasm, financial buyers can model revenue with confidence. Mid-chasm is valuation purgatory—too expensive to be a talent acquisition, too uncertain to model as a business.
Competitive dynamics shift dramatically. Pre-chasm markets reward technical capability and innovation velocity. Post-chasm markets reward execution, sales efficiency, and customer success. This affects both what acquirers value and which companies can command premium valuations.
Conclusion: Navigating the Chasm as Strategy, Not Just Metaphor
Geoffrey Moore’s chasm framework isn’t just a useful metaphor—it’s a diagnostic tool that should inform every strategic decision for early-stage SaaS companies building on or competing with generative AI.
The answer to “Has GenAI crossed the chasm?” is yes for consumers, no for enterprises, and not even close for agentic systems. This split reality creates both opportunity and peril:
Opportunity exists for companies that accurately diagnose where their specific market sits and build the whole product for pragmatist adoption ahead of competitors. First-mover advantage in crossing the chasm in a particular vertical or use case creates defensible market position.
Peril awaits companies that mistake early adopter enthusiasm for mainstream adoption, burn through capital building for a market that’s 24 months further away than their models assume, or get caught mid-chasm when funding markets tighten.
The companies that will dominate enterprise AI aren’t necessarily those with the best models or most innovative technology. They’ll be the companies that solve the boring, hard problems of governance, compliance, integration, and reliability that pragmatist buyers demand—and do so while conserving enough capital to reach the other side of the chasm.
For founders, investors, and advisors navigating this transformation, the question isn’t whether AI will eventually cross the chasm—it’s whether your company has the runway, strategy, and whole product to survive the crossing.
About the Author: John Mecke is Managing Director of DevelopmentCorporate LLC, an M&A advisory and strategic consulting firm specializing in early-stage SaaS companies. With over 30 years of enterprise software experience including executive roles at KnowledgeWare and Sterling Software, he advises pre-seed and seed-stage CEOs on competitive intelligence, market positioning, and exit strategy. Contact him at john@developmentcorporate.com or visit developmentcorporate.com.
