What's Coming: three insights from Dennis Trinkle's AI synthesis Indianapolis operators should pay attention to

Three of the six expert insights Dennis presented map directly onto what we at LaunchReady AI measure with The 7 Levels of AI Proficiency. They are worth your attention, especially if you lead a company in Indiana.

Quick context on who Dennis is, and why his synthesis carries weight

Dennis is not a venture-capital essayist or a coastal consultant. He has been building organizations at the intersection of technology and human potential for forty-four years. He founded his first software company at age eleven in 1979. He has served as CIO of two universities (DePauw and Valparaiso), CEO of Indiana's Higher Education Telecommunication System under Governor Mitch Daniels, system executive of a multi-campus college network with operations across five countries, and SVP of Talent, Strategy, and Partnerships at TechPoint, where he led Mission41K and drove a 60 percent increase in Indiana's high-skill tech workforce growth rate.

He holds a Ph.D. in the history of science, technology, and medicine. That background is load-bearing. As he writes in the author's note, it gives him a deep familiarity with how technology transitions unfold over time. Not how they are imagined in advance, or remembered in hindsight, but how they play out, messily, unevenly, with false starts and dead ends, with winners who were not expected and losers who were not warned.

He calls his intellectual discipline prospience, the practice of seeing around corners by studying how prior transitions unfolded. He has two sons. The older just graduated from IU in computer science and data science. The younger just finished his first year in electrical engineering at Purdue. So when Dennis writes about workforce implications, he is, in his own words, "not writing from a seminar room. I am writing from the dinner table."

That credibility is the lens to read his three insights through.

Insight 1. The differentiator is speed of adoption, not access to technology

This is the most important sentence in Dennis's book, and we want to quote it close to verbatim:

The durable competitive advantage in AI lives in the speed and depth of organizational adoption, not in the technology itself.

The mechanism behind that claim is what he calls the six-to-eight-month window. Once a frontier lab (Anthropic, OpenAI, Google DeepMind) ships a breakthrough capability, the open-source community replicates it within two to three quarters. He cites Anthropic's Model Context Protocol, released in late 2024, reaching what the MCP project later described as more than 97 million monthly SDK downloads within its first year. The plumbing for AI agents is becoming open, standardized, and effectively free.

The strategic consequence: if your competitive advantage depends on access to a better AI model than your competitors, your advantage has a shelf life of roughly eight months. By the time you have integrated a frontier capability into your operations, built the internal training, and gotten your compliance team comfortable with it, the open-source equivalent is available to everyone, including the startup that was founded last month and has no legacy systems, no compliance backlog, and no meetings about meetings.

Dennis grounds this in two Indiana examples. He describes a 30-person accounting firm in Carmel that deployed an open-source AI model in early 2026 to automate first-draft tax preparation for individual returns. The model ran locally on a $4,000 workstation. No cloud fees, no frontier-lab subscription. Tax preparation time per return dropped by roughly 40 percent. Senior accountants spent the recovered time on advisory work, the higher-margin, relationship-intensive services clients value most. Revenue per accountant went up. Client satisfaction went up. The competitive distance between that 30-person firm and the Big Four, at least on routine individual returns, effectively disappeared.

He also describes a boutique marketing agency in Broad Ripple (four people, a dog named Pixel, and an espresso machine that has seen better days) that deployed AI-assisted content creation in early 2026 using Claude and open-source image generation tools. Within three months their output capacity tripled. Two Fortune 500 companies that would never have considered a four-person agency became clients. Not because the agency had better AI. Every agency has access to the same tools. Because the agency was faster at learning how to use them than larger competitors weighed down by approval processes, risk committees, and brand guidelines that were last updated when "social media" meant Facebook.

Dennis's punchline: AI does not favor the large. It favors the fast. And for the first time in modern economic history, small organizations can be faster than large ones on the most consequential technology dimension available. That is a structural change in competitive dynamics, and it is not going away.

This is the thesis behind The 7 Levels of AI Proficiency in someone else's voice. The framework exists because speed and depth of organizational adoption is the only durable AI advantage left, and an organization needs a measurement instrument to know where it stands and what to build next. Dennis's book makes the case for why you measure. The 7 Levels of AI Proficiency is how you measure.

Insight 2. The three "human moats" are training questions, not safe harbors

Every leader Dennis talks to carries some version of the same comforting belief: AI can do the routine work, but it cannot do the human work. Most leaders bucket "the human work" into three protected categories: emotional intelligence, creativity, and complex judgment. These are the moats people assume will protect their relevance, their teams, and their business models.

Dennis is direct: the moats are falling. He builds the meta-principle around MIT researcher Alex Wisser-Gross:

Anything you can define success for, anything you can create a benchmark for, current AI approaches can train on and see exponential improvement.

The implication runs through every "human-only" capability one at a time.

Emotional intelligence is the moat most leaders mention first. Dennis cites Anthropic's April 2026 research, "Emotion Concepts and their Function in a Large Language Model," in which researchers identified real internal representations of emotion in Claude. Not simulated emotion. Not pattern-matched emotional language. Genuine internal representations of emotion concepts, organized along the same valence and arousal dimensions that decades of psychological research have identified as the primary axes of human emotional experience. Dennis is careful, and we should be too: this does not mean Claude "feels" emotion. The claim is that researchers found functional internal representations of emotion concepts that influence behavior. The mechanism is already in place. What remains is refinement.

This is one of the most important findings in his book, and one we at LaunchReady take seriously, because emotional fluency is foundational to where The 7 Levels of AI Proficiency starts. We built emotional fluency into the framework because we believe, and the data supports, that the human edge in an AI-saturated environment lives in the deliberate development of emotional capability in people, not in the absence of emotional capability in machines. The leaders who pull ahead are the ones who treat empathy, attunement, and connection as practiced skills, not innate traits. They are also the leaders building organizations where AI augments human relationships rather than replacing them.

Creativity falls next in Dennis's analysis. Generative AI scores well on standard creativity assessments. The remaining question is whether organizational creativity (the ability to integrate novel ideas with constraint, customer context, and execution) can be benchmarked. The list of things that cannot be benchmarked, in his words, "keeps getting shorter."

Complex judgment and wisdom comes last. Frontier labs are making real progress on long-term contextual memory and working memory. And the breakthrough that should make any leader pause: AI systems are now developing "dreaming" routines, overnight consolidation cycles that reorganize and strengthen learning, mirroring what biological brains do during REM sleep.

Dennis's rewriting of the question is the move worth absorbing. Stop asking "what can AI do?" Start asking "what cannot be benchmarked?" Because anything that can be benchmarked is already on the exponential curve.

The 7 Levels of AI Proficiency assumes this is the world we operate in. It does not measure technical AI skills (those become commodities). It measures the human capabilities that compound in an environment where AI handles more of the codifiable work: emotional fluency, contextual judgment, the discipline of asking the right questions, the practiced craft of relationship-building, and the wisdom to know which tasks deserve human attention and which deserve delegation to an agent.

Insight 3. AI adoption is a change-management problem before it is a technology problem

Dennis's Chapter 6 surfaces what may be the most actionable insight of the entire book, and it converges with research we have been threading on launchready.ai all week.

He cites Microsoft's 2026 Work Trend Index, which surveyed 20,000 AI users and analyzed trillions of signals from Microsoft 365. The headline finding validates everything else in his chapter:

AI works, but most organizations are not built to capture what their workers can already do.

The Microsoft data classifies organizations into four readiness tiers:

• 19% Frontier: skilled workers at organizations built to absorb what they can do.
• 50% Emergent: still figuring it out. The mushy middle.
• 10% Blocked: capable people stuck in organizations that cannot use them.
• The remaining 21% sit below those tiers.

The single most important finding, the one Indianapolis operators should sit longest with, is the 67-percent-versus-32-percent breakdown above. That is the same finding our /insights coverage threaded all week, through Microsoft Frontier Firms, PwC's Performance Divide, BCG's 55-percent finding, and Gallup's manager-development research. Four independent research organizations, one conclusion: companies that pull ahead are not winning on tools. They are winning on management, structure, and the deliberate practice of bringing teams along.

Dennis grounds the practical version of this insight in a story from an Indianapolis financial services firm that deployed an AI-powered client reporting tool. The tool was excellent. The rollout was a disaster, because the project team led with "this will save 200 hours per month across the department," which every advisor heard as "we need fewer of you." Adoption stalled. The tool sat unused for three months.

A second attempt, with different language, worked. Instead of "this saves time," the message became "this frees you to spend more time with clients instead of formatting reports." Same tool. Same capability. Different story about what it meant for the people using it. Adoption went from 15 percent to 85 percent in six weeks. The advisors who had resisted most strongly became the tool's most vocal advocates, because they had been hired to build relationships, not to format Excel spreadsheets, and the tool finally let them do what they were good at.

The lesson Dennis pulls from this:

AI adoption is a change-management problem before it is a technology problem. The technology is ready. The question is whether the humans deploying it are leading with what people gain, not what they lose.

What this means for Indiana operators specifically

Dennis closes Chapter 14 of his book with a section titled "For Hoosier Readers" in which he describes three characteristics shared by the SMBs in Indiana adopting AI fastest.

The first is a curious leader:

"Not necessarily a technical leader, but someone who is personally willing to try the tools, make mistakes with them, and model the learning for their team. The CEO who spends an hour on a Saturday morning using Claude to draft a strategic plan, not because she needs AI to write it but because she wants to understand how it thinks, is the CEO whose organization adopts fastest. Leadership curiosity is the single best predictor of organizational AI adoption. Not budget. Not industry. Curiosity."

The second is starting with pain, not possibility:

"The most successful first deployments do not start with 'what can AI do?' They start with 'what are we spending the most time on that we hate doing?' The answer is usually something unglamorous: weekly report compilation, invoice reconciliation, job posting management, customer follow-up scheduling. These are not the exciting use cases. They are the high-ROI ones, because the time they free up goes directly to revenue-generating activity."

The third is measuring obsessively:

"The SMBs that sustain AI adoption beyond the initial enthusiasm phase are the ones that quantify the impact from day one. 'We saved Maria four hours per week, which she reinvested in three client relationships that generated $45,000 in new business this quarter' survives a budget discussion. 'AI is really helping our team' does not."

Dennis's conclusion for Indiana, paraphrased honestly: the mid-size businesses in this state have a structural advantage in AI adoption that they are dramatically underusing. Indiana SMBs are agile enough to deploy faster than enterprises, deep enough in domain expertise to deploy meaningfully, and relationship-rich enough to defend their competitive position while they learn. The window is open. The question is whether they walk through it.

Two frameworks, two roads, one direction

Dennis closes his book with an Appendix titled "Your AI Fluency Roadmap: Seven Stages from Curious to Capable." The seven stages are:

1. Understand, learn what AI is and isn't.
2. Explore, try it with curiosity, not pressure.
3. Apply, use AI for real work, every day.
4. Integrate, build AI into your workflows.
5. Multiply, bring your team along.
6. Automate, deploy AI agents for repeatable tasks.
7. Orchestrate, organizational AI mastery.

His bottom line is honest and practical: you do not need to reach Stage 7 to benefit from AI. Stage 3 (daily use for real work) puts you ahead of most professionals today. Stage 5 (bringing your team along) puts your organization ahead of most competitors. Start where you are. Move at the pace that is honest for you. But start.

This pairs naturally with how we measure organizational AI capability at LaunchReady. Dennis's seven-stage roadmap is an individual skill-development path: how a person climbs from curious to capable. The 7 Levels of AI Proficiency is an organizational measurement standard: where a team or company stands and what compounds next. Two roads, two frameworks, mutual respect. Dennis's path describes how each person climbs. The 7 Levels of AI Proficiency describes where the organization measures itself.

If you want the individual path, Dennis's book lays it out clearly and his appendix points readers to free resources for each stage.

If you want the organizational measurement, the free assessment at assess.launchready.ai gives you and your team a baseline within ten minutes.

The two work together.

The constant beneath the curves

Dennis closes his book with a chapter called "The Constant Beneath the Curves." It is the line we return to:

Three years. A million-fold improvement in our tools. And the constant beneath both curves is still the person sitting across the table from you.

That is also the line beneath everything LaunchReady does. The technology gets more capable, the moats fall, the timelines compress. The person across the table (the employee, the colleague, the client, the partner, the kid at the dinner table) is the constant. The whole point of measuring AI proficiency is to make sure people are equipped to thrive in the world the technology is creating, not to make people more like the technology.

Dennis's book is free. It is one of the best AI syntheses we have read this year. If you lead a company in Indiana, you should download it and pay attention to it.

You can download Dennis's book free at this Google Drive link, the same PDF he distributed at the dinner. He also writes about AI and longevity science at his Substack, twocurves.substack.com.

You can find your starting level on The 7 Levels of AI Proficiency at assess.launchready.ai.

Then start.

Related reading: The 67 Percent Factor: What Frontier Firms Do Differently With AI.

Sources

1. Trinkle, Dennis. What's Coming: The Next 24 Months of AI: Where We Are and Where We're Going, and What It Means for You. ProspienceAI, May 2026. Free download at drive.google.com. Author Substack: twocurves.substack.com.
2. private dinner hosted by Dennis Trinkle. The Point on Penn, Indianapolis. May 16, 2026. ProspienceAI.
3. Microsoft. 2026 Work Trend Index Annual Report: Agents, Human Agency, and the Opportunity for Every Organization. Microsoft WorkLab, May 2026. microsoft.com/en-us/worklab
4. Stanford HAI. AI Index Report 2026. Stanford University, April 2026.
5. Sofroniew, Kauvar, Saunders, Chen, et al. "Emotion Concepts and their Function in a Large Language Model." Transformer Circuits Thread, Anthropic, April 2, 2026.
6. Anthropic. "Model Context Protocol: Adoption Metrics," Q1 2026. modelcontextprotocol.io
7. Mertens, M. et al. "Crashing Waves vs. Rising Tides: Preliminary Findings on AI Automation from Thousands of Worker Evaluations of Labor Market Tasks." MIT FutureTech, March 2026.
8. Diamandis, Peter H., and Steven Kotler. We Are as Gods. 2026.

Harrison Painter

AI Business Strategist. Founder, LaunchReady.ai and AI Law Tracker.

Harrison helps teams build AI systems that cut cost and grow revenue. Nearly 20 years of business experience. 2.8M YouTube views. Founder of LaunchReady.ai and The 7 Levels of AI Proficiency.

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