{"id":27177,"date":"2026-03-04T09:11:20","date_gmt":"2026-03-04T14:11:20","guid":{"rendered":"https:\/\/www.insightpartners.com\/?p=27177"},"modified":"2026-03-06T08:58:23","modified_gmt":"2026-03-06T13:58:23","slug":"generative-ai-ethan-mollick","status":"publish","type":"post","link":"https:\/\/www.insightpartners.com\/ideas\/generative-ai-ethan-mollick\/","title":{"rendered":"The jagged frontier of generative AI: A conversation with Ethan Mollick"},"content":{"rendered":"<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"><a href=\"https:\/\/www.insightpartners.com\/ideas\/generative-ai-stack\/\">Generative AI<\/a> is advancing fast, dazzling us with feats of reasoning or creative generation one moment and stumbling over simple tasks or logic the next. Wharton professor and AI researcher Ethan Mollick joins Lonne Jaffe to explore the &#8220;jagged frontier&#8221;: where threshold effects in the improvement of genAI capabilities are reshaping product strategy, talent composition, and organizational design.<\/p>\n<h2 class=\"text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold\">Key speakers<\/h2>\n<ul class=\"[li_&amp;]:mb-0 [li_&amp;]:mt-1 [li_&amp;]:gap-1 [&amp;:not(:last-child)_ul]:pb-1 [&amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\">\n<li class=\"whitespace-normal break-words pl-2\"><a href=\"https:\/\/www.insightpartners.com\/team\/lonne-jaffe\/\">Lonne Jaffe<\/a>: Managing Director, Insight Partners<\/li>\n<li class=\"whitespace-normal break-words pl-2\"><a href=\"https:\/\/www.linkedin.com\/in\/emollick\/\">Ethan Mollick<\/a>: Professor, The Wharton School; Author, <em>Co-Intelligence<\/em><\/li>\n<\/ul>\n<h2 class=\"text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold\">Key takeaways<\/h2>\n<ul class=\"[li_&amp;]:mb-0 [li_&amp;]:mt-1 [li_&amp;]:gap-1 [&amp;:not(:last-child)_ul]:pb-1 [&amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\">\n<li class=\"whitespace-normal break-words pl-2\">The &#8220;bitter lesson&#8221; of AI research is now arriving in business: general-purpose models trained on outputs are beginning to displace handcrafted, process-specific solutions.<\/li>\n<li class=\"whitespace-normal break-words pl-2\">Moats still exist \u2014 but only where process, not output, is what creates value.<\/li>\n<li class=\"whitespace-normal break-words pl-2\"><span data-teams=\"true\">Sometimes even a small improvement in model accuracy can multiply agentic task capacity by 2\u20133x, and many thresholds like this have quietly been crossed, unlocking new valuable capabilities.<\/span><\/li>\n<li class=\"whitespace-normal break-words pl-2\">Switching costs, one of software&#8217;s most durable moats, are being targeted at the Fortune 50 level, with large-scale legacy migration projects already underway.<\/li>\n<li class=\"whitespace-normal break-words pl-2\">The biggest constraint on AI adoption is not technology. It is leadership, organizational design, and the incentive structures that surround both.<\/li>\n<\/ul>\n<p>These insights came from our\u00a0<a href=\"https:\/\/scaleup.events\/\">ScaleUp:AI<\/a>\u00a0event in October 2025, an industry-leading global conference that features topics across technologies and industries. Watch the full session below:<\/p>\n<p><iframe loading=\"lazy\" title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/Yka2S-yTX70?si=LElJivPaU5cxVkBr\" width=\"800\" height=\"450\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<h2 class=\"text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold\">The bitter lesson comes for business<\/h2>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">The bitter lesson is a concept from machine learning research with a simple, uncomfortable premise: Your beautiful, handcrafted solution to a specific problem will eventually be destroyed by a general-purpose model trained at scale.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">Mollick illustrates it with chess. For decades, building a competitive chess engine meant hiring grandmasters and encoding every gambit and position. That approach powered <a href=\"https:\/\/www.ibm.com\/history\/deep-blue\">Deep Blue<\/a>. Then, in 2018, <a href=\"https:\/\/deepmind.google\/research\/alphazero-and-muzero\/\">Google&#8217;s AlphaZero learned chess from scratch<\/a> by playing itself \u2014 no human knowledge, just computation and reinforcement learning.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">The business analogy is arriving in real time. When Mollick and his colleagues pushed back on an early OpenAI Agent for lacking a visible to-do list, the response was clarifying: &#8220;The model is directly reinforcement learning trained on what good PowerPoint and Excel looks like. It just does it. We have no idea how or why.&#8221;<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">That&#8217;s the moment the bitter lesson enters the enterprise. If your product&#8217;s value is in the output \u2014 a report, a customer interaction, a generated document \u2014 a general-purpose model can be trained to match or exceed it. The process disappears. The moat evaporates.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">Where moats survive, Mollick argues, is where process is the point. Where the back-and-forth matters. Where human interaction, organizational judgment, and multi-stakeholder workflows are inseparable from the value delivered.<\/p>\n<blockquote class=\"ml-2 border-l-4 border-border-300\/10 pl-4 text-text-300\">\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\" style=\"padding-left: 40px;\"><em>&#8220;If the output is what matters in your business, you&#8217;re in trouble of being bitter lessoned. If the process matters \u2014 the conversations, the writing of the report more than the report itself \u2014 then there&#8217;s hope.&#8221;<\/em><br \/>\n<em>\u2014 Ethan Mollick<\/em><\/p>\n<\/blockquote>\n<h2 class=\"text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold\">The incumbent paradox<\/h2>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">When ChatGPT first emerged, conventional wisdom favored incumbents. They had distribution, customers, and existing data relationships. AI would arrive like electricity, a utility to be wired into existing products. Startups, the logic went, would struggle to compete.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">That narrative has fractured.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">The frontier labs didn&#8217;t just provide a commodity input. They kept shipping. <a href=\"https:\/\/www.insightpartners.com\/ideas\/ai-agents-disrupting-automation-and-reimagining-productivity\/\">Agents<\/a>, multimodal reasoning, deep research capabilities \u2014 capabilities that would have defined standalone products just 18 months ago are now baseline features. Meanwhile, many early AI-native products were simply not good enough, and the organizational adoption they relied on was slower than expected.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">That leaves incumbents in a paradox: They have the customers and the data, but also the inertia. In Mollick&#8217;s telling, many large organizations are still governed by AI ethics committees assembled in early 2023 \u2014 bodies that don&#8217;t fully understand the current technology and are working through backlogs of hundreds of use cases. Meanwhile, the models have moved on by several generations.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">The phase progression most enterprises are experiencing:<\/p>\n<ol class=\"[li_&amp;]:mb-0 [li_&amp;]:mt-1 [li_&amp;]:gap-1 [&amp;:not(:last-child)_ul]:pb-1 [&amp;:not(:last-child)_ol]:pb-1 list-decimal flex flex-col gap-1 pl-8 mb-3\">\n<li class=\"whitespace-normal break-words pl-2\"><strong>Phase Zero<\/strong> \u2014 AI ethics committee formed; rules established; real adoption stalls.<\/li>\n<li class=\"whitespace-normal break-words pl-2\"><strong>Phase One<\/strong> \u2014 Co-pilot deployed; &#8220;talk to your documents&#8221; solution built; works moderately well; not transformative.<\/li>\n<li class=\"whitespace-normal break-words pl-2\"><strong>Phase Two<\/strong> \u2014 Direct chatbot and tool access; build vs. buy decisions; vendor fatigue sets in.<\/li>\n<li class=\"whitespace-normal break-words pl-2\"><strong>Phase Three<\/strong> \u2014 The locus shifts from IT to organizational design. CEOs start personally engaging with AI strategy.<\/li>\n<\/ol>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">The savviest organizations are already in phase three, and they&#8217;re rethinking vendor relationships accordingly. As Mollick describes it, the framing has evolved from &#8220;we need a vendor to help us&#8221; to &#8220;we are renting capability while we learn.&#8221; The difference is agency.<\/p>\n<h2 class=\"text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold\">The switching cost reckoning<\/h2>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">One of the most <a href=\"https:\/\/www.insightpartners.com\/ideas\/building-a-moat-in-the-age-of-ai\/\">durable moats<\/a> in enterprise software has always been switching costs. Products that customers don&#8217;t love but can&#8217;t leave. Multi-year contracts. Data locked in proprietary formats.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">That era is facing its first serious structural challenge.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">At the Fortune 50 level, Mollick reports a consistent message from CTOs: &#8220;We&#8217;re not signing another five-year contract.&#8221; Some organizations are already running experiments where <a href=\"https:\/\/www.insightpartners.com\/ideas\/state-of-the-ai-agent-ecosystem-use-cases-and-learnings-for-technology-builders-and-buyers\/\">AI Agents<\/a> interact with legacy systems through their human-facing interfaces \u2014 using the UI as a bridge \u2014 to extract and recombine data without a formal migration. It&#8217;s slow, imperfect, and happening right now.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">The incentive is clear: hundreds of millions of dollars in potential savings, and accumulating frustration with lock-in that no longer delivers proportional value.<\/p>\n<blockquote class=\"ml-2 border-l-4 border-border-300\/10 pl-4 text-text-300\">\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\" style=\"padding-left: 40px;\"><em>&#8220;Organizations that are transparent about how they&#8217;re helping solve problems will survive. AI magic is going to wear thin very, very quickly.&#8221;<\/em><\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\" style=\"padding-left: 40px;\"><em>\u2014 Ethan Mollick<\/em><\/p>\n<\/blockquote>\n<h2 class=\"text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold\">Agents are real. Most people haven&#8217;t noticed.<\/h2>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">Perhaps the most significant signal from the conversation: Agents have crossed a threshold, and the market hasn&#8217;t priced it in.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">For years, the assumption was that <a href=\"https:\/\/www.insightpartners.com\/events\/ai-agents-briefing-series\/\">agentic AI<\/a> was limited by error accumulation; hallucination rates compound over long task chains, making sustained autonomous work unreliable. That assumption is now outdated. Larger models are self-correcting at rates that make extended agentic work viable.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"><a href=\"https:\/\/openai.com\/index\/gdpval\/\">OpenAI&#8217;s GDP evaluation paper<\/a> is instructive here. Experts with an average of 14 years of experience were given tasks requiring four to eight hours to complete. AI systems produced comparable outputs \u2014 rated nearly 50\/50 in blind expert evaluation \u2014 in five to ten minutes. In software specifically, the AI outperformed humans most of the time.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">The agentic layer is no longer something to plan for. It&#8217;s already embedded in the interfaces most knowledge workers use daily. The opportunity cost of waiting is no longer theoretical.<\/p>\n<h2 class=\"text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold\">The jagged frontier is shrinking<\/h2>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">The &#8220;jagged frontier&#8221; concept \u2014 the idea that AI has surprising areas of strength alongside surprising blind spots \u2014 has been a useful frame for explaining AI limitations to skeptical stakeholders. It is becoming less useful because the frontier is narrowing.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">Tasks that reliably illustrated AI&#8217;s gaps 12 months ago have been resolved. The remaining edge cases are increasingly technical, domain-specific, and debatable \u2014 not the clear demonstrations of incompetence that once made for compelling cautionary tales.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">Mollick is direct about what this means for software vendors in particular: the reassurance that comes from watching competitors fail to adopt AI is borrowed time. Organizational inertia is a delay, not a moat. The <a href=\"https:\/\/fortune.com\/2025\/08\/18\/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo\/\">95%-of-projects-fail statistic<\/a>, widely repeated across LinkedIn and boardrooms, originated from roughly 50 interviews conducted at a single conference. It is not a study.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">Clear-eyed assessment of the actual capability curve, not the six-week-old capability curve, is what separates organizations with a chance from those in comfortable decline.<\/p>\n<h2 class=\"text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold\">Leadership is the actual variable<\/h2>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">The technical questions are largely resolved. The model capabilities required to transform most industries are already deployed. What remains is the harder problem: getting organizations to act.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">Mollick&#8217;s prescription is pointed. Vague mandates to &#8220;be 10% more productive&#8221; don&#8217;t move people \u2014 and they shouldn&#8217;t. What moves organizations is a concrete, compelling vision of what the company looks like on the other side of this transition. That requires senior leaders to do real thinking, not just issue directives.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">The most effective organizational patterns Mollick has observed:<\/p>\n<ul class=\"[li_&amp;]:mb-0 [li_&amp;]:mt-1 [li_&amp;]:gap-1 [&amp;:not(:last-child)_ul]:pb-1 [&amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\">\n<li class=\"whitespace-normal break-words pl-2\"><strong>The lab model<\/strong>: Identify the 2% of employees already pushing boundaries with AI \u2014 they&#8217;re usually easy to find because they&#8217;ve been trying to get access for months \u2014 and formalize them as an internal capability center. Quick wins follow quickly.<\/li>\n<li class=\"whitespace-normal break-words pl-2\"><strong>Permission from the top<\/strong>: At organizations where the C-suite has personally bought in \u2014 naming JP Morgan&#8217;s Jamie Dimon and Mary Erdos as examples \u2014 the cultural permission structure changes, and experimentation accelerates.<\/li>\n<li class=\"whitespace-normal break-words pl-2\"><strong>Incentive redesign<\/strong>: Some organizations are running weekly prizes for the most impactful automation. Others require teams to spend two hours attempting a task with AI before posting a job requisition for that role. The specific mechanism matters less than the signal it sends.<\/li>\n<\/ul>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">The organizations that will win, Mollick argues, share one trait: Senior leadership willing to absorb risk. That has historically been the province of founders. It is increasingly the defining variable for established companies, too.<\/p>\n<h2 class=\"text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold\">Shaping what comes next<\/h2>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">The frame that Jaffe and Mollick keep returning to is a familiar one in disruption history: Tower Records, Blockbuster, London car services when Uber arrived. The question is never whether the shift is happening. It is whether your organization will be among those that bet on the curve early enough to shape what comes next.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">For founders, the message is to be more ambitious, not less. The capability gains that make agentic products economically viable are recent \u2014 and largely unpriced in market expectations. There is more room to build, and more risk in building too cautiously.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">For enterprises, the message is simpler and harder: the window for comfortable observation has closed. What remains is a choice between leading the transition or being led through it.<\/p>\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">Watch more sessions from\u00a0<a href=\"https:\/\/www.youtube.com\/watch?v=pbcYZxrv6lQ&amp;list=PLHbbevETrT8HRnGZzr4u5M5jiFcriETT7\">ScaleUp:AI<\/a>, and read more recaps <a href=\"https:\/\/www.insightpartners.com\/ideas\/search\/?tax=category&amp;term=scaleup-ai\">on the blog<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Generative AI is advancing fast, dazzling us with feats of reasoning or creative generation one moment and stumbling over simple tasks or logic the next. Wharton professor and AI researcher Ethan Mollick joins Lonne Jaffe to explore the &#8220;jagged frontier&#8221;: where threshold effects in the improvement of genAI capabilities are reshaping product strategy, talent composition, [&hellip;]<\/p>\n","protected":false},"author":36,"featured_media":27183,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[141,21,22,35],"tags":[],"class_list":["post-27177","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-scaleup-ai","category-article","category-thought-leadership","category-post-format-video","topic-industry-insights","domain-ai-ml"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v23.1 (Yoast SEO v23.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>The jagged frontier of generative AI: A conversation with Ethan Mollick | Insight Partners<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.insightpartners.com\/ideas\/generative-ai-ethan-mollick\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The jagged frontier of generative AI: A conversation with Ethan Mollick\" \/>\n<meta property=\"og:description\" content=\"Generative AI is advancing fast, dazzling us with feats of reasoning or creative generation one moment and stumbling over simple tasks or logic the next. Wharton professor and AI researcher Ethan Mollick joins Lonne Jaffe to explore the &#8220;jagged frontier&#8221;: where threshold effects in the improvement of genAI capabilities are reshaping product strategy, talent composition, [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.insightpartners.com\/ideas\/generative-ai-ethan-mollick\/\" \/>\n<meta property=\"og:site_name\" content=\"Insight Partners\" \/>\n<meta property=\"article:published_time\" content=\"2026-03-04T14:11:20+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-06T13:58:23+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2026\/03\/DSC07609-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"1707\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Cecilia Beard\" \/>\n<meta name=\"twitter:card\" 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