Schneider Electric Acquires PTC for $22.6 Billion in Largest Industrial AI Software Deal
France's Schneider Electric confirmed this morning it will acquire US industrial software firm PTC for $22.6 billion, the company's largest-ever acquisition and one of the biggest enterprise software deals of 2026. The acquisition follows Schneider's $3.1 billion purchase of AI software provider Cognite in June, signaling an aggressive push to pair software intelligence with its energy management and industrial automation hardware. PTC brings a formidable portfolio: Creo for product design, Windchill for product lifecycle management, ThingWorx for industrial IoT, Codebeamer for application lifecycle management, and Onshape for cloud-native CAD. The strategic logic is clear — as factories, data centers, and infrastructure become software-defined, the companies that own both the physical layer and the intelligence layer will capture disproportionate value. Schneider is betting that industrial AI requires deep domain models trained on proprietary operational data, not generic foundation models. For enterprise software vendors, the deal is a reminder that vertical integration is back: the acquirers aren't other software companies, but the industrial giants whose operations generate the data that makes AI valuable.
Read on Bloomberg →Gartner: 70% of Enterprises Will Abandon Vendor-Built Agentic AI by 2028, Trapped by Costs
Gartner issued a stark warning last week: by 2028, 70% of enterprises will abandon agentic AI systems built through vendor "forward-deployed engineering" (FDE), trapped by soaring costs and unable to evolve the systems on their own. The FDE model — where vendors send implementation engineers to build custom agent solutions on-site — has become popular as enterprises scramble to deploy agentic AI. But Gartner argues this approach creates dangerous dependency: enterprises get working systems they don't understand, can't modify, and can't maintain without ongoing vendor involvement. The costs compound over time as agents need updating for new use cases, changing data schemas, and model upgrades. The finding echoes a broader pattern in enterprise AI: the gap between pilot success and scaled production remains stubbornly wide. According to First Page Sage's analysis of adoption statistics, 62% of enterprises remain in the experimentation phase, with only 13% reaching full-scale deployment. Gartner's recommendation: enterprises should insist on knowledge transfer, documentation, and internal capability building as non-negotiable parts of any agentic AI implementation — even if vendors resist sharing their methods.
Read on Gartner →Former Anthropic Researcher to Testify at NYC AI Hearing: "People Building AI Believe It Could Kill Us All"
Jacob Coxon, the former Anthropic researcher who resigned last month warning that "people building AI earnestly believe that it could kill us all by the end of the decade," will testify Monday at a New York City Council hearing on AI safeguards. NYC Council Speaker Julie Menin requested his appearance alongside representatives from Anthropic, OpenAI, Google, and Meta, as well as former DeepMind researcher Alex Turner and ex-OpenAI's Daniel Kokotajlo. The hearing comes as AI governance debates intensify following recent incidents with autonomous agents. Meanwhile, Columbia Law School scholars Amelia Miazad, Barak Orbach, and Menesh Patel published an analysis arguing that public statements by Amodei, Altman, and Hassabis endorsing coordinated AI slowdowns could expose labs to Sherman Act Section 1 antitrust liability. Their argument: "a bare agreement among competitors to slow innovation does not become lawful merely because its stated purpose is to reduce risks to society." The collision between AI safety concerns and antitrust law creates a genuine bind — the labs may be legally prohibited from the very coordination that safety researchers say is necessary.
Read on Yahoo News →Gartner: 40% of Enterprise Apps Will Have Task-Specific Agents by Year-End, Up from 5% in 2025
Gartner's 2026 Hype Cycle for AI Governance Technologies forecasts that task-specific AI agents will feature in roughly 40% of enterprise applications by the end of 2026, up from under 5% in 2025 — an 8x increase in a single year. But the same report warns that 40% of agentic AI projects will be canceled by end of 2027, suggesting that rapid adoption is outpacing enterprise capability to govern and maintain these systems. The tension is visible in adoption statistics: First Page Sage's analysis of over 15,000 businesses finds that enterprise adoption leads at 25%, but 62% of those enterprises remain in experimentation. Mid-market companies show slightly higher partial deployment rates (18% vs 15% for enterprise), likely because they face fewer approval layers while having more budget than SMBs. The data suggests a coming shakeout: enterprises are adding agents to applications faster than they're developing the governance frameworks, operational processes, and internal expertise to run them at scale. The next 18 months will separate companies that treat agents as features from companies that treat agents as operating systems.
Read on TrueFoundry →ARC-AGI-3 Benchmark Scores Jump from 7% to 56% in 30 Days — Without New Base Models
Top scores on the ARC-AGI-3 Kaggle competition jumped from roughly 7% to about 56% in the last 30 days — a leap that came almost entirely from reasoning harness and scaffolding advances, not from new base model releases. The competition, which carries an $850,000 prize pool and closes November 2, 2026, restricts participants to Kaggle-permitted compute, meaning solutions must use small open-weight local models. The current leader is Tufa Labs at 55.89%, followed by Yi-Chia Chen at 48.59%. The r/MachineLearning discussion emphasizes what this means for capability forecasting: raw model benchmarks increasingly understate what's achievable through sophisticated prompting, tool use, and multi-step reasoning frameworks wrapped around smaller models. For enterprise AI, the implication is that capability is becoming more about engineering than about model access — a shift that favors companies with strong implementation teams over those merely licensing the latest frontier models. The ARC-AGI benchmark itself tests abstract reasoning and novel problem-solving, capabilities once thought to require ever-larger models but now apparently achievable through architectural innovation around existing weights.
Read on r/MachineLearning →💡 My Take
Today's stories paint a picture of enterprise AI at an inflection point — money is flowing, but the operational foundations remain shaky. Schneider Electric's $22.6 billion bet on PTC isn't a bet on software; it's a bet that industrial AI requires owning both the data source and the intelligence layer. Generic foundation models can't optimize a factory floor or a power grid without deep domain knowledge embedded in operational systems. The vertical integration thesis is back, and software-only vendors should be concerned. Gartner's warnings about vendor-built agentic AI and the 40% project cancellation forecast suggest the industry is about to learn an expensive lesson about the difference between deploying AI and operating AI. Forward-deployed engineering is essentially paying vendors to build technical debt — systems that work until they don't, maintained by people who don't work for you. The companies that will succeed are building internal capability alongside external deployments. The ARC-AGI-3 results are perhaps the most underappreciated signal: capability is increasingly about engineering, not about model access. A well-scaffolded small model can now outperform a poorly-deployed frontier model. That's good news for enterprises worried about model vendor lock-in, and bad news for enterprises hoping to buy their way to AI maturity. Meanwhile, the collision between AI safety concerns and antitrust law — labs may be legally prohibited from coordinating slowdowns — adds another layer of uncertainty to an already uncertain landscape. Today feels like the moment before the shakeout.