Forbes Releases 2026 AI 50 List
Forbes dropped its annual AI 50 ranking of the most promising AI companies. Among the standouts: Gamma, the AI presentation tool that investors once called "the worst idea" they'd ever heard, is now valued at $2.1B with 100 million users. Reflection AI, a Brooklyn-based open-source lab, raised $2.1B at an $8B valuation to challenge Chinese dominance in open models. The list signals which companies have moved beyond hype into real enterprise traction.
Read on Forbes →Gartner: AI Platform Spending Up 63% to $64B in 2026
Gartner's latest forecast projects worldwide spending on AI models and platforms will hit $64 billion in 2026 — up 63.4% from $39 billion in 2025. Generative AI models are surging 117%, while domain-specific models are growing 210%. The insight buried in the numbers: the winners will be vendors that help enterprises manage where and how AI is used, not just which models to deploy. Governance is becoming the differentiator.
Read on Gartner →Gartner: By 2030, Most CDPs Will Be Composable
MarTech's deep dive on "the missing layer behind customer data ROI" surfaces a major prediction: by 2030, the large majority of new enterprise CDP deployments will be embedded in or composable with data platforms rather than bought as standalone products. Adobe's expanding Databricks integration and Scott Brinker's observation that "application platforms are becoming infrastructure platforms" point to the same conclusion. The standalone CDP as we know it is being absorbed into the data layer.
Read on MarTech →SAP Went From 40 Agents to 200+ in One Year
CIO's Isaac Sacolick, synthesizing nine vendor conferences, notes that SAP expanded from 40 AI agents in 2025 to over 200 in 2026. Meanwhile, Deloitte found that 36% of IT leaders expect at least 10% of jobs to be fully automated within a year. The speed of agentic AI deployment is accelerating faster than most org charts can handle. The question isn't whether agents will run enterprise workflows — it's who's building the governance frameworks to manage them.
Read on Anicca →Martech Consolidation Requires Fixing Workflows, Not Just Cutting Vendors
CMSWire argues that rationalizing the AI stack forces marketing teams to make concrete operating trade-offs. Standalone AI tools offer specialized depth but add switching costs, governance burden, and adoption risk. The real work isn't vendor selection — it's workflow redesign. Teams that focus only on cutting tools without fixing the processes those tools serve end up with leaner stacks that still don't work.
Read on CMSWire →💡 My Take
Read this one: The MarTech piece on CDPs going composable. Gartner saying most CDPs will be "embedded in or composable with data platforms" by 2030 is the clearest signal yet that the martech stack is collapsing into the data layer. If you're evaluating CDP vendors, you're already asking the wrong question — ask about your data architecture instead.