It’s now 2026 and AI has become a daily part of business conversations. Everyone in the C-suite is contemplating whether to hop on the “hype train.”
The bubble surrounding the industry seems poised to pop at some point as the market becomes saturated with companies producing “AI slop” in terms of plagiarized art and writing and inflating the value of GPUs and other computer hardware equipment, along with demanding more capacity for data center power.
Here, experts share their predictions for AI in 2026:
Hallucination impacts continue to expand leading to regulatory direction setting: AI induced hallucinations impact businesses and consumers alike. Fabricated content, made up sources, misstating or misrepresenting facts, outright errors, and AI system produced illusory derivations from data are all a menace to business credibility, user decision making, and can create millions and billions of dollars of risk. AI Hallucination risk will become a regulatory concern across multiple industries and will produce new oversight.—Jim King, CEO and co-founder, IndagoAI
AI governance becomes the new DevOps for the enterprise: AI adoption will shift from a modeling challenge to an operational one. Enterprises will standardize how AI is discovered, approved, secured, and monitored—making AI governance a core discipline much like DevOps.—Yuval Fernbach, JFrog’s VP and CTO of MLOps
AI will fundamentally make the idea of the “next big AI platform” obsolete: AI is decomposing the concept of monolithic application data platforms. Agentic AI is enabling persona-driven applications that leverage hyperscaler infrastructures like Microsoft, AWS and Databricks for data storage purposes, but this shift makes the idea of a “next big AI platform” obsolete. Access to clean, usable and contextualized data will become the critical enabler, as organizations are empowered to build solutions from specific components versus locking into one vendor.—John Harrington, co-founder and chief product officer, HighByte
Costs begin to pile up: After years of inflated expectations and unsustainable spending, the AI industry is trapped in a bubble where companies reflexively attempt to deploy LLMs at every problem, driving up costs with minimal to no return. Businesses that break free from this spending cycle are the ones that understand the need to ground LLM responses in factual data and learn from prior mistakes. We believe the best way to do this will be with highly accurate embedding models and rerankers for reliable data retrieval.—Frank Liu, staff product manager, MongoDB
AI will reduce the limitations of cybersecurity’s reliance on customer data: In 2026, one of the most important shifts in cybersecurity won’t be the new attack techniques, but how AI will enable teams to build and test defenses without access to real customer data—a long-standing limitation that AI is finally helping to overcome. Security teams have always worked at a disadvantage because the data they need to train and test systems is the data they can’t access. What’s changing is that newer AI models can make sense of unfamiliar enterprise data without having been trained on it directly. That’s going to matter far more than chasing the next unpromising headline about AGI.—Mike Rinehart, VP of AI at Securiti AI
Grid infrastructure investment becomes critical path: In 2026, the energy industry will confront a stark reality: the primary bottleneck is no longer the capacity to generate clean energy, but the ability to transmit it. Data transmission capacity constraints will become the single most limiting factor for large-scale energy deployment. The power grid in major economic blocs is already severely strained by the simultaneous, exponential growth of data centers and the intermittent nature of new renewable technologies. This tension necessitates a ton of investment; estimates project that the European Union alone will require a nearly trillion-dollar investment in its grid infrastructure to handle these compounded demands. On top of that, building more wind and solar farms will hit a ceiling unless we rapidly modernize and expand the high-voltage backbone. The race is now on to build the digital and physical highways necessary to move them from where they are generated to where the hyperscalers and consumers need them most.—Jason Eichenholz, CEO/founder, Relativity Networks
ChatGPT will launch ads and a new ad-free paid tier: We expect OpenAI to launch ads in 1H 2026 along with a higher-priced, ad-free premium tier. This will disrupt existing marketing budgets and create two new channels we need to master. ChatGPT Ads will become a major arena for performance testing and optimization. “VIP SEO” will emerge inside the ad-free product, giving us access to a high-earning, highly engaged segment of ChatGPT power users who cannot be reached through ads.—Alex Halliday, co-founder and CEO, AirOps
Enterprises will face a GPU scaling reality check: Enterprise AI deployments will hit a harsh reality check in 2026 as the gap between AI ambitions and infrastructure capabilities forces a major recalibration of corporate AI strategies. Most enterprises are planning AI deployments based on traditional cloud scaling assumptions, but AI workloads operate fundamentally differently. While CPU-based applications can scale dynamically, AI systems require GPU resources that can take 20-30 minutes to provision and must often be statically allocated upfront. The crunch will come when enterprises face unexpected user adoption spikes and discover that cloud providers haven't truly delivered a "cloud model" for AI workloads. When 10,000 employees suddenly want to use an enterprise AI tool, the infrastructure simply won't be there. This will lead to high-profile AI service outages at major corporations. Companies will be forced to either drastically scale back deployment ambitions or invest heavily in sophisticated GPU resource management, which requires deep technical expertise most organizations lack.—Gil Spencer, chief technology officer, WitnessAI