NeurIPS (the Conference on Neural Information Processing Systems) is where the global AI research community goes to define the frontier. Founded in 1987 as a small, researcher-centric meeting in the American Rocky Mountains, it has grown into the largest annual gathering in AI, with 26,000 registrants in 2025, thousands of accepted papers, and a rapidly expanding industry expo.

For researchers and industry practitioners alike, NeurIPS is a bit like the Monkey King from the classic Chinese novel Journey to the West, on a quest for the “holy scrolls” of AI. It is where the scrolls are presented and retrieved; the newest ideas are brought, debated, and stress tested before they show up in products and P&Ls.

This year’s San Diego program stood out for the depth of enterprise engagement. The signal for business leaders is clear:

• Cutting-edge AI technologies are increasingly being applied in real business settings.
• The research community is actively working to connect technical progress with economic and societal outcomes.

We view NeurIPS as a window into the next three to five years of enterprise AI. Below are five themes that matter most for executives and boards.

From static models to “living” agents

AI’s long-term direction is shifting from static models to those that learn continuously from experience. In his keynote speech, Richard Sutton, corecipient of the 2024 ACM A.M. Turing Award (often called the “Nobel Prize of Computing”), made a provocative but simple claim: As AI has become a huge industry, it has lost its way by focusing too heavily on static models like large language models trained once on Internet-scale data. He argues that artificial general intelligence will come from AI systems that live in an environment, take extended actions, learn causal models of the world, and optimize a long-term reward.

Implication for leaders: More AI systems will begin to look like “digital employees” that improve as they work, rather than fixed models retrained on a schedule. That puts a premium on instrumentation, feedback loops, and governance, because whatever your AI systems see and measure, they will learn from.

Generative recommenders: when custom models meet strong data foundations

A standout industry session from Shopify, Nvidia, and Liquid AI showed how a generative recommender—a custom model trained on rich first-party commerce data—moved core metrics like click-through and conversion. Rather than relying on handcrafted features and segmentation rules, the model learned its own representations directly from behavior data. The teams also made deliberate architectural and hardware choices to meet strict latency targets and to support very high volumes of real-time traffic.

Implication for leaders: Increasingly, impact will come from how you adapt generative models to your own data and use cases, rather than relying only on generally available, off-the-shelf LLMs. These efforts are feasible but not do-it-yourself. They require the right partners across cloud, hardware, and modeling, and they only succeed with strong evaluation and A/B testing to prove real business lift.

“AI for doing AI”: agentic tools for data science and R&D

A major research theme was using AI to build and improve other AI systems. Google and others showcased agentic tools such as MLE-STAR, for machine learning engineering, and DS-STAR, for end-to-end data science workflows, as well as multi-agent “AI R&D” frameworks that can propose ideas, design experiments, and iterate with limited human intervention.

Implication for leaders: Analytics, machine learning, and even product development teams will increasingly work with agents, not just tools. In the near term, these “digital teammates” will accelerate routine tasks such as baseline modeling, data cleaning, and experiment setup, while humans focus on problem framing, sign-off, and risk. Leaders should already be asking where in their AI R&D and broader innovation value chains these capabilities can safely compress cycle times and increase the rate of high-quality experiments.

Model diversity and the rise of fit-for-purpose AI

NeurIPS made clear there will be no single “best” model. Instead, two trends are reinforcing each other:

• Richer, more efficient multimodal models. Work in the multimodal track focused on making models that handle text, images, video, and long contexts cheaper and faster to serve—for example, through smarter parallelism and compression of key value caches, so assistants can reason over documents, screenshots, and other media in real time without prohibitive latency or cost.
• Smaller, task-specific models for speed and edge deployment. In the generative recommender example, the teams intentionally chose a compact architecture and infrastructure stack to meet user experience and cost constraints, illustrating how “good enough, very fast” can beat “best but slow” in production.

Implication for leaders: Portfolio thinking now beats single-model bets. Enterprises will mix large, small, cloud, and edge models across workflows. That elevates evaluation as the new control plane, deciding when a lighter model is adequate, when to adopt a new foundation model, and how to prevent regressions. Bain’s collaboration with OpenAI on agentic evaluation, highlighted by Sam Altman at Dev Day, shows how multitier evaluation frameworks can make complex agent systems safe and auditable in production.

Safety, alignment, and explainability are moving into the mainstream

Beyond capability, a large share of NeurIPS work focused on robustness, alignment to human values, and explainability. This included interpretable models, new benchmarks for stress testing systems under adversarial or ambiguous conditions, and “guardrail” models that sit alongside generative systems to monitor behavior.

Implication for leaders: Risk and governance are being engineered into the stack itself. Regulators and boards will increasingly expect explainability, bias monitoring, and traceability as standard features. Organizations that invest early in structure, auditability, and human oversight will be better positioned to scale AI safely and confidently.

Seeing the horizon beyond today’s streetlights

A closing invited talk on “demystifying depth” offered a useful metaphor. One set of slides showed familiar “streetlights” of modern AI such as transformers, ResNets, and Adam. Another revealed the broader landscape of theory and methods these belong to, beyond the streetlights, many of which are still underexplored.

For business leaders, the parallel is clear. It is easy to over-rotate to the brightest buzz of the moment, whether a frontier LLM, the latest video or image generator, or a new agentic framework. NeurIPS is a reminder that there is no single, unifying theory of AI and no model that will rule them all. Different approaches illuminate different parts of the problem space, and progress comes from combining them thoughtfully rather than betting everything on one paradigm.

The more durable question is how quickly and effectively your organization can absorb new capabilities, evaluate them rigorously, and refit processes around them without losing sight of risk, values, and long-term strategy. We are still early in the AI revolution. The organizations that build strong data foundations, robust evaluation and governance, and a culture that treats AI as a living platform, not a one-off project, will define the next generation of competitive advantage.