The importance of humans: AI expectations for 2025 and key NeurIPS learnings

It's been a month since the excitement of NeurIPS 2024, but the insights and conversations from this landmark AI conference continue to resonate with us as we look to the top AI trends we expect to see in 2025. 

Looking past the recent DeepSeek announcements and into the future of generative AI in 2025, there is a common theme emerging that aligns with a key message heard at NeurIPS last month: there is an undeniable importance of human intelligence in shaping the future of AI. 

The importance of humans is not just in providing new data labels or fine-tuning models. It is about recognizing that human preferences, nuanced evaluation, and a deep understanding of context are essential to building trustworthy, efficient AI systems. 

In this blog post, we’ll touch on the exciting trends we expect to see in the AI landscape in 2025 that build on these learnings. Then we’ll look back to the learnings from NeurIPS last month and look at how the conference highlighted the crucial role of human expertise, from preference modeling and diverse benchmarking to the rise of agentic evaluation and the pursuit of architectural innovation. 

Shaping the future of Generative AI in 2025

As the Labelbox team looks ahead to how the AI landscape will evolve in 2025, we see a handful of critical trends that we’ll be following—and supporting with our focus on high-quality data, complex multimodal reasoning, and more—throughout the year:

• Human alignment horizon: The pursuit of advanced AI and even AGI will hinge on agile alignment, a continuous cycle of integrating specific human knowledge and reasoning into models through targeted post-training. We saw the power of using reinforcement learning (RL) with the DeepSeek approach to R1 and expect the value of advanced post-training with high-quality data to continue to rise. 
• Domain domination: Expect a fierce battle for domain domination as AI labs race to build specialized models that excel in complex reasoning tasks across a variety of fields like coding, medicine, legal, and finance. This will fuel demand for high-quality, domain-specific data and post-training expertise with advanced degrees and/or years of work experience.
• Agentic alignment: Agentic alignment will take center stage. Building AI agents capable of complex reasoning and autonomous decision-making will require intensive post-training by human experts who can impart their domain-specific knowledge and workflows.
• Supervised synthetic data: Synthetic data will continue to be a popular idea, but human oversight will remain crucial. Experts will play a key role in curating, cleaning, and refining synthetic datasets to ensure quality and mitigate issues like model collapse.

Human preferences & evaluation are at the core of AI

Supporting these 2025 themes, the NeurIPS's message last month was clear: the next frontier of AI innovation lies at the intersection of human insight, robust evaluation frameworks, architectural diversity, and efficient deployments. Whether you’re a researcher exploring new training optimizations or an enterprise looking to scale AI responsibly, the lessons from the conference point toward a more adaptive, inclusive, and human-aligned future.

NeurIPS highlighted how the integration of human preferences, nuanced evaluation methodologies, diverse benchmarking, and scalable architectures can unlock the next generation of trustworthy, efficient, and context-aware AI models.

A recurring theme was the importance of human preferences, alignment, and evaluation. The next big breakthroughs in AI as well as its continued adoption will depend on nuanced, but critical, adjustments to models through post-training and the expansion of complex reasoning capabilities and preferences fueled by human-centric data. 

Here are some of the noteworthy takeaways from the show that supported this theme of human preferences and evaluation and align with our predictions for 2025:

Diverse & representative feedback

The “PRISM Alignment Dataset” (Datasets and Benchmarks Best Paper at NeurIPS) advanced the conversation on evaluation by factoring in user demographics, cultural backgrounds, and other contextual factors. This revealed how performance metrics can vary widely across populations and domains, challenging the notion of a one-size-fits-all evaluation. PRISM exemplifies the critical importance of representative, multicultural feedback, ensuring that AI models align not just with a narrow set of users, but with the broader, diverse communities they serve.

Nuanced preference integration

Talks like “Enhancing Preference-Based Linear Bandits via Human Response Time” took preference modeling further by combining response times with choice patterns. This helped distinguish strongly held preferences from weaker ones, leading to more accurate and context-sensitive feedback loops. Rather than treating human opinions as a single static metric, these techniques allow for richer, more human-centric AI tuning.

Informed human input

One of the most compelling insights arose from sessions on “Human Expertise in Algorithmic Prediction,” which demonstrated that not all human annotations carry equal weight. By selectively soliciting expert judgments only where they meaningfully improve accuracy for certain predefined “subsets” of data, researchers showed it’s possible to enhance model quality without excessive human overhead. In some cases, just 10–20% of instances required human review—an approach that underscores the value of targeted human guidance in refining AI outputs.

Robust, contamination-free evaluations

Extending beyond PRISM, NeurIPS highlighted the idea that robust, contamination-free evaluations are the bedrock of reliable progress. Alongside PRISM’s multicultural lens, “EUREKA: Evaluating and Understanding Large Foundation Models” offered a blueprint for in-depth, multimodal evaluations spanning language, vision, and beyond. 

Specialized benchmarks targeting long-context reasoning (e.g., BABILong), multi-agent RL, code generation, self-driving, and hardware performance reflected a desire to dissect model capabilities under a variety of realistic conditions. These efforts emphasize that as AI diversifies in application, our benchmarks must diversify as well—ensuring meaningful measurements of performance, stability, and representativeness. Error bars, demographic considerations, and longitudinal tracking of preferences over time will all be crucial components of these next-generation evaluation frameworks, and human preference will lie at the core of them.

Prepare for the unexpected in 2025

With the first month of 2025 almost behind us, it’s clear that the AI world remains as exciting and innovative as ever—full of surprises, and with many more to come. 2025 promises to be a year of exciting advancements and paradigm shifts in the world of AI, and as NeurIPS made abundantly clear, human intelligence will be at the forefront of it all.

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