I’ve been thinking a lot about what comes next in AI. As impressive as LLMs are, we’re inevitably heading toward the “trough of disillusionment” that follows every hype cycle. While the industry races to enhance LLMs with new connection protocols and agent frameworks, I’ve been exploring a fundamentally different approach that might offer a promising direction when the dust settles: Energy-Based Models (EBMs).

The Current AI Landscape: Enhancement vs. Reimagination

If you follow AI developments, you’ve likely noticed the flurry of announcements around protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication. These are valuable efforts to address real limitations in today’s AI systems.

MCP aims to solve how AI systems connect to data sources, standardizing these connections so models can access more relevant information. A2A frameworks enable specialized AI agents to work together on complex tasks. Both represent important evolutionary steps for current AI approaches.

But what if, instead of just enhancing current approaches, we considered fundamentally different ones?

Energy-Based Models: A Different Approach to Machine Intelligence

Energy-Based Models, championed by researchers like Yann LeCun and others, offer a distinct alternative to today’s dominant AI paradigms. I’ve been working through some of the foundational literature, and the core concepts are fascinating.

At their heart, EBMs associate a scalar “energy” value to different configurations of variables. Lower energy means higher compatibility or correctness. The learning process shapes this energy landscape so that correct configurations have lower energy than incorrect ones.

This might sound abstract, so let’s break it down:

In a classification task:
- LLMs: "Given this input, what's the statistical likelihood of each possible class?"
- EBMs: "Which class creates the lowest energy (highest compatibility) with this input?"

It’s a subtle but profound difference in approach.

Key Characteristics That Make EBMs Intriguing

Several aspects of EBMs stand out as particularly promising:

1. Relationship-Centric Rather Than Sequential

LLMs fundamentally predict the next token in a sequence based on patterns they’ve observed. EBMs instead model relationships and compatibility between variables directly. This shift from “what comes next?” to “how well do these things fit together?” might better capture certain aspects of how we understand the world.

For example, when we evaluate a situation, we often consider multiple factors simultaneously rather than sequentially. EBMs naturally model these kinds of relationships.

2. Global Consistency

One challenge with LLMs is maintaining consistency across long outputs. EBMs approach this differently - the energy function evaluates entire configurations, naturally maintaining global consistency. This is especially valuable for tasks requiring structured outputs like image segmentation or sequence labeling.

3. Explicit Constraint Modeling

With EBMs, domain-specific constraints can be directly incorporated into the energy function. Rather than hoping a model implicitly learns constraints from data (as with LLMs), we can explicitly encode them.

For instance, if we know certain combinations are physically impossible, we can assign them high energy values, ensuring the model never selects them.

4. Multiple Valid Solutions

Real-world problems often have multiple valid solutions. EBMs represent this naturally through energy landscapes with multiple minima. This aligns well with how humans reason about problems that have more than one correct answer.

Implementation Approaches: Loss Functions Shape Understanding

What I find particularly interesting is how different loss functions shape how EBMs learn. Each approach creates different energy landscapes:

  • Hinge Loss: Creates a margin between correct and incorrect answers
  • Square-Square Loss: Pushes correct energies to zero while pushing incorrect energies above a margin
  • Negative Log-Likelihood Loss: Turns energies into probabilities

These aren’t just mathematical abstractions - they represent fundamentally different ways of shaping how a model understands relationships.

Why EBMs Might Matter After the LLM Hype Cycle

As we inevitably move past peak LLM hype, I believe approaches like EBMs might offer valuable directions for several reasons:

  1. They address fundamental limitations rather than symptoms: Instead of adding more data or parameters to existing approaches, EBMs rethink the foundation

  2. They align with certain aspects of human reasoning: The focus on relationships and compatibility rather than statistical patterns might capture important aspects of cognition

  3. They offer computational efficiency potential: While current implementations may not be optimized, the approach itself doesn’t necessarily require the massive resources of today’s LLMs

  4. They handle structured problems elegantly: For tasks requiring consistent, structured outputs, the energy-based approach provides natural solutions

The Path Forward: Exploration and Integration

I’m not suggesting EBMs will replace LLMs overnight - or even that they should. The most promising path forward likely involves exploring multiple approaches and potentially integrating their strengths.

What excites me is that while everyone races to build incremental improvements to existing paradigms, there’s this fundamentally different approach that deserves more attention. As the industry inevitably moves through the hype cycle, alternatives like EBMs might offer fresh directions.

What’s Next?

I’m continuing to explore EBMs and their potential applications. There’s still much to learn about efficient implementations, scaling to complex problems, and potential hybridization with other approaches.

What do you think? Are EBMs on your radar as a potential next direction for AI? Or do you see other approaches that might define the post-LLM landscape? I’d love to hear your thoughts.