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Sean Horgan
Updates
AI in 2025
Sean Horgan
Drafting - this doc is a compilation of rough notes as I review 2024 and think about the world of AI in 2025.
Uncategorized references
Tools will increasingly be built for AI-powered agents
Note: I’m going to follow Simon Willison’s definition of agent which is an LLM with access to tools that work to achieve a goal.
For most of the last 20+ years, we’ve focused on building tools for the human end-users (e.g. websites, apps) or for other human engineers who are building tools for others (e.g. operating systems, APIs). With the emergence of more independent reasoning capabilities in LLMs, there will be a material shift toward building tools for LLMs to use that will require some real changes, e.g. new purchasing flows for autonomous agents.
References
The AI pricing wars will put some companies out of business
References
Mergers & consolidation will accelerate as companies realize their limits
This current phase of development in AI can be widely characterized as divergent. Many more people can now create their own AI-powered solutions because AI is lowering the barriers, leading to a proliferation of new companies with competing value propositions.
Examples of this in previous eras and domains
References
Winners will continue to be attacked on all fronts
In response, will NVIDIA increase its compete more directly public clouds like AWS?
Governments will spend real time and money trying to get a handle on AI, often with nationalistic aims
References
Talent & leadership will be real constraints on AI adoption
As technology barriers and costs continue to drop and pressure to put AI to work mounts from boardrooms and investors, many companies will struggle to find people to implement new programs.
From , it’s becoming trivial to run insanely sophisticated models on consumer-grade hardware. For example:
ollama run llama3.2
I have an M4 Macbook with just 16GB of RAM so I needed to focus on smaller models.
Data scaling laws favor companies that can amortize training and inference costs
Chinchilla scaling refers to the optimal increases in data versus parameter counts relative to increases in compute. Not enough data causes the model to generalize poorly, while too much data results in overtraining, which wastes compute resources. There are some instances where deviating from the optimal ratio makes sense: over-training models (e.g. GPT-4o and Llama) can decrease inference costs significantly and is preferrable for providers that have a larger user base to serve said model to.
A key point here is that the costs companies are willing to spend on training are directly tied to the value they are capturing on inference. For example, a company like OpenAI or Google can afford to invest heavily in training that reduces inference costs as they directly benefit from those lower serving costs AND they can offer cheaper model services.
Another key factor will be which occurs when the increasing efficiency of resource consumption leads to increased consumption:
References
The shift toward inference over training will shake up the current AI stacks
It appears that NVIDIA’s stack is optimized for training, not for inference. As AI focus shifts toward inference, is there an opportunity for new stacks to take hold? Where does that leave companies like Cerebras?
From : this means that LLM inference throughput is largely determined by how large a batch you can fit into high-bandwidth GPU memory. See in the NVIDIA docs for more details.
As inference demands increase, models will be subsumed further into compound systems. This will be driven by first principles, e.g. prompts and sampling methods are intrinsically connected to specific models, and commercial interests, e.g. Databricks and Snowflake aim to build unique value propositions around open source AI capabilities.
From , a Compound AI System as a system that tackles AI tasks using multiple interacting components, including multiple calls to models, retrievers, or external tools. In contrast, an AI Model is simply a statistical model, e.g., a Transformer that predicts the next token in text.
Practical Applications
AI in Bio
Industry Marketing
Benedict Evan’s “AI eats the world”
Some specific slides
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