In 2025, I expect progress in training foundation models to slow down as we hit scaling limits and inference costs continue to rise. Instead, I hope for an explosion of innovation on top of AI, such as the rapidly developing agents stack. I hope we will see innovation in how we combine AI with tools and existing systems to deliver exciting new capabilities and create new product categories. Perhaps most of all, I am excited to see how people change in response to this new world.
We have achieved AGI. Now what? Let’s start with — and hopefully end — the longstanding debate around artificial general intelligence (AGI). I know this is controversial, but I think we have achieved AGI, at least definitionally: Our AI is now general. I will leave the longer debate about sentience and superintelligence to the philosophers and instead focus on the key innovation: generality.
The artificial intelligence or machine learning of previous decades was intelligent but highly specialized. It could often surpass human ability on a narrowly defined task (such as image recognition or content recommendation). Models today, and perhaps more importantly the systems around them, are capable of accomplishing a very wide range of tasks often as well as, and in some cases better, than humans. It is this generality that will allow engineers, scientists, and artists to use these models to innovate in ways that the model developers never imagined. It is also this generality, combined with market forces, that will make 2025 so exciting.
Becoming AI-native: The generality of these models and their natural language interfaces mean that everyone can use and explore AI. And we are! We are learning to explain our situations to machines, give context and guidance, and expect personalized answers and solutions. At RunLLM, where I’m a co-founder, we’re building high-quality technical support agents. We find that users increasingly use our agents not just to solve problems but to personalize solutions to their specific tasks. We’ve also found — to our surprise — that users share much more with an AI than they would share with another person.
Meanwhile, at UC Berkeley, I am impressed by students who use AI to re-explain my lecture or study from an AI-generated practice exam. They have found ways to use AI to help personalize and improve their learning experiences. In 2025, maybe we will begin to prefer AIs over humans when we need help or are trying to learn.
Across all these use cases, we’re clearly getting better at working around the limitations of large language models and using AI in ways I would not have imagined 12 months ago.
Return on AI: The focus in 2025 will turn to showing real value from past investments. Investors and enterprises will expect startups and enterprise AI teams to transition from exploring to solving real problems — reducing cost, generating revenue, improving customer experience, and so on. This is bad news for academics who need to raise research funds (DM me if you have any leftover funds from fiscal year 2024) but great news for everyone else, who will ride the wave of new AI-powered features.
There will be a race to find innovative ways to incorporate AI into every aspect of a product and business. In many cases, we will see hastily executed chatbots and auto-summarization features — the first step on the AI journey. I hope these will be quickly replaced by contextual agents that adapt to users’ needs and learn from their interactions. The pandemic paved the way for remote (digital) assistants and exposed a virtually accessible workplace with the tools needed for tomorrow’s agents. These agents likely will specialize in filling roles once held by people or maybe filling new roles created by other agents. Perhaps we will know that AI has delivered on its promise when everyone manages their own team of custom agents.
Chat is only the beginning: My hope for 2025 is that we move beyond chatting and discover how to use AI to do great things! I hope we will see AI agents that work in the background, invisibly helping us with our daily tasks. They will surface the right context as we make decisions and help us learn as the world changes. Through context and tools, they will let us know what we are missing and catch the balls we drop. We will chat less and our AI powered agents will accomplish more on our behalf. I look forward to the day when I can confidently step away from a keyboard and focus on the human interactions that matter.
Joseph Gonzalez is a professor at UC Berkeley, a co-founder of RunLLM, and an advisor to Genmo and Letta.