What to Expect Next in Artificial Intelligence
The future of artificial intelligence is already shaping up with some exciting trends set to define 2025. From smarter AI agents to advances in memory and model sizes, these changes will affect how we interact with technology in everyday life and business. Let’s explore eight key AI trends expected to have a big impact next year and beyond.
Understanding AI Agents: The Cornerstone of 2025
AI agents are becoming the face of what many think of as artificial intelligence. But what exactly are they? Think of AI agents as smart programs that don’t just give you answers; they can plan, reason, take multi-step actions, and use tools to get real work done. These systems break down complex tasks into smaller steps and interact with databases or software to reach their goals.
The popularity of AI agents is clear — videos about them often see a big spike in interest. People want to know how these systems really work and how they might change the way we use AI daily.
Right now, AI agents handle simple plans fairly well, like answering straightforward questions or completing basic tasks. But when problems get complicated with many moving parts, current models struggle. They sometimes lose track or make decisions that don’t add up logically.
Here’s a quick look at what AI agents can do well today versus what they find challenging:
Strengths:
- Handle simple, straightforward tasks
- Break down basic problems into steps
- Use tools and databases for quick info
Challenges:
- Keeping consistent logic in multi-step plans
- Managing complex scenarios with many variables
- Making sound decisions when problems get complicated
In 2025, better models will be needed to improve how AI agents reason and plan. This ties closely to developments in how AI thinks during inference (more on that shortly), aiming to make these agents not just reactive helpers but reliable problem solvers.
Quick definition: AI agents are intelligent systems designed to plan, reason, and act autonomously, often by breaking complex tasks into smaller, manageable actions.
Smarter AI Through Inference Time Compute
AI models go through a training phase where they learn from huge amounts of data. Afterward, they perform inference, which means working with real-time inputs (like your questions) to produce answers.
The new trend shaking up inference is AI that spends a variable amount of thinking time before responding. Instead of rushing to give an answer, the AI "thinks" longer if the query is tough or requires deeper reasoning. For example, a simple question might be answered in a second or two, but a complex problem could take minutes.
Why does this matter? Traditionally, improving AI reasoning meant retraining the whole model with better data — a costly and time-consuming process. Now, with inference time computing, the AI's reasoning can be improved independently during the response stage. This means smarter, more thoughtful answers without full retraining.
One method behind this improvement is called “chain of thought” training. It teaches AI how to follow logical steps during inference, helping it reason like a person might when solving puzzles.
This trend is expected to lead to smarter AI agents that plan better and make clearer decisions. It adds a new layer of intelligence, separating improvements made during training from those made as the AI “thinks” on the spot.
For readers curious about the technical details, the AI model guide offers more insights into how these learning and inference processes work.
The Size Spectrum of AI Models: Very Large and Very Small
The models behind AI come in different sizes, mainly measured by parameters—think of them as the number of “neurons” or parts in a neural network.
In 2024, the biggest large language models (LLMs) have about 1 to 2 trillion parameters. For 2025, there’s talk of models growing to 50 trillion parameters or more. These enormous models can understand and generate highly complex content, helping power advanced AI agents and applications.
Here’s a bold fact: Models could jump from a few trillion to tens of trillions of parameters in just one year.
At the same time, a surprising trend is the rise of very small models. These models have just a few billion parameters but can still perform well on specific tasks. What's cool about these smaller models is they don’t require large data centers with stacks of GPUs. Instead, they can run easily on a personal laptop or even a smartphone.
For example, the IBM Granite 3 model has 2 billion parameters and runs smoothly on a standard laptop without taxing the device. In 2025, expect many more models tuned like this for specific applications where efficiency and speed matter more than raw size.
Here’s a quick comparison:
Very Large Models (Trillions of Parameters):
- Handle broad and complex tasks
- Require big computing power and lots of data
- Power general-purpose AI agents
Very Small Models (Billions of Parameters):
- Specialized for specific tasks
- Run on personal devices like laptops or phones
- Cost-effective and energy-efficient
We’re heading into a future where having both very large and very small models working side by side will become the norm.
Enterprise AI Use Cases Evolving in 2025
In 2024, according to a Harris Poll, companies used AI mainly in these areas:
- Improving customer experience
- IT operations and automation
- Virtual assistants
- Cybersecurity
These use cases form the baseline of how businesses interact with AI today.
Looking toward 2025, expect these applications to become more advanced and smarter:
- Customer service bots won’t just route tickets but will solve complex problems directly, making customer interactions smoother and less frustrating.
- IT operations tools will shift to optimize entire networks automatically, spotting problems before they happen.
- Security tools will adapt instantly to new threats, sensing and responding in real-time without waiting for updates.
This shift is from reactive to intelligent AI systems that anticipate challenges and help fix them proactively.
If customer service AI sounds interesting, you can register for Virtual Agents Day to learn more about how AI agents will upgrade customer interactions.
Expanding AI’s Memory: Moving Toward Near Infinite Context
Early AI language models could only remember about 2000 tokens (think words or characters) at a time. That limited how much context the AI had when answering questions.
Fast forward to today, and models can handle hundreds of thousands to even millions of tokens in their context windows. This means they can recall and use much more information from past conversations or documents when responding.
The future points to near infinite memory in AI chatbots. Imagine an AI customer service bot that remembers every conversation it has had with you, offering a truly personalized experience without repeating questions or losing context.
There are benefits and concerns with this level of AI memory:
Pros:
- Consistent, personalized interactions
- Faster issue resolution without repeating history
- Improved relationship-building with users
Cons:
- Privacy concerns about storing personal information long-term
- Security risks if data is not handled carefully
Readers are encouraged to think about what it would mean to talk to an AI that remembers everything you’ve ever said. How comfortable would you be? What benefits or drawbacks do you see?
Human-in-the-Loop Augmentation: Challenges and Opportunities
The concept of humans working alongside AI to improve decisions is called human-in-the-loop augmentation. It sounds perfect in theory, but there are challenges.
A notable study showed a chatbot outperforming doctors in diagnosing medical conditions from case reports. While the chatbot scored higher than any individual doctor, doctors who used the chatbot scored lower than the chatbot alone.
This result highlights a key issue: combining human expertise with AI is not always straightforward. Even experts struggle with crafting effective prompts or knowing how to get the best out of AI systems.
To build better human + AI teams, we need tools that:
- Integrate AI seamlessly into a professional’s work
- Don’t require users to be AI experts themselves
- Help experts craft better prompts or understand AI reasoning
- Improve trust and clarity in AI answers
In 2025, expect progress in creating these user-friendly AI interfaces, making expert augmentation truly smarter than either humans or AI alone.
Your Input Counts: Trend Number Eight Is Up to You
Instead of guessing alone, last year’s AI trends video invited viewers to share what they thought would be important for 2024. Hundreds responded with valuable ideas.
This year, the eighth AI trend is left open for the community. What do you think will shape AI in 2025? Share your thoughts and predictions because community ideas drive awareness and innovation in this fast-changing field.
Feel free to leave your comments or join the discussion on the AI Trends for 2025 video to help shape the conversation.
Artificial intelligence is rapidly advancing, with 2025 looking like a milestone year. From smarter, reasoned AI agents to models balancing size and efficiency, memory that approaches infinite recall, and better ways for humans to work alongside AI, the future holds many possibilities. These trends will influence the tools we use in business and everyday life, making AI more useful, responsive, and personal.
Stay informed and ahead by keeping an eye on these shifts and joining discussions to share your thoughts on where AI should head next.
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