2025 AI Trends: What’s coming next?

First week back, and with a long to-do list, I finally had time to put together my thoughts on what’s next in AI for 2025 and beyond. Some of these trends might materialize this year, while others will take a few years to fully develop—but they all set the foundation for the future of AI.

2025: Cost efficiency - pricing strategies

As AI technology continues to evolve, cost efficiency and model optimization have become central themes in the discussion around AI adoption. The industry is shifting towards smaller, more efficient models that deliver high performance while reducing computational and financial overhead.

One clear trend is the rise of smaller language models (SLMs) that run efficiently on local devices. I’ve been experimenting with WebLLM, which allows for in-browser inference of small models. Running it on my M1 MacBook has been surprisingly effective :D delivering good results even offline, making it particularly useful when I am on a plane.

Pricing strategies are also evolving... AI providers are reconsidering pricing structures, shifting towards pay-per-intelligence models that charge based on the quality of the response rather than just raw token consumption. The rationale behind this shift is that LLM token pricing behaves like a rapidly depreciating asset, with token costs dropping roughly every six months, they need new strategies to maintain profitability...

2025: Cost per response

Even though price per token is decreasing, a new challenge is emerging: the rising cost per response. AI models are increasingly relying on additional context retrieval (RAG), guardrails, multi-agent reasoning, and tool integrations to improve accuracy and reduce hallucinations. Each of these steps adds more computation and increases the number of tokens required to generate a single response. This means that even as individual token costs drop, the total cost of generating a high quality & structured answer may actually increase.

So, what does this mean for AI adoption? while hallucination reduction and improved reasoning are critical, they come at a cost and businesses must carefully decide where precision is worth the expense and where a leaner, more cost effective AI approach makes sense.

2025 - 202X - The Rise of AI Agents

AI agents, autonomous systems capable of performing tasks without direct human intervention, are expected to become a major trend starting in 2025. In a previous article, I explored how AI agents are evolving and the role they will play in the coming years (AI Agents Taking Over 2025).

While some early applications of AI agents exist today, 2025 is likely to be the year where the groundwork is laid for their broader adoption. This includes improvements in agent architectures, better integration with tools, and the development of ecosystems that allow agents to interact, collaborate, and function with greater autonomy.

The transition from simple AI-driven workflows to fully autonomous agents will take time. Some advancements may be visible this year, but it is likely that the most transformative applications of AI agents will take a few more years to mature.

2025 - Fine-Tuning for specific industries

The conversation around fine-tuning AI models for industry-specific applications is gaining momentum. More companies are recognizing the value of tailoring models to their unique needs, improving accuracy, efficiency, and overall effectiveness.

This year, we are likely to see more companies deploying models that are fine tuned, models that can be both proprietary and open-source. As an example Anthrophics Claude models can be fine tunes in Amazon Bedrock and we have put together this best practices for fine tuning. At the same time, open-source models such as LLaMA, Mistral, Phi, and Whisper are becoming easier to run once fined tuned, features in Amazon Bedrock Marketplace are streamlining access to a diverse range of foundation models, while AWS fine-tuning capabilities are making it easier to adapt models to proprietary data.

We can expect more accessible and cost effective approaches to gain intelligence with your own data instead of paying for a premium - as we discussed price per intelligence with bring your own intelligence.

2025 - Voice Integration in Conversational AI

Speech-to-speech models should become a major trend in 2025. I expect models to improve significantly, and companies to start integrating them more widely. Honestly, I’m tired of typing and I’d rather talk to my computer and even (maybe) have my computer talk back to me.

We’re reaching a point where speech recognition and AI-generated voices sound natural and understand context much better. I expected after ChatGPT advance voice to see more speech-to-speech models, but it seems 2025 will be the year for that.

I expect for 2025 that I can have a real back and forth conversation with Alexa or interacting with a virtual agent over a call center that actually understands and responds in a meaningful way, voice-enabled software and devices should finally take off this year—at least, I hope so!

2025 - Any to Any models

This year the rise in popularity of any-to-any multimodal models, might gain widespread adoption.

NExT-GPT: A multimodal LLM capable of processing and generating text, images, videos, and audio seamlessly. (arxiv.org)

AnyGPT: A unified multimodal LLM leveraging discrete sequence modeling for flexible input and output across multiple formats. (arxiv.org)

These models enable AI to take in video, images, speech, music, and text as inputs and generate any combination of these formats as output, offering unprecedented flexibility in AI-driven content creation and task execution.

2025 - Responsible AI

I have been having more conversations around Responsible AI... I’m hearing it from customers, at conferences, and across the industry... companies are looking for guidance, frameworks, and best practices to ensure their AI is fair, transparent, and accountable. Job postings for AI governance, responsible AI specialists, and core user protection roles are starting to appear in Linkedin - which signals that organizations are taking this seriously.

Regulations like the EU AI Act are setting new standards for AI oversight, and more companies are forming AI governance boards to address biases and build trust. But it’s not just about compliance—user experience and human-computer interaction will also play a major role in making AI usable, explainable, and ethical. As we move through 2025, I expect to see more structured approaches to Responsible AI, with companies embedding governance into their AI development lifecycles rather than treating it as an afterthought.

2025 - Hyper-Personalization and consumer experiences

Hyper-personalization is a term I keep hearing more and more. Businesses want to move away from static, one-size-fits-all experiences and create truly personalized interactions at scale. But doing this efficiently is still a challenge... sending millions of GenAI personalized emails isn’t cheap.

How do we crack hyper personalization? Might token cache help? Lets see what 2025 brings...

2025 - AI Workflows

AI is moving beyond just generating text or images—it’s now learning how to use your computer !!! We’ve already seen Anthropic release computer-use capabilities (Anthropic’s AI can now use computers), and I expect this trend to accelerate.

In 2025, we will likely see AI tools automating repetitive, manual tasks within existing enterprise software. Think about copy/pasting between spreadsheets, updating ERP systems, processing invoices or data entry... these are all tasks that GenAI should start handling autonomously.

2025 - Sustainability, Energy, and Environmental Impact

AI’s energy consumption is becoming a real concern. With data centers consuming more power than ever, we are starting to see physical constraints on energy availability. Companies will need to optimize AI workloads for efficiency as energy costs and environmental impact become critical considerations.

I wouldn’t be surprised if we start tracking AI efficiency in new ways, with token per kilowatt-hour (kWh) becoming a key performance metric, alongside the more familiar token-per-dollar cost analysis. There’s already a push for sustainable AI computing—AWS, for example, is working on ways to optimize AI workloads for environmental sustainability (AWS AI Carbon Footprint) (Sustainable AI Workloads).

2025 - Generative AI and Humanoids - Home Appliances

Generative AI is moving from software to hardware, powering everything from smart phones, to hopefully home appliances to humanoid robots. I expect this year we’ll GenAI-driven appliances—voice-enabled ovens, toasters, water heaters, and other smart devices that interact naturally with users.

Humanoid robots are gaining traction too... The hardware needed to run GenAI models is getting smaller, cheaper, and easier to integrate, making these robots more context-aware and capable of performing household or industrial tasks. While full-scale humanoid adoption is still a few years away, 2025 could be the year we see the first mainstream commercial use cases take off.

202X - Progression from ANI to AGI and ASI

The AGI debate exploded in late 2024, with speculation about whether OpenAI had already achieved Artificial General Intelligence.

? Artificial Narrow Intelligence (ANI) – AI specialized in one task, like language translation or image recognition.

? Artificial General Intelligence (AGI) – AI with human-like reasoning and adaptability across multiple tasks.

? Artificial Superintelligence (ASI) – AI that surpasses human intelligence in every domain.

Sam Altman has predicted that AGI could arrive as early as 2025, which raises a big question—if AGI is achieved, will ASI become the next target?

If we’re truly approaching AGI, the conversation in 2025 will shift toward what comes next?

How we navigate the risks and opportunities of AI that is more capable than humans?