From Multi-Agent Models to Hardware Advances – Key AI Trends for 2026

By Gaby Diamant, BridgeWise CEO

Earlier this month, Time Magazine named the “Architects of AI” as its Person of the Year for 2025. With a cover featuring AI leaders including Sam Altman, Mark Zuckerberg, Elon Musk, Jensen Huang, and others, the recognition signals that AI platforms have reached a new level of mainstream acceptance.

While the list usually features political or social figures, it has occasionally included tech figures in the past, including Elon Musk, Mark Zuckerberg, and Jeff Bezos. The magazine also recognized “The Computer” as Machine of the Year in 1982.

I think this choice by Time is a unique opportunity to review where the AI industry stands at the end of 2025 and what trends we can expect for 2026. Now, you might say that 1982 was a bit early to recognize the significance of the personal computer, or that Bezos was chosen a year before the dot-com crash. Both are true. But personal computing and the internet were ubiquitous only a few short years after Time selected them. I have no doubt AI will prove the same, even if some predict the bursting of an AI bubble.

But before I look at 2026, I want to take a look back at the predictions I made a year ago about the important AI trends for 2025 and see how they held up. I highlighted three key trends for 2025:

• The Rise of the Multi-Agent Model
• More Efficient AI Models
• More Players Following BridgeWise into the Investment Intelligence Space

Looking back, all three of these trends have proven true over the past year. The multi-agent model, also referred to as agentic AI, is moving forward at full speed and driving the growth of connected fields such as verticalized AI, which I’ll elaborate on later in this post. More AI models have embraced efficiency, since there’s no way to brute-force scale and there’s no chance AI models will continue to grow indefinitely. We aren’t going to go from 2 trillion parameters to 20 trillion; It won’t work.

Finally, we’ve seen more growth in the investment intelligence space. You only need to look at OpenAI hiring over 100 former investment bankers to train financial AI, or Anthropic’s Claude for Financial Services offering, to understand that the industry is looking at the investment space as an opportunity.

Looking ahead to 2026, I want to focus on three areas.

Multi-Agent and Verticalized AI See Accelerating Growth

This was one of my predictions from last year, but we now see that it has become the main paradigm of AI services and will continue to lead and develop over the course of the next year. 

While there are many different terms being used – multi-agent, agentic, multi-modal – they are all part of the larger overarching trend in AI. Alongside verticalized AI that targets distinct industries, these trends lead to a future where multiple subject-specific models will be developed to tackle unique challenges, with AI services leveraging them to support the full spectrum of possible queries.

For specific examples, look at Harvey AI, the leading legal AI provider. The company recently raised money at an $8 billion valuation. In the health space, AI medical scribe Heidi raised money at a valuation approaching half a billion dollars. Or look at what our friends at NVIDIA are doing with Nemotron. We were fortunate to be one of the first companies to use these new models on Amazon Bedrock to boost our testing capabilities and ensure accurate outputs.

All of it adds up to an agentic world in which specialized models exist to tackle the myriad set of knowledge bases and problems we face, and solve them with highly targeted, context-specific models.

A Push for Hardware Innovation

While the current set of hardware technologies has enabled the powerful AI solutions we see today, and near-term advances should allow the industry to continue to develop, we are starting to approach the point of diminishing returns with the current generation of AI silicon. One need only look at the power demands of rapidly growing data centers around the world to realize something needs to change.

This is why I predict we are going to see accelerating investment in new AI-focused hardware technologies. One area that could garner a great deal of attention is quantum computing. Now, you may have heard this technology talked about for some time, and it feels like one of those areas where the big breakthrough is always 10 years away.

But quantum computing and AI are a perfect fit due to the parallel nature of the technology. A typical computing system will try to solve one problem, and then another, and another, in serial. With quantum computing, the problems are solved all at once. I’m oversimplifying, and the challenges to implementing quantum computers are serious, but the opportunity the technology carries to unlock accelerated AI development cannot be discounted. And if quantum computing isn’t the solution, the demand for AI hardware will push the industry to find an alternative.

Consolidation in the AI Industry

While the technological race continues at full speed, don’t be surprised if we see major developments on the business side of the AI industry. The opportunity for consolidation of services and AI players is approaching critical mass, and you shouldn’t be surprised if we see major developments in this direction, whether through acquisitions, partnerships, or simple market dominance.

In other areas of the tech world, we see a handful of dominant players, often one, sometimes two, but you can just look at the last 40 years of tech development to see how early mover advantages don’t always translate into future dominance.

That’s one of the reasons I think the whole industry needs to keep its eyes on Google. The momentum they’ve established with Gemini is remarkable, and they don’t appear to be slowing down. I’ve said before that I would bet my money on Google taking the lead in the AI space, and while nothing is guaranteed, I certainly wouldn’t want to bet against them.

But regardless of whatever consolidation happens in the wider AI space, I feel confident about our corner of it here at BridgeWise. When we look at AI-driven investment intelligence, I know that no player has a more robust, reliable, and comprehensive solution.

We cover a broad range of asset classes – from equities to ETFs and funds, bonds, crypto, and more – and we provide a host of solutions and services that are tailored for multiple categories of financial institutions and investors.

Our AI models are unmatched in their performance and reliability, and most critically, in the way we have mitigated the hallucination challenge, which can impede AI services operating in a highly regulated space such as ours. And we can tie all of these services together with Bridget™, the most advanced AI chat for investment intelligence.

With an AI industry that is moving quickly towards a multi-agent future, while also facing the rising prospects of consolidation, at BridgeWise, we are well-positioned to navigate what is certain to be one of the most dynamic phases of the accelerating AI revolution. I can’t wait for the industry to see some of the surprises we have in store for 2026.