By David Wong and Joel Hron

When ChatGPT launched in late 2022, it was a wake-up call for many companies. For us, it wasn’t just a signal, it was a catalyst. It validated the long-held ambitions of our engineers and product leaders to apply AI in solving the kinds of customer problems we had struggled to crack with earlier-generation technologies.

Traditional chat-based interfaces, while useful for reactive tasks, often struggle to stay aligned with user goals, handle multistep reasoning or take meaningful action. They can be more like teammates waiting for the next assignment than ones who anticipate needs.

Now, a new chapter is emerging as agentic AI takes center stage. We have access to tools that don’t just respond, but act. These agents can interpret complex objectives, plan multistep tasks, adapt in real time, and execute workflows alongside human professionals.

The evolution of AI in professional domains has demanded platforms that prioritize trust, accuracy and domain expertise — values we’ve spent years integrating into our systems. Our work includes agentic capabilities in products such as CoCounsel Tax, a vertical-specific AI for tax and accounting, and CoCounsel Legal, the legal industry’s first professional-grade agentic AI research tool.

CoCounsel can interpret complex objectives, plan and execute multistep workflows, and deliver results with the same precision as an experienced lawyer, accountant or compliance officer. It draws on industry-leading tools — including Westlaw for legal research, Practical Law for procedural guidance, and Checkpoint for tax and accounting expertise — allowing it to do real work, like using a calculator to complete a tax return.

This means that instead of simply providing information, professionals can delegate complete assignments — like working through a tax return or drafting and reviewing legal motions — knowing the work will be handled to industry standards. Behind these capabilities is deep expert guidance from thousands of legal, tax and compliance specialists, ensuring that outputs are not only technically accurate but also aligned with the way seasoned practitioners actually work.

Watching this transformation from our vantage points as chief product officer and chief technology officer at a company with a 150-year-plus history of providing trusted expertise to professionals, it was clear early on that our roles and products would never be the same.

This shift is especially significant in the high-stakes domains we support, including law, tax, compliance and risk. In these industries, accuracy, transparency and trust are paramount. AI must perform reliably, align with regulation and support nuanced human judgment.

Transitioning from answers to outcomes

Three years ago, our AI journey looked very different. We were early adopters of tools like GitHub’s Copilot, and today, more than 80% of our engineers use them weekly. But that was just the beginning. Now, we’re focused on full agentic systems — tools that can reference internal documentation, interact with servers, retrieve live data, triage and fix bugs, and even build applications from scratch.

Unlike software code, which has testable and verifiable outcomes, many expert domains have a range of acceptable answers, some better than others. That is where human judgment is pivotal.

We’ve learned that embedding AI engineers within domain expert teams accelerates iteration and trust. Our 250-plus AI engineers work alongside more than 4,500 domain experts including lawyers, accountants and compliance leaders, to shape AI into real-world capabilities.

Imagine a system for lawyers that doesn’t just suggest clauses in a contract, but compares documents, identifies legal risks and escalates complex issues for expert judgment. Or a tool for tax professionals that goes beyond retrieving tax codes to flagging compliance risks, adapting to real-time data, and completing multistep workflows. These are not abstract concepts. They’re embedded, outcome-focused systems that are starting to redefine how professionals work.

Intelligence is only half the battle

Tech culture has long celebrated moving fast and breaking things. But in law and tax, breaking things isn’t an option. Speed is important, but trust is even more valuable. No matter how advanced agentic capabilities get, they won’t be adopted if professionals can’t trust them. Intelligent systems need to go beyond results to provide transparency, consistency and oversight.

Designing effective agentic products is as much a human challenge as a technical one. Agentic systems need to know when to escalate decisions, how to explain their reasoning, and how to adapt without straying from the user’s standards.

Human-in-the-loop controls are central to this process. Experts guide development, stress-test edge cases, and ensure performance in the contexts that matter most.

When systems are empowered to plan and act, small misalignments in goals, context or data quality can lead to significant errors. Overreliance on automation without clear guardrails can sideline human judgment when nuance matters most.

Some of the most powerful features of agentic systems are invisible, such as their ability to maintain context, reason over multiple sources, or decide when not to act without enough information. These are what allow professionals to work with greater confidence.

Rebuilding systems and teams

In our leadership roles, we focus on adaptability. We value curiosity, the ability to learn quickly, and cross-disciplinary work, rather than specialization alone.

We have restructured for small, highly aligned teams and empowered subject matter experts to shape AI behavior. We iterate quickly without sacrificing rigor or trust.

The future of true agentic capabilities is not about the fastest or most autonomous system. It is about building the most useful one: a system that can reliably assist professionals in moments where stakes are high and time is short. The most meaningful agentic AI will expand what professionals can achieve, especially when margins for error are small.

 David Wong is the chief product officer at Thomson Reuters, where he leads the product management, editorial, content and design, and product analytics teams. He oversees product strategy and product development of Thomson Reuters’ global software and business information services portfolio, which includes creating new AI software and technology for the company’s professional customers. Wong has more than 15 years of experience building business-to-business software, information services, and machine learning and AI systems. Previously, he served as a senior product leader at Facebook. He has also held senior roles at Nielsen and worked at McKinsey & Co. as a management consultant. Wong holds a degree in engineering science from the University of Toronto and is an inventor with four named patents.

Joel Hron is the chief technology officer at Thomson Reuters, where he leads product engineering and AI R&D across legal, tax, audit, trade, compliance and risk. He joined Thomson Reuters in 2022 through the acquisition of ThoughtTrace, where he served as CTO. Since then, he has helped transform Thomson Reuters’ technology strategy. While leading TR Labs and AI, his teams launched seven generative AI products in just 18 months, including AI assistants for legal research, tax research and contract drafting. Hron holds a master’s degree in mechanical engineering from the The University of Texas at Austin and a bachelor’s in engineering from Texas Christian University.

Illustration: Dom Guzman

A few years ago, I began advising a startup developing physical infrastructure for smart cities, with an AI layer on top. I vividly recall conversations with investors who told me, “We don’t touch anything hardware related.” They said it was too slow, too capital-intensive, too risky.

Fast-forward to today, that same company has rolled out its solution across dozens of U.S. cities and now employs hundreds of people. The very hardware once deemed “too heavy” has become the immovable foundation of its market leadership.

And yet this isn’t an isolated story. Some of the most valuable companies in the world today such as Nvidia and Tesla, are fundamentally hardware-driven. Their sky-high valuations stem not just from software, but from controlling the infrastructure that enables others to build.

In the age of AI, when software can be built (and copied) at lightning speed, hardware companies offer something far more durable: presence, permanence and defensibility. Here’s why I believe it’s time for venture capitalists — and entrepreneurs — to rethink their stance on hardware.

Hardware Is the new moat

Software is increasingly commoditized. No-code, AI coding assistants and open-source frameworks have narrowed the gap between vision and execution.

In contrast, physical hardware is much harder to replicate or replace once installed. When your device is literally bolted into a city’s infrastructure, the switching cost is not just technological, it’s political, logistical and financial. That’s a moat software alone rarely provides.

Software is still relevant, but it builds on hardware

The misconception is that hardware companies are “just hardware.” In reality, the best ones are platforms. Once deployed, they can continuously upgrade their offering via software, new features, analytics, integrations and even AI layers.

That base unit of hardware becomes your permanent sales rep on the ground, enabling upsells and renewals without reselling the core product.

Bias against hardware is an outdated vestige

Many investors avoid hardware because of legacy scars: high burn, manufacturing delays, complex supply chains. But those assumptions don’t always hold today. Advances in prototyping, global contract manufacturing and recurring-revenue models have reshaped the economics. When properly structured, a hardware business can achieve healthy margins, strong retention and scalable growth.

I urge founders and VCs alike not to dismiss hardware out of habit, because the next generation of enduring tech giants may be building their moat from silicon, steel and infrastructure.

Itay Sagie is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to Crunchbase News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at SagieCapital.com. Connect with him on LinkedIn for further insights and discussions.

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Illustration: Dom Guzman

If the five most-valuable U.S. technology companies want to buy startups, they can certainly afford to do so.

Today, the Big Five — Nvidia, Apple, Microsoft, Alphabet and Amazon — have a combined market capitalization of more than $16 trillion. They’ve also got close to $400 billion in cash on the books between them.

Even with that massive spending power, however, the largest tech companies have not bought many startups this year. Per Crunchbase data, they’ve disclosed just 10 deals to purchase private seed or venture-funded companies.

This doesn’t reflect a change in M&A appetite. Rather, It’s a continuation of a trend toward fewer acquisitions that’s persisted for a few years, as charted below.

Wiz is the one really big deal amid several smaller ones

Before we push the slow M&A narrative any further, however, it would be remiss not to mention that this year’s tally does include the largest planned startup acquisition of all time. That would be Google’s agreement, announced in March, to buy cloud security provider Wiz for $32 billion in cash.

It’s not a done deal, as the transaction still faces scrutiny from antitrust regulators. It may help that Google is planning to buy a cybersecurity company, and not, say, a giant acquisition of a search engine or advertising platform. But still, the sheer dollar size means some pushback is likely.

Meanwhile, among the rest of this year’s disclosed Big Five startup acquisitions, listed below, none comes with a reported price.

Still, these could be big deals, and there could be more

Some deals without disclosed prices still do involve companies that previously raised quite a bit of funding and likely sold for good-sized sums.

For instance, Gretel, a synthetic data platform for AI, raised about $68 million in seed and early-stage funding before selling to Nvidia in March. And Axio, a Bangalore-based fintech startup, raised more than $200 million in debt and equity before selling to Amazon early this year.

In other cases, the Big Five snapped up companies still in seed stage. This spring, for instance, Google snatched up design startup Galileo AI, which had raised a few million in seed funding. And Amazon snagged Bee, the seed-backed developer of an AI-enabled wearable.

It’s also likely there were more acquisitions we haven’t heard about. It’s not impossible for a tech giant to pick up a seed-stage or stealth startup without an announcement or attracting attention, if it so desires.

Also, because the Big Five are so valuable, they may not have to disclose details for acquisitions that might qualify as significant and require reporting for a smaller company.

Other strategies: partnerships and wooing startup talent

Of course, tech giants don’t need to buy a company to have a stake in it or otherwise benefit from its success.

All of the Big Five are prolific startup investors. That includes leading several of the larger GenAI financings and taking equity stakes as well as forming partnership agreements.

They’re also big acquirers of talent and have ways to pursue this goal without buying companies outright. Last year, for example, Microsoft drew headlines when it lured two co-founders of GenAI startup Inflection AI away from the company, hired most of its 70-person staff, and licensed its technology.

The new normal?

If something that looks like a trend or cycle persists long enough, it’s logical to conclude that it might instead be the new normal. This is one interpretation of the persistent slow pace of startup acquisitions by the Big Five.

While a couple decades ago, getting acquired by one of the top tech giants was an oft-discussed startup exit path, that strategy now looks passé. After all, the Big Five have plenty of reasons not to do an acquisition, including regulatory and compliance burdens and the prospect of antitrust pushback. Additionally, they can pay whatever it takes to simply license a technology or lure top talent.

So far, there’s zero indication that public markets care about the Big Five’s slow M&A pace. Companies don’t get valuations in the trillions because investors are pessimistic about their prospects.

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Illustration: Dom Guzman