A longtime Microsoft engineering leader who was previously a Copilot chief technology officer is joining Amazon Web Services to work on its competing AI tools for coding and workplace productivity.

Pedram Rezaei, a nearly 20-year Microsoft veteran who was most recently a corporate vice president on the Microsoft Superintelligence team, starts Monday as a distinguished engineer in the AWS Agentic AI and Emerging Technologies organization, led by Swami Sivasubramanian.

Rezaei will work initially on Kiro, the AWS coding tool, and Quick, Amazon’s AI assistant for workplace tasks, Sivasubramanian wrote in a memo to his team Monday. “He is a hands-on engineer who is still in the code every day, and he plans to keep it that way here,” he added.

Kiro’s user count doubled from the first quarter to the second, and monthly AI users for Quick grew fourfold, according to the memo. Rezaei’s plan for his first few months is to “meet people, learn how things work here, and ship something real,” Sivasubramanian wrote.

Sivasubramanian’s organization was expanded in July to include emerging technologies, part of a broader reshuffling of Amazon’s AI teams.

The hire comes as Amazon and Microsoft compete more directly in AI tools for work. Microsoft last month unveiled an all-in-one Copilot app that combines chat, coding and agents, its answer to OpenAI and Anthropic. Amazon made its Quick desktop app generally available in September and brought its main features to mobile.

Rezaei joined Microsoft in 2006 and held senior technical roles on Power BI, Dynamics 365, Cortana, Bing and Azure Maps before moving to Copilot, according to his LinkedIn profile. He became Copilot’s CTO in February and moved to the Microsoft Superintelligence team in June, according to the profile. Sivasubramanian’s memo said Rezaei was most recently working on training foundation models.

We’ve contacted Microsoft for comment on his departure.

Rezaei is the latest Microsoft executive to move to AWS this year. Shawn Bice returned to AWS in May from a security role at Microsoft to lead its Automated Reasoning Group.

In his memo Monday, Sivasubramanian also told his team that two longtime AWS engineers, Marvin Theimer and Reto Kramer, have returned to work on neurosymbolic AI and automated reasoning, which he called “one of our biggest bets for the future.” Both came back from Microsoft, according to their LinkedIn profiles.

Neurosymbolic AI pairs large language models with rule-based systems that can check whether an AI agent is following a company’s policies and procedures.

Speaking at Madrona’s IA40 Summit in Seattle last week, Sivasubramanian said nearly every CIO and CEO raises the same question about AI agents. “How do I know it is always going to obey all my rules and regulations?” he said. “This is a real concern.”

Solving that problem “actually unlocks so many use cases for all these enterprises,” he added.

Theimer, an AWS distinguished engineer for nine years before leaving in 2022, joined Microsoft in July 2024 as a corporate vice president and technical architect and stayed until March. It was his second stint at the company, where he worked from 1998 to 2006. He rejoined AWS in July as a vice president and distinguished engineer.

Kramer, who led an automated reasoning team at AWS before leaving in 2023, joined Microsoft in September 2025 as vice president of Security Platform & AI and stayed until May. He rejoined AWS in September as vice president of applied science.

Sivasubramanian hinted at more hires to come, writing that Rezaei joins “as we continue to build out our technical leadership team to help take on the ambitious work we have planned.”

Executives from companies such as Microsoft, Amazon, Anthropic and Stripe agreed on a lot this week about where AI is headed. But one big question was left unresolved: who keeps the relationship with the customer, and the data that comes from it, once an AI agent is in the middle?

That question was a recurring theme at Madrona’s IA40 Summit, the venture capital firm’s annual gathering of AI startups, investors and tech executives, held Wednesday at the Four Seasons Hotel on the downtown Seattle waterfront.

As Madrona Managing Director Matt McIlwain said in his closing remarks, the question of who gets to use the data that comes from people’s engagement with AI systems came up “over and over and over again” during the day.

GeekWire attended the entire day and came up with these takeaways, reducing our own human bottleneck (see below) with lots of back-and-forth with Anthropic’s Claude and other AI tools.

AI agents are coming between companies and their customers: Charles Lamanna, the Microsoft executive vice president who oversees Microsoft 365 and the platform behind Copilot, predicted most business software will end up being used by AI assistants on a person’s behalf, leaving software makers with less power to set prices.

Jean-Denis Greze, CEO of Town, a startup that makes an AI assistant for work, went further. Since about July, he said, AI has been able to operate a browser or a computer almost as well as a person, at a reasonable cost. That means an AI assistant can work through an app’s user interface the way a person does, without needing an API from the software maker, he said.

“Everything is going to become a thin app because the better the AI gets at using the computer, the less the app matters as a unit of software,” Greze said, echoing the term Lamanna had used that morning for software that people open only briefly.

The same shift is under way in retail. Maia Josebachvili, Stripe’s chief revenue officer for AI, said commerce by AI agents on Stripe was roughly flat for eight or nine months and then rose sharply in the past six weeks.

Many merchants make their money by getting shoppers to add to their orders at checkout and by showing them ads, she said, and they lose those chances when an agent does the buying. That won’t work over the long term, Josebachvili said.

That conflict is playing out in real time in a high-profile dispute between two tech giants. Amazon blocked Meta’s Muse assistant from shopping on its site last month.

The data side of the question came up in a session with Anthropic Chief Technology Officer Rahul Patil. Moderator Raphaëlle d’Ornano asked who owns the record of an AI agent’s work, including its mistakes and corrections, and whether it belongs to the customer. She said she has never gotten a clear answer.

Patil didn’t answer directly. He said each company that supplies the software for running agents will work to improve them, and “will use every data that’s available to them to make it better.”

“It’s good for the ecosystem if the agents actually improve,” he said.

Swami Sivasubramanian, Amazon Web Services’ vice president of Agentic AI and Emerging Technologies, said one of the least appreciated parts of making AI agents work is getting them the right company data for the task.

AWS in June announced a service for that purpose, called AWS Context, which maps the relationships in a company’s data for agents to use. Sivasubramanian said it’s meant to work across platforms and cloud providers, and he expects this kind of service to be a basic building block for the next 20 years, as cloud storage and databases were two decades ago.

Most companies aren’t keeping up with the technology: Patil said Anthropic writes about 200 times as much code as it did 18 months ago. Some customers have doubled or tripled their output, he said, but almost none have seen gains like Anthropic’s.

“The bottleneck is actually not AI. The bottleneck is human,” said Archana Vemulapalli, global head of AI product management at Goldman Sachs. A company’s roles and processes were built before AI, she said.

Sivasubramanian said teams inside Amazon were showing him impressive AI agents they had built in two or three weeks. When he asked when the agents could be rolled out, he said, almost every team told him it still had to work out security, identity and monitoring.

McKinsey senior partner Lari Hamalainen said about 40% of companies say AI has increased their profits, but only 6% describe the increase as substantial.

Even as AI becomes more and more capable, there’s still lots of work to do to close the gap between “the goals, the capabilities, and what we all are aspiring to do,” McIlwain said.

Companies are split on depending on a single AI provider: Anthropic’s Patil, notably, argued that large companies are spending too much effort preserving the ability to switch between AI providers. To keep that option open, he said, they end up building for the least common denominator, limiting themselves to the features every AI model shares. That effort also takes their engineers away from the work that sets their own company apart.

Carlos Guestrin, co-CEO of the AI startup Noeri, offered an alternative take on the same panel. Intelligence shouldn’t be controlled by one or two companies that own the models, he said, and every company should be able to build and own its own AI systems.

Guestrin is a Stanford computer science professor and former University of Washington professor who founded the Seattle machine learning startup Turi, which Apple acquired in 2016.

Eno Reyes, co-founder and chief technology officer of Factory, a startup that makes AI coding tools, said on a later panel that many businesses don’t see a way to build their future without ceding control to one AI lab. Thomas Dohmke, the former GitHub CEO who now leads the startup Entire, said developers always want choice.

Amazon works with more than one AI lab. It is a major Anthropic investor and partner, and Dan Grossman, Amazon’s vice president of business and corporate development, pointed out on an investor panel that Amazon had launched a managed agents product with OpenAI that week.

Amazon also sells its own product for running AI agents, AgentCore, which overlaps with Anthropic’s Managed Agents.

Companies don’t yet trust AI with important decisions: Zico Kolter, the Carnegie Mellon professor who chairs the safety and security committee of OpenAI’s board, said the ability to control AI systems has to keep pace with their capabilities, which might mean developing them more slowly than is possible.

Sivasubramanian said executives worry about whether an AI agent will always follow their company’s rules and regulations. Companies can keep the creativity of AI models, he said, by pairing them with separate systems that check a model’s work against the company’s rules and catch violations.

The money is concentrated in a few companies: The companies on this year’s IA40 list have raised $410 billion since they were founded, according to Madrona’s data. OpenAI, Anthropic and Databricks account for 92% of it.

Anthropic raised $143 billion of its $161 billion total in the 12 months ending Aug. 15, a figure that includes debt. The company has filed confidentially for an initial public offering and is seeking a valuation of about $2 trillion, Reuters reported this week. Bloomberg reported that it could go public as early as mid-November.

Capital spending by Amazon, Alphabet, Microsoft, Meta and Apple is projected to rise 74% this year to about $742 billion, according to PitchBook figures Madrona presented.

Lamanna said Microsoft’s heaviest AI users among its software developers are each on track to spend more than $1 million a year on AI usage, even with the company’s internal discount.

Madrona’s closing slide said returns on AI are still early for most of the companies using it but look inevitable based on what the earliest adopters are seeing.