AWS just made a pretty bold move in the artificial intelligence market.
The cloud giant announced a $1 billion investment to create a new unit called Forward Deployed Engineering, or simply FDE. The core idea is to place specialized engineers directly inside client companies, working side by side with internal teams to speed up real AI adoption — in practice, no fluff.
And this is no small deal.
We are talking about thousands of engineers, organized into small groups of five or six people, operating within customer environments and delivering results in a matter of weeks.
The announcement was made during the AWS re:Invent conference in Las Vegas, and it marks a major shift in how Amazon Web Services plans to compete in the applied artificial intelligence market.
The model itself is not exactly new — Palantir was using something similar over a decade ago — but it has come roaring back now that OpenAI and Anthropic also announced similar initiatives this year, through partnerships with banks, private equity firms, and consultancies.
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The difference here is that AWS is the first major hyperscaler to enter this race in a structured way, with its own dedicated unit, a billion-dollar budget, and clients like the NBA, the NFL, Ricoh, and the Allen Institute already working with its embedded engineers. 🚀
What is a forward deployed engineer and why it matters now
The idea behind the forward deployed engineer is simpler than the name suggests. An FDE is basically an employee who gets embedded directly inside another company to help accelerate a technical transformation. Instead of selling an artificial intelligence platform and leaving the client to figure out how to use it on their own, AWS sends its own specialists right into the company.
These engineers stay for a set period, dive into the real pain points of the business, understand the available data, legacy systems, and strategic goals, and then roll up their sleeves to build solutions that actually work. It is not traditional consulting. It is not training. It is joint execution, with accountability on both sides.
According to the company, these professionals will work closely with clients’ business, engineering, and security teams. The goal is to leave behind self-sufficient teams with new solutions and capabilities in just a few weeks. And there is an interesting detail: these engineers do not work alone. They operate alongside AI agents, tools capable of completing tasks independently on behalf of users.
As Francessca Vasquez, AWS vice president of engineering and frontier AI services, explained, the company already had this type of capability scattered across the organization over the years, but now decided to bring it all together under a single business unit with a common deployment framework. It is the first time AWS has done this in such a structured way.
How the $1 billion investment will work in practice
The investment announced by AWS will fund the creation and operation of this embedded engineering unit, with massive hiring of engineers specialized in artificial intelligence, machine learning, data engineering, and systems integration. These professionals will be organized into lean squads, usually five or six people, deployed directly into client environments for periods that vary depending on project complexity and the company’s level of technological maturity.
The idea is that each squad goes in with surgical focus on a specific problem, delivers value quickly, and then transfers knowledge to the internal team before moving on. As Vasquez pointed out, the currency that clients value most right now is speed. The FDE ends up being a go-to choice for anyone looking to accelerate time to value for their stakeholders, their own customers, and their executive teams.
The first confirmed clients already give a sense of how far this initiative could reach. The NBA and the NFL, two of the biggest sports leagues in the world, are on the list, along with the Allen Institute, a global benchmark in scientific research, and Ricoh, the Japanese technology and printing giant. This shows that AWS is not just targeting the traditional enterprise market.
Sectors like entertainment, sports, and science are also in the crosshairs, which makes sense when you think about the volume of data being generated and the number of processes still done manually or with outdated tools. According to Vasquez, the next wave of adopters is expected to come from highly regulated industries with diverse data sets, precisely where complexity demands closer technical support. Technical transformation in these segments can generate visible and rapid impact, which serves as a showcase to attract new clients.
Another relevant point is that this model creates a virtuous cycle for AWS itself. The more engineers embedded inside client companies, the deeper Amazon’s infrastructure becomes rooted in those organizations. Every solution built uses AWS services, every AI model trained consumes cloud resources, every integration developed increases the dependency — in a good way — on the Amazon ecosystem. It is an investment strategy that pays for itself through client retention, revenue expansion, and a stronger competitive position in a market growing at breakneck speed. 💡
The race for applied AI has already begun
The move by AWS is not happening in a vacuum. In May, Anthropic announced the formation of a new AI services company in partnership with Blackstone, Hellman & Friedman, and Goldman Sachs, focused on helping mid-market companies deploy its Claude models.
Just days later, Anthropic’s biggest rival, OpenAI, revealed OpenAI Deployment Co., alongside firms like TPG, Advent International, Bain Capital, and Brookfield Asset Management. The new organization was created to expand OpenAI’s ability to embed engineers inside companies dealing with complex problems in demanding environments.
And Palantir, the pioneer of this model of physical presence inside companies, saw its approach validated by the market for years before it became a broader trend. What is happening now is a convergence: the biggest technology companies in the world have all reached the same conclusion that selling access to an artificial intelligence tool is not enough.
It is worth noting that Amazon has already poured billions of dollars into both Anthropic and OpenAI. Even so, the company’s executives are not hiding their ambition to compete directly with these labs in certain areas. An AWS spokesperson said the company hopes to have the opportunity to work with the FDE units from OpenAI and Anthropic, and that more details about partnership programs will be shared soon.
What puts AWS in a unique position in this race is exactly its scale. No other player in the market has the combination of global infrastructure, AI services portfolio, established customer base, and now a dedicated unit with a $1 billion budget to execute this vision. Companies already running on Amazon’s cloud face a much lower barrier to entry for taking advantage of this initiative, because the data is already there, the systems are already integrated, and the teams already have some familiarity with the tools.
The step of bringing in an embedded engineering squad becomes much more natural in this context than migrating to a completely new platform just to get access to this kind of support. And as Vasquez reinforced, this offering is aimed at clients who are genuinely looking for ways to evolve their workflows.
For the market as a whole, this move signals an important shift in how technical transformation will play out over the coming years. The era of big consulting contracts that take months to deliver a diagnosis is fading away. What companies want now is speed, execution, and results. Embedded engineering, the way AWS is structuring it, answers exactly that demand. Agile squads, a focus on rapid delivery, knowledge transfer, and intensive use of the best artificial intelligence tools available.
This could be the model that defines how AI actually reaches businesses — not as a promise on a slide deck, but as something that genuinely works in day-to-day operations. 🤖
