Microsoft Commits 6000 Employees to AI Deployments - Forward Deployed Engineers Force Feed Tech

The episode opens with the CNBC headline that Microsoft is committing 2.5 billion dollars and 6,000 employees to a new AI implementation unit called Microsoft Frontier. Eli reads this not as a triumphant expansion but as a tell. In his framing, the big tech companies are pulling out the big guns to shove AI down everybody's throat because consumers and corporations are not adopting it fast enough on their own. The forward deployed engineering model, he explains, means Microsoft staff physically embed inside other companies' offices to, in his words, duct tape some AI solution together and convince major corporations there is any value in the technology.

He contrasts this with how genuinely useful technology used to be sold. Nobody had to spend billions convincing a CEO to buy email, file servers, or print servers. He acts out the old pitch: tell a CEO that instead of every employee owning a printer, they can buy one 1,000 dollar printer that a hundred people share, and the sale closes itself. When technology delivers obvious value, selling it is easy all day long.

That contrast becomes his central thesis. When companies must spend billions upon billions to force their technology into corporations, that effort itself says something about the actual value of the product. He notes this is not a Microsoft-only phenomenon: the announcement came two days after Amazon committed a billion dollars to a similar initiative, and both Anthropic and OpenAI established forward deployed groups in May, partnering with private equity firms, banks, and consulting firms.

Eli sharpens his skepticism by pointing to the value proposition question. He cites Accenture, the global consulting firm that has been pushing AI on its clients for two years, which has publicly acknowledged that the executives it supports do not see a recognizable value in their AI deployments. If the biggest, most sophisticated companies already cannot identify returns, he argues, that is a serious warning sign about the technology itself.

He raises a second, more practical worry: institutional knowledge. When forward deployed engineers from Microsoft, Anthropic, or another vendor come into your environment and build something, what happens when they leave? He wonders whether organizations will retain the ability to support systems that have suddenly become core to their operations.

To make the point he reaches back to the certification era of MCSE, CCNP, and CCIE credentials. Companies like Cisco and Microsoft built certification and partnership programs precisely so that clients could support deployed technology long-term. In the old model you might hire HP to do an initial build-out or architecture design, but the expectation was that your own certified staff would carry that forward into the future. He questions whether the forward deployed engineering rush is building any comparable durable capability.

Eli walks through the specifics of the CNBC report to underline the scale and nature of what Microsoft is deploying. The 2.5 billion dollar investment creates the Microsoft Frontier group, and the 6,000 people being embedded with clients are not purely engineers. The division draws on existing Microsoft full-time employees, technical consultants, support staffers, and salespeople with experience in specific industries. That mix, he stresses, means a lot of people, including sales staff, will be landing in client server rooms, and he pointedly asks what they will leave behind when they go.

He quotes Judson Althoff, CEO of Microsoft's commercial business, who framed the effort as a response to the reality that customers are in very different places trying to figure out AI, unsure whether to snap to a single model from OpenAI or Anthropic, adopt a whole family of models, or approach the problem from a technology-first mindset versus reshaping existing business processes. Althoff credits the data analytics vendor Palantir with popularizing the forward deployed job title, a term rooted in the US military's forward deployed forces abroad; Palantir sent employees to US bases in Afghanistan.

Eli notes that Accenture and Ernst and Young both announced plans earlier in the year to ally with Microsoft on AI-centric forward deployed programs, and that Microsoft already generated about 2.1 billion dollars in enterprise and partner services revenue in the March quarter, up 2.5 percent year over year. His verdict on the whole enterprise is dripping with sarcasm: this is going to go so well.

Eli's core argument is that AI is not worthless, it is simply moving too fast, and he anticipates the pushback that he is just a boomer who does not get the value. He rejects that: he grants there is a great deal of amazing value clumped into the technologies called AI. His point is that implementing any technology into the real world simply takes time, regardless of how good the tech is on paper.

He illustrates with a story from a corporation he worked for that deployed an efficiency system, something like a telephone system meant to dramatically improve the productivity of the secretaries. The servers worked, the software worked, everything technically worked fine. But the secretaries hated it and refused to use it, and the project died. It failed not because of bad hardware or software, but because nobody communicated with the secretaries, understood their real workflow, or grasped how the tool would integrate into their daily jobs.

He reinforces this with his construction management clients from fifteen years ago. Smartphones, 3G, and project management software were all new and could have massively improved field efficiency. But when he showed CEOs beautiful contract management software, they told him plainly: have you ever met a construction worker? Their crews knew how to use a fax machine and were going to keep using fax machines. The lesson, he says, is that no matter how impressive the technology, you actually have to deploy it into real human workflows, and that reality is exactly what the AI rush ignores.

To calibrate expectations, Eli reaches for the history of the internet. Most people were introduced to it around 1995 with Windows 95, meaning the technology has had roughly thirty years to mature. It is easy to look at the seamless internet of 2026 and assume it arrived fully formed, but the internet of 1996 was janky even though it already used TCP IPv4 and HTTP. He recalls a friend who worked on a team around 1996 or 1997 that was literally trying to figure out how to process credit card transactions online, something now trivially easy but genuinely hard problem thirty years down the pike.

His sharpest analogy is the browser wars. Back when Netscape Navigator, Internet Explorer, and Firefox coexisted, the same HTML tags could render differently across browsers, and some tags only worked in certain browsers at all. Developers had to guess which browser their users would use and code specifically for it, which is why sites once carried badges like optimized for Internet Explorer. That, he says, is exactly the maturity level of the current AI stack: we are in the era of different browsers rendering tags differently, and yet the technology is being forced on everyone regardless.

He closes by turning to his audience with questions. Is joining these 6,000 forward deployed engineers an amazing career stepping stone, or a warning sign of 6,000 layoffs coming in about six months? He is candid that if he were one of them, he would probably have his resume out. He invites comments and points listeners to Spotify, Apple Podcasts, and his LinkedIn.