The AI IPO tsunami on the stock market has only recently gotten under way, with SpaceX’s more-than-$2 trillion IPO likely to be followed in several months by OpenAI’s and Anthropic’s IPOs — each of which is likely to hit $1 trillion.

That will mint three new trillion-dollar AI companies in a matter of months, all of which compete with Microsoft. 

Wall Street has never seen anything like it. Previously, the most money raised by all IPOs in a single year was $671 billion in 2021. It took 38,644 deals to get to that figure. Compare that to three deals this year that by themselves will likely total $4 trillion.

The numbers are eye-popping. 

For Microsoft though, it’s not the numbers themselves that are important. It’s what will happen to the company once it as three newly minted trillion-dollar AI competitors. Until recently, when it came to AI, Microsoft was king of the hill. But can it keep that place?

Microsoft’s weakened position

The IPOs come at a particularly fraught time for Microsoft. At one point, it was the most valuable AI company in the world, with a big head start on the tech industry.

No longer. Microsoft’s stock price has tanked in the past 12 months, even as the S&P index has soared. In the last year, Microsoft’s stock price has dropped 24%, while the S&P has jumped 24%. The two were pretty much in sync until last fall..

Microsoft has fared even worse against its most powerful AI competitor, Google. In June 2025, Google’s total value was $2.1 trillion, well behind Microsoft’s  $3.57 trillion. By  mid-June this year Google was worth $4.5 trillion, Microsoft, $2.8 trillion. That’s entirely due to Google’s AI push and Microsoft’s failure to improve Copilot significantly.

Matt Vellosso, who worked for Microsoft for 14 years — including four as technical advisor to CEO Satya Nadella and then as Partner Director for fostering AI innovation in Windows before leaving in 2023 — is scathing about what he views as Microsoft’s AI failures. 

He warned: “Microsoft missed the internet wave, the mobile wave and now it missed the AI wave.”

Show me the money

The most immediate likely impact on Microsoft after the three massive IPOs take place will be on the company’s stock price, which could well take another hit. Investors don’t want to miss out on the AI boom, and until now, there’s been a relatively small number of companies in which they could. When Anthropic and OpenAI join SpaceX as publicly traded companies, investors will have three more choices than they did only a few months ago. That could make it tougher for Microsoft to attract AI-focused investors. And that, in turn, could knock down its stock price. 

A lower stock price means the company will have a harder time raising money when it needs it. In addition, Microsoft won’t be able to as easily buy other companies in stock-only deals, because the value of its stock will be less.

Still, the IPOs are not all bad news for Microsoft’s stock position. It does, after all, own 27% of OpenAI. So if OpenAI is valued at $1 trillion after its IPO, Microsoft has a $270 billion stake in it.

Thumbs up for increased Azure revenue

One area where these mega IPOs should be unalloyed good news for Microsoft is in Azure revenue, which should significantly. Microsoft’s final divorce settlement with OpenAI requires the latter to buy $250 billion in Azure services through 2030. And its  estimated trillion-dollar valuation ensures the company will be around for the long term, meaning Microsoft can count on that revenue — and possibly more on an ongoing basis.

Microsoft will likely also get tens of billions of dollars from Anthropic for Azure cloud services. Anthropic might spend $43 billion annually on Azure cloud services by 2030, according to estimates by HSBC, one of the world’s largest banking and financial services companies.

Thumbs down for the effect on Copilot

Although Azure use will boom thanks to the IPOs, Microsoft’s own Copilot could face significant competition at a time when it’s having serious problems gaining traction. In its April 2026 earnings call, the company said Microsoft 365 Copilot had 20 million paid seats. That’s up from 15 million paid seats as of January, but still a minuscule number, considering Microsoft has 450 million Microsoft 365 commercial subscribers. That means only about 4% of the company’s business customers have been willing to pay for it since its launch in late 2023.

Beyond that, developers have been leaving GitHub Copilot and turning to Anthropic’s Claude Code and SpaceX’s recently purchased Cursor. A survey by the software development company JetBrains of more than 10,000 developers found that 29% used GitHub Copilot, 18% used Cursor and 18% used Claude Code. 

Early in 2025, GitHub Copilot had a commanding 67% market share.

More downside than up from the IPOs

Although the IPOs do have some upside for Microsoft — dramatically increased Azure revenue and several hundred billion dollars in OpenAI stock holdings — mostly, they’re bad news thanks to increased competition. 

Unless Microsoft significantly improves Copilot for businesses, developers and consumers, the company’s one-time AI dominance will erode even further.

AI has created a tough job environment for entry-level workers and things aren’t getting better anytime soon — even those with AI capabilities now need “senior-level” skills to land a job.

“AI-exposed entry-level roles are seven times more likely to require traditionally senior-level skills such as judgement and leadership,” consulting firm PwC said in a study released this month.

That’s because AI is changing the traditional career ladder. Companies are increasingly looking for candidates that use the cutting-edge tools and services to amplify their performance and grow faster. “Organizations must rethink how they mentor and train junior staff, helping them step up to complex decision-making much earlier in their careers,” PwC said.

Entry-level job seekers with or without AI skills are already dealing with stagnant wages,  layoffs, and stalled hiring. 

(The PwC findings echo similar concerns raised late last year in McKinsey’s State of AI report. Many companies are reducing headcount by deploying AI agents to take over entry-level jobs.)

Early-career AI job postings “have flatlined in highly AI exposed sectors,” and listings for junior roles with mid-career or senior-level skills have grown 35% since 2019, PwC said.  The consulting firm largely discounted the notion that AI is taking jobs away, though other studies point in the opposite direction. 

By the end of May, AI-driven job cuts had reached 87,174 for 2026, already outpacing the total of around 54,836 in 2025, according to figures released by Challenger, Gray and Christmas earlier this month.

The AI-driven layoffs haven’t reached the “jobpocalypse” stage yet, and workers are more productive with it, said Andy Challenger, chief revenue officer at Challenger, Gray and Christmas. But companies are rethinking hiring and long-term operational strategies as AI becomes a routine component in daily workflows and processes, he said.

Businesses are “restructuring aggressively as they reposition for an AI-driven economy,” he said.

That’s putting downward pressure on entry-level hiring as AI tools absorb more routine work, said Kye Mitchell, head of Experis US, a part of ManpowerGroup. “That doesn’t remove opportunity, but it changes the expectations. Employers now expect candidates to come in with hands-on experience, AI familiarity, and the ability to contribute faster,” Mitchell said.

Compensation remains strong for specialized, in-demand skills, while more commoditized roles such as customer service, helpdesk, and some entry-level positions  are flattening. “The shift overall is toward skills-based hiring, where demonstrable capability matters more than credentials alone,” Mitchell said.

Graduates who combine technical fundamentals with practical experience, AI fluency and strong communication skills stand out quickly. Job candidates can’t rely solely on academic credentials. 

“Employers are moving away from ‘train-from-scratch’ hiring and looking for talent that can contribute earlier and continue to adapt,” Mitchell said.

The PwC study also focused on the productivity gap between companies that have invested heavily in AI and companies lagging in adoption.

Since ChatGPT showed up in 2022, AI-exposed companies have seen productivity gains of 40% versus other companies. “The companies achieving the biggest productivity gains from AI are not using it only to cut costs,” PwC said.

AI-forward firms are also raising headcounts and wages. “Far from being a job killer, AI may actually be a job expander when used to unlock growth and enter new markets,” PwC said. 

Workers who use their domain expertise to supplement AI tools can advance, with AI-exposed roles “2.5 times more likely to rely on skills like empathy, judgement, and creativity that become even more valuable as AI absorbs some routine work,” PwC said.

Microsoft has been pushing hard to make Visual Studio Code a major way to consume its AI services, mostly in the form of GitHub Copilot. GitHub Copilot’s deep integration with VS Code brings many conveniences — inline autocomplete, for instance — but it’s frustrating for those, like me, who would rather use another model provider, or even a locally hosted LLM, for those functions.

Visual Studio Code 1.122 introduced a new feature, “Use BYOK [Bring Your Own Key] without a GitHub sign-in,” that allows you to “use chat, tools, and MCP servers in air-gapped or restricted environments where GitHub sign-in isn’t possible.” More importantly, it “enables fully offline workflows with local models like Ollama.”

In other words, you can now use locally hosted LLMs for chat, tools, and Model Context Protocol servers inside Visual Studio Code. The one thing you still can’t do is use a local LLM for inline and next-edit suggestions — at least, not without additional tooling.

Choosing a model for BYOK mode

If you want to use a local LLM with VS Code’s bring-your-own-model system, the first thing you need is a way to host the model. VS Code lacks a model-hosting mechanism of its own, although it’s conceivable that a VS Code extension may offer something like that in the future. That said, hosting models is complicated enough that a dedicated app is really needed for the job.

One easy way to host models is via a product like LM Studio, a convenient GUI for standing up, serving, and managing LLMs on one’s own hardware. The model host does not have to be the same system you run VS Code on, either. It can be on a server box you control, or on a cloud instance.

The choice of model is also important. Many models are powerful but won’t run well on commodity hardware because they’re simply too big. A good rule of thumb is to choose a model that fits into existing VRAM, along with the memory needed for a sizable token context (the more, the better). Also, the model should be suited to coding and development work. Some models in this vein that fit comfortably into 8GB VRAM include:

• Gemma4 (effective 2 billion parameters version)
• Qwen3.5 9B
• Codestral 22B v.0.1 (proprietary license)

Setting up BYOK mode in VS Code

Once you have a model up and running, you can integrate it with Visual Studio Code. If you’ve disabled VS Code’s AI features, you will need to turn them on. Make sure the setting chat.disableAIFeatures is turned off. You can find it in Settings | Chat | Miscellaneous.

Third-party language models are managed through Visual Studio Code’s language model list. Press Ctrl-Shift-P and type Manage Language Models to open the list of existing language models.

Foundry

First you will see a list of the built-in models, which are all externally hosted. To add a new model, select Add Models at the top right and select Custom Endpoint.

You’ll then get a series of prompts:

• Group Name: This is “Custom Endpoint” by default, but you can choose any name you want. The name is strictly for organizing the model list and doesn’t affect things like model recognition or connectivity.
• API Key: If you’ve configured LM Studio to use an API key for serving models, provide it here. If you’re hosting the model locally and you haven’t explicitly set up API keys, you can leave this blank.
• API Type: The options here are Chat Completions, Responses, and Messages. Most of the time you’ll want to use Responses, as it’s the most general-purpose option of the three.

Once you finish providing those answers, you’ll be dropped into a modal editor for a JSON file that holds the details about the endpoint you’re configuring.

Foundry

You’ll need to provide a few more details by typing them into the labeled fields:

• id: A text field that uniquely identifies this particular entry. The choice of ID is pretty much arbitrary; if you’re using only a single model, the ID could be the model name.
• name: The name of the model that is used to identify it on the model server. In LM Studio, you can get this name by clicking on My Models in the main interface, then selecting the three-dot icon for the model in question and clicking Copy Default Identifier. For Qwen 2.5, for instance, name might be something like qwen2.5-coder-7b-instruct.
• url: The URL to the server’s endpoint. On LM Studio, this defaults to something like http://127.0.0.1:1234/v1. The /v1 at the end is important because that endpoint is used for autodiscovery of models and their capabilities.

The other fields generally don’t need editing. Most models have tool calling functionality. If you know for a fact that the model you’re using doesn’t have vision support, then set vision to false.

Once you have these fields filled in, you can close the modal editor to save the changes. If you reload the Manage Language Models page, you’ll now see your new endpoint:

Foundry

You should now be able to launch the chat window and use the defined model for conversation and utilities:

Foundry

One current, and major, limitation of Visual Studio Code’s BYOK functionality is that it only works for chat and utility tasks. It doesn’t allow you to use a local model for inline suggestions or code completions. The only way to take advantage of local models for expanded functionality with VS Code is to use a third-party tool like Continue.

It isn’t clear if Microsoft will eventually lift this restriction. GitHub Copilot integration in VS Code is a large part of how Copilot as a service reaches its target audience. For the time being, you can certainly use third-party and local models for a significant part of your AI-assisted development work in VS Code, and you can close the functionality gap with additional tooling.

Unless you’ve been living under an old woodpile in your backyard, you have certainly seen how agentic coding is rocking the software development world. Things are happening fast and furious, and keeping up is practically a full-time job. 

The latest area that is catching the attention of developers is how agentic coding is affecting the open source community. The open source movement has been defending the rights of folks to use, change, and contribute to software for many years. And of course, agentic coding is starting to become part of that process.

On the one hand, maintainers of open source projects rightfully are frustrated as they become overwhelmed with pull requests of dubious quality and usefulness being submitted by coding agents. On the other hand, as David Heinemeier Hansson notes, maintainers are starting to get a little snooty about accepting AI-written code, viewing it as somehow not worthy of being included. Some organizations have explicitly banned AI-generated submissions.

I get that they don’t want AI slop overwhelming their input queues. But I think it is a huge mistake to ban AI-written code outright.

Whose code?

Before I dig deeper into that notion, it’s important to look at another issue that arises from all of this: Who actually owns the code that AI writes? 

Copyright requires that a human produce the thing being copyrighted. If you prompt Claude Code with “Write me a CMS system” and then Claude writes you a CMS system that you check into a public GitHub repository unchanged, it’s not quite clear if that code is protected by copyright. However, if you prompt Claude Code with a specification and guidelines and then you work with Claude to refine the initial result, reviewing the code and making changes as part of an iterative process, then it could be argued that a human did produce that code. But it is not at all clear-cut legally. (Please note that I am not a lawyer.)

The current thinking is that the result of accepting verbatim the output of a simple prompt is not copyrightable, and that no one actually owns the code — an interesting notion in and of itself. 

But then the ethical question comes into play. If I find a bug in an open source project, I ask GitHub Copilot to fix it, and Copilot writes a clever and effective fix, then who cares who owns the code? Should a maintainer of the project reject such a pull request just because it was AI-generated? That seems silly to me, yet it is happening today. 

Our code

There is, too, the issue of license compliance for AI-generated code. As a general rule, LLMs generate code rather than copying it. They don’t copy and paste code directly from repositories. However, there have been cases where AI-produced code has resembled open source code so closely that the claim could be made that it is a copy. If this happens with GPL code, it could be a violation of the license to use it without the receiving code base being “infected.” Open source maintainers naturally should be concerned about this happening.

In the end, an open source maintainer should care about the quality and license compliance of submissions, not how those submissions were derived. Gatekeeping based on the source of code doesn’t seem like a good path towards project success. Good code is good code, no matter where it comes from.

Agentic coding is here, and the open source community needs to realize — and embrace — that inevitability.