Microsoft is working on a new "Tenant Copilot" offering, according to an internal memo.
The company is also developing news ways for customers to manage AI agents alongside human staff.
Microsoft at the time was planning to announce the developments at next week's Build.
Microsoft is working on a new Copilot and could unveil it at the company's Build conference next week, according to an internal memo viewed by Business Insider.
The software giant also has grand "Agent Factory" ambitions, and is developing new ways for corporate customers to manage AI agents alongside human employees, the memo shows.
The Tenant Copilot project is run by the organization behind the Microsoft 365 business. This new Copilot is designed to "rapidly channel an organization's knowledge into a Copilot that can 'talk,' 'think,' and 'work' like the tenant itself," according to an April 14 email sent by Microsoft executive Jay Parikh.
A "tenant" is the term used to describe corporate users of the Microsoft 365 suite of business applications. A Copilot that has access to these tenants would essentially be able to access customer information stored within their Microsoft 365 accounts.
Parikh explained in the email that Microsoft is using different AI techniques to power the Tenant Copilot feature. Supervised fine-tuning helps "to capture a tenant's voice." The tool will also tap into OpenAI's o3 reasoning model "to shape its thought process." Lastly, "agentic" fine-tuning will "empower real-world tasks," he wrote.
Microsoft at the time planned to offer a public preview of Tenant Copilot at Build, according to the memo. The company sometimes changes what it plans to announce at the conference.
Meanwhile, the CoreAI Applied Engineering team is also "working to launch a collaborative go-to-market plan for top-tier customers to drive successful adoption of our Al cloud," Parikh added in the memo.
Microsoft declined to comment.
Parikh's 'Agent Factory' concept
Parikh is the former head of engineering at Facebook. Microsoft CEO Satya Nadella hired Parikh in October and tapped him in January to run a new group called CoreAI Platform and Tools focused on building AI tools. The group combined Microsoft's developer division and AI platform team and is responsible for GitHub Copilot, Microsoft's AI-powered coding assistant.
This year's Build event will be Parikh's first at the helm of this new organization. In the email to the nearly 10,000 employees in the organization, Parikh discussed a new "Agent Factory" concept. That's likely a nod to cofounder Bill Gates, who talked about Microsoft being a "software factory."
"Building our vision demands this type of culture β one where Al is embedded in how we think, design, and deliver," Parikh wrote. "The Agent Factory reflects this shift β not just in what we build, but in how we build it together. If we want every developer (and everyone) to shape the future, we have to get there first."
Parikh has been trying to work across organizations to collaborate on AI agents, through a "new type of cross-product review" combining teams such as security services like Entra and Intune with "high-ambition agent efforts" within LinkedIn, Dynamics, and Microsoft 365.
Meet your new AI agent co-worker
Part of this effort focuses on how to manage AI agents alongside human employees.
Microsoft, for example, has been working on how to handle identity management for AI agents, according to the memo. This technology usually controls security access for human users. Now, the company is trying to spin up a similar system for AI agents.
"Our hypothesis is that all agent identities will reside in Entra," Parikh wrote, although "not every agent will require an identity (some simpler agents in M365 or Studio, for instance, don't need one)."
Microsoft is taking a similar approach to M365 Admin Center, which is used by IT administrators to manage employee access to applications, data, devices, and users. Future versions of this system will accommodate AI agents as "digital teammates" of human workers, according to Parikh's memo.
Microsoft's Copilot Analytics service is also expanding into broader workforce analytics to give corporate customers a view of how work gets done both by humans and AI agents.
And Parikh aims to make Azure AI Foundry, its generative AI development hub, "the single platform for the agentic applications that you build," he wrote. "At Build, we will have the early versions of this, and we'll iterate quickly to tackle a variety of customer use cases."
Microsoft is releasing a pair of new Surface devices today, both models that undercut last year's Surface Laptop and Surface Pro on price but also take a pretty big step down in specs. One of the devices is a new 12-inch Surface Pro tablet, which we've covered in more detail here. The other is a new 13-inch Surface Laptop, whose specs and price straddle the narrow gap between the current seventh-generation Surface Laptop and the original price of the aging Surface Laptop Go 3.
The new Surface Laptop starts at $899, and preorders open today. It will be available on May 20.
The new laptop shares many specs in common with last yearβs entry-level seventh-generation Surface Laptop, including an Arm-based Qualcomm Snapdragon X Plus processor, 16GB of RAM, and support for Windows 11βs expanded Copilot+ capabilities. Itβs also smaller and lighter than the 13.8-inch Surface Laptop. But the CPU has eight cores instead of 10 or 12, the screen is smaller and lower resolution, and youβre more limited in your upgrade options; weβve outlined the key differences in the table below.
Microsoft is trying to simplify AI sales, according to slides from an internal presentation.
The current approach slowed sales, confused customers, and affected cost and quality, insiders say.
Microsoft plans to slash the number of "solution areas."
Microsoft is trying to simplify its many AI offerings by streamlining how the products are pitched to customers, according to internal slides from a recent presentation.
The software giant has a bunch of different AI tools called Copilot. There's Copilot for its Teams chat app, Copilot for its PowerPoint presentation tool, Copilot for its Outlook email service β just to name a few.
These products are often split into different "solution areas," as Microsoft calls them. Having Copilot tools in many different buckets can slow down sales, confuse customers, and affect cost and quality of the tools, people in the organization told Business Insider. They asked not to be identified discussing private matters.
Microsoft has sales teams focused on each solution area, which will now be consolidated.
Microsoft Chief Commercial Officer Judson Althoff this week unveiled plans for addressing these issues in the company's upcoming fiscal year, which begins in July. BI obtained copies of slides from his presentation.
According to one of the slides, three major changes include:
Consolidate Microsoft's solution areas.
Accelerate regional skills at scale.
Align teams working with small, medium, and corporate customers with those working with outside channel partners who market and sell Microsoft products.
The organization currently has six solutions areas: Modern work, Business Applications, Digital & App Innovation, Data & AI, Azure Infrastructure, and Security.
Beginning in July, these areas will be combined into three: AI Business Solutions, Cloud & AI Platforms, and Security.
AI Business Solutions will include tools such as Copilot for Microsoft 365, Copilot for Teams, Copilot for Outlook, plus a data visualization product called Power BI, according to a person who attended a Thursday all-hands for Althoff's organization. This person asked not to be identified discussing private matters.
"We are evolving the commercial solution areas within our sales organization to better reflect the era of AI and support the growth of our customers and partners," a Microsoft spokesperson said in a statement. "This evolution reflects the shift in how customers and partners are buying and will better serve their needs."
The other changes include expanding training for salespeople and a reorganization to Small, Medium Enterprise & Channel (SME&C) team, which was announced internally earlier this year.
The changes come as Microsoft is trying to figured out how to make money from its significant AI investments. It has mulled changes including new software bundles with Copilot. The company earlier this year said it plans to spend $80 billion on expanding its network of AI data centers.
Nearly a year after announcing the feature, Microsoft is finally ready to roll the controversial Windows Recall feature out to the general public, the company announced today on its Windows Experience Blog.
Only available on Copilot+ PCs, a subset of Windows 11 systems sold within the last year or so, Recall takes continuous screenshots of everything you do on your PC, saving them, scraping text from them, and saving it all in a searchable database. This obviously has major security and privacy implicationsβanyone who can get access to your Recall database can see nearly everything you've done on your PCβwhich is why Microsoft's initial rollout attempt was such a mess.
Recall's long road to release involved a rushed initial almost-launch, harsh criticism of its (then mostly nonexistent) security protections, multipledelays, a major under-the-hood overhaul, and five months of testing in Microsoft's Windows Insider beta program. Microsoft signaled that Recall was nearly ready for release two weeks ago when it came to the near-final Release Preview channel.
The new hire had a simple task. All they had to do was assign people to work on a new web development project based on the client's budget and the team's availability. But the staffer soon ran into an unexpected problem: They couldn't dismiss an innocuous pop-up blocking files that contained relevant information.
"Could you help me access the files directly?" they texted Chen Xinyi, the firm's human resources manager. Ignoring the obvious "X" button in the pop-up's top right corner, Xinyi offered to connect them with IT support.
"IT should be in touch with you shortly to resolve these access issues," Xinyi texted back. But they never contacted IT, and the new hire never followed up. The task was left uncompleted.
Fortunately, none of these employees are real. They were part of a virtual simulation designed to test how AI agents fare in real-world professional scenarios. Set up by a group of Carnegie Mellon University researchers, the simulation mimicked the trappings of a small software company with internal websites, a Slack-like chat program, an employee handbook, and designated bots β an HR manager and chief technology officer β to contact for help. Inside the fake company called TheAgentCompany, an autonomous agent can browse the web, write code, organize information in spreadsheets, and communicate with coworkers.
Agents have emerged as the next major frontier of generative AI as Google, Amazon, OpenAI, and every other major tech company race to build them. Instead of executing one-off instructions like a chatbot would, agents can independently act on a person's behalf, make decisions on the go, and perform in unfamiliar environments with little to no intervention. If ChatGPT can suggest a few vacuum cleaners to buy, its agentic counterpart theoretically could pick one and buy it for you.
Naturally, the promise of AI agents has captivated CEOs. In a Deloitte survey of over 2,500 C-suite leaders, more than one-quarter of respondents said their organizations were exploring autonomous agents to a "large or very large extent." Earlier this year, Salesforce's chief said today's CEOs will lead the last all-human workforces. Nvidia's cofounder and CEO Jensen Huang predicted every company's IT department will soon "be the HR department of AI agents." OpenAI's Sam Altman has said that this year, AI agents will "join the workforce." But it's still unclear how well these agents can accomplish the tasks a company might need them to.
To test this out, the Carnegie Mellon researchers instructed artificial intelligence models from Google, OpenAI, Anthropic, and Meta to complete tasks a real employee might carry out in fields such as finance, administration, and software engineering. In one, the AI had to navigate through several files to analyze a coffee shop chain's databases. In another, it was asked to collect feedback on a 36-year-old engineer and write a performance review. Some tasks challenged the models' visual capabilities: One required the models to watch video tours of prospective new office spaces and pick the one with the best health facilities.
The results weren't great: The top-performing model, Anthropic's Claude 3.5 Sonnet, finished a little less than one-quarter of all tasks. The rest, including Google's Gemini 2.0 Flash and the one that powers ChatGPT, completed about 10% of the assignments. There wasn't a single category in which the AI agents accomplished the majority of the tasks, says Graham Neubig, a computer science professor at CMU and one of the study's authors. The findings, along with other emerging research about AI agents, complicate the idea that an AI agent workforce is just around the corner β there's a lot of work they simply aren't good at. But the research does offer a glimpse into the specific ways AI agents could revolutionize the workplace.
Two years ago, OpenAI released a widely discussed study that said professions like financial analysts, administrators, and researchers are most likely to be replaced by AI. But the study based its conclusions on what humans and large language models said were likely to be automated β without measuring whether LLM agents could actually do those jobs. The Carnegie Mellon team wanted to fill that gap with a benchmark linked directly to real-world utility.
In many scenarios, the AI agents in the study started well, but as tasks became more complex, they ran into issues due to their lack of common sense, social skills, or technical abilities. For example, when prompted to paste its responses to questions in "answer.docx," the AI treated it as a plain text file and couldn't add its answers to the document. Agents also routinely misinterpreted conversations with colleagues or wouldn't follow up on key directions, prematurely marking the task complete.
It's relatively easy to teach them to be nice conversational partners; it's harder to teach them to do everything a human employee can.
Other studies have similarly concluded that AI cannot keep up with multilayered jobs: One found that AI cannot yet flexibly navigate changing environments, and another found agents struggle to perform at human levels when overwhelmed by tools and instructions.
"While agents may be used to accelerate some portion of the tasks that human workers are doing, they are likely not a replacement for all tasks at the moment," Neubig says.
The Carnegie Mellon study was far from a perfect simulation of how agents would work in the wild. Most proponents of agents envision them working in tandem with a human who could help course-correct if the AI ran into an obvious roadblock. The generation of agents that was studied is also not that skilled at carrying out humanlike tasks such as browsing the web. Newer tools, like OpenAI's Operator, will likely be more adept at these tasks.
Despite these limitations, the research offers something valuable: It points to what's coming next.
Stephen Casper, an AI researcher who was part of the MIT team that developed the first public database of deployed agentic systems, says agents are "ridiculously overhyped in their capabilities." He says the main reason AI agents struggle to accomplish real-world tasks reliably is that "it is challenging to train them to do so." Most state-of-the-art AI systems are decent chatbots because it's relatively easy to teach them to be nice conversational partners; it's harder to teach them to do everything a human employee can.
In TheAgentCompany, AI succeeded the most in software development tasks, even though those are more difficult for humans. The researchers hypothesize this is because there's an abundance of publicly available training data for programming jobs, while workflows for admin and financial tasks are typically kept private within companies. There just isn't great data to train an AI on.
Jeff Clune, a computer science professor at the University of British Columbia who helped build an agent for OpenAI that could use computer software like a human, thinks that training AI agents on proprietary data from day-to-day activities and workflow patterns could be the key to improving their efficacy. That's exactly what a lot of companies are starting to do.
Moody's is one of many major companies experimenting with training AI on in-house data. The 116-year-old financial services firm is automating business analysis through agentic AI systems, which draw insights from decades of research, ratings, articles, and macroeconomic information. The training is designed to emulate how a human team would analyze a business, using carefully crafted instructions broken into independent steps by people experienced in the field.
While it's too early to tell how effective Moody's approach is, its managing director of AI, Sergio Gago, says the firm is actively exploring what kinds of work β like analyzing the financials of a small business β agents could take over.
Similarly, Johnson & Johnson tells Business Insider it was able to cut production time for the chemical processes behind making new drugs by 50% with fine-tuned in-house AI agents that could automatically adjust factors like temperature and pressure. Jim Swanson, J&J's chief information officer, says the company is focused on training people to collaborate with AI agents.
The direction things are heading looks different from what most people thought a few years ago.
Johns Hopkins scientists have created an Agent Laboratory, which leverages LLMs to automate much of the research process, from literature review to report writing, with human-provided ideas and feedback at each stage. "I think it won't be long before we trust AI for autonomous discovery," Samuel Schmidgall, one of the Johns Hopkins scientists, says. Likewise, LG Electronics' research division developed an AI agent that it says can verify datasets' licenses and dependencies 45 times faster than a team of human experts and lawyers.
It's still unclear whether organizations can trust AI enough to automate their operations. In multiple studies, AI agents attempted to deceive and hack to accomplish their goals. In some tests with TheAgentCompany, when an agent was confused about the next steps, it created nonexistent shortcuts. During one task, an agent couldn't find the right person to speak with on the chat tool and decided to create a user with the same name, instead. A BI investigation from November found that Microsoft's flagship AI assistant, Copilot, faced similar struggles: Only 3% of IT leaders surveyed in October by the management consultancy Gartner said Copilot "provided significant value to their companies."
Businesses also remain concerned about being held responsible for their agents' mistakes. Plus, copyright and other intellectual property infringements could prove a legal nightmare for organizations down the road, says Thomas Davenport, an IT and management professor at Babson College and a senior advisor at Deloitte Analytics.
But the direction things are heading looks different from what most people thought a few years ago. When AI first took off, a lot of jobs seemed to be on the chopping block. Journalists, writers, and administrators were all at the top of the list. So far, though, AI agents have had a hard time navigating a maze of complex tools β something critical to any admin job. And they lack the social skills crucial to journalism or anything HR-related.
Neubig takes the translation market as a precedent. Despite machine language translation becoming so accessible and accurate β putting translators at the top of the list for job cuts β the number of people working in the industry in the US has remained rather steady. A "Planet Money" analysis of Census Bureau data found that the number of interpreters and translators grew 11% between 2020 and 2023. "Any efficiency gains resulted in increased demand, increasing the total size of the market for language services," Neubig says. He thinks that AI's impact on other sectors will follow a similar trajectory.
Even the companies seeing massive success with AI agents are, for now, keeping humans in the loop. Many, like J&J, aren't yet prepared to look past AI's risks and are focused on training staff to use it as a tool. "When used responsibly, we see AI agents as powerful complements to our people," Swanson says.
Instead of being replaced by robots, we're all slowly turning into cyborgs.
Shubham Agarwal is a freelance technology journalist from Ahmedabad, India, whose work has appeared in Wired, The Verge, Fast Company, and more.
Generative AI is reshaping enterprise software, challenging traditional SaaS models.
AI agents are evolving from assistants to core applications, impacting mid-market companies.
Mid-size SaaS vendors face pressure from AI-native startups and tech giants like Microsoft, AlixPartners says.
A foundational shift is underway in enterprise software, and it's being driven by generative AI.
The rise of reasoning models and AI agents is beginning to erode the core assumptions that have defined the software-as-a-service business model for decades, according to a new study released on Monday by AlixPartners.
The consulting firm warned that this is squeezing more than 100 mid-market software companies, which are stuck in the midst of a powerful trend.
These companies are caught in a "big squeeze," pressured on one side by nimble, AI-native entrants that can replicate applications at a fraction of the cost and on the other side by tech behemoths, such as Microsoft and Salesforce, that are pouring billions of dollars into the AI arms race, AlixPartners said.
"We believe many mid-size enterprise software companies will face threats to their survival over the next 24 months," the firm added ominously. It declined to identify specific companies, given the sensitive nature of its findings.
AI: the new foundation, not just a feature
The most mature uses of AI in enterprise software today include copilots for software coding, such as GitHub Copilot from Microsoft, and support chatbots like Zendesk's Answer Bot. But these could be just the beginning. Generative AI is advancing from narrow use cases to the broader "logic and presentation layers" of software, the very foundation that traditional SaaS tools are built on, AlixPartners explained in the study.
This means AI agents are no longer just assistants within applications; they are becoming the applications themselves. These agents are capable of handling complex tasks, such as scheduling meetings, analyzing reports, and creating code, with little need for a graphical interface or structured workflow. And because they can run across various data types without needing extensive data normalization, they could render some traditional SaaS layers redundant.
"This shift could eliminate the need for many enterprise software companies that thrived in the traditional SaaS architecture," AlixPartners said.
A new threat from both sides
This puts mid-size SaaS vendors in a tricky position, the firm explained, citing a recent analysis it conducted of 122 publicly listed enterprise software companies with annual revenue below $10 billion.
It found that sales growth has slowed considerably lately. For instance, the percentage of high-growth companies in this group decreased from 57% in 2023 to 39% in 2024. This year, industry analysts are expecting further declines, indicating that only 27% of companies will be in the high-growth category.
AlixPartners also highlighted that software customers are moving around more than before. The median net dollar retention rate of enterprise software companies dropped from 120% in 2021 to 108% in the third quarter of 2024, the firm noted, citing data from Bank of America. (NDR is a common way to measure customer stickiness. When it's above 100%, that indicates revenue from existing customers is growing, while an NDR below 100% suggests revenue is declining from these sources.)
Many of these companies are now being undercut by AI-powered challengers with lower costs and faster iteration cycles. Simultaneously, larger players are integrating AI into their broader platforms, offering bundled functionality at lower price points through economies of scale, according to the consulting firm.
Klarna's recent decision to drop Salesforce and Workday in favor of smaller AI-powered vendors and in-house agents is a sign of where this trend may be headed, AlixPartners noted.
The SaaS model: ripe for reinvention
Traditional SaaS depends heavily on the user interface, structured data workflows, and seat-based pricing. But AI agents don't need dashboards, and they can function without rigid data hierarchies. This calls into question the relevance of the SaaS model itself, according to AlixPartners.
AlixPartners
Some companies are already pivoting. Salesforce and ServiceNow have begun experimenting with outcome-based pricing for AI agents, where fees are tied to results, not user counts.
Among those 122 mid-sized software companies, AlixPartners found that half expect significant changes to business models in the next year.
At the same time, the compute costs associated with running AI agents can be significantly higher than for classic SaaS tools, making profit margin compression a potential threat. Software providers may need to rethink infrastructure strategies, possibly shifting to more efficient inference architectures, industry experts have warned recently.
Meanwhile, higher interest rates and tightening capital markets in recent years have put the onus on SaaS profitability, not just growth. Software companies have responded by cutting costs, optimizing portfolios, and rethinking pricing strategies.
According to the AlixPartners report, more than 60% of executives are now focused on AI as a growth driver. Unlocking that growth requires more than just product tweaks, it may require transformation across operations, go-to-market models, and customer relationships, the consulting firm suggested.
How to survive: a new playbook
So what's the path forward? The report discusses several strategic imperatives. Here are a few:
Build AI agents: Not as bolt-on features, but as core products.
Transform the business model: Move beyond a seat-based fee structure to usage- or outcome-based pricing.
Streamline and focus: Shed low-growth products and reallocate R&D to AI development.
Lean into M&A: For some, the best route may be to get acquired or consolidate.
In a world where generative AI tools can write or replicate software pretty well, differentiation must come from speed, relevance, and efficiency, not UI design or legacy feature sets.
The software era isn't ending. But the SaaS era, as we know it, is evolving. The next generation of enterprise tools may not be applications, they could be agents. And only the most adaptable companies will make the leap, according to AlixPartners.
Some elements of Microsoft's Copilot assistant in Windows 11 have felt like a solution in search of a problemβand it hasn't helped that Microsoft has frequently changed Copilot's capabilities, turning it from a native Windows app into a web app and back again.
But I find myself intrigued by a new addition to Copilot Vision that Microsoft began rolling out this week to testers in its Windows Insider program. Copilot Vision launched late last year as a feature that could look at pages in the Microsoft Edge browser and answer questions based on those pages' contents. The new Vision update extends that capability to any app window, allowing you to ask Copilot not just about the contents of a document but also about the user interface of the app itself.
Microsoft's Copilot Vision update can see the contents of any app window you share with it.
Credit:
Microsoft
Provided the app works as intendedβnot a given for any software, but especially for AI featuresβCopilot Vision could replace "frantic Googling" as a way to learn how to use a new app or how to do something new or obscure in complex PC apps like Word, Excel, or Photoshop. I recently switched from Photoshop to Affinity Photo, for example, and I'm still finding myself tripped up by small differences in workflows and UI between the two apps. Copilot Vision could, in theory, ease that sort of transition.
On Monday, Microsoft reportedly terminated the roles of two software engineers, Ibtihal Aboussad and Vaniya Agrawal, who protested the companyβs reported dealings with the Israeli military during Microsoftβs Copilot and 50th anniversary event last week. According to an internal message viewed by CNBC, Microsoft wrote that Aboussad could have raised concerns βconfidentially with your manager, [β¦]
Microsoft has released a browser-based playable level of the classic video game Quake II. This functions as a tech demo for the gaming capabilities of Microsoftβs Copilot AI platform β though by the companyβs own admission, the experience isnβt quite the same as playing a well-made game. You can try it out for yourself, using [β¦]
GitHub Copilot, Microsoft-owned GitHubβs AI coding assistant, could soon become costlier for some users. On Friday, GitHub announced βpremium requestsβ for GitHub Copilot, a new system that imposes rate limits when users switch to AI models other than the base model for tasks such as βagenticβ coding and multi-file edits. While GitHub Copilot subscribers can [β¦]
For its 50th birthday, Microsoft is teaching its AI-powered Copilot chatbot a few new tricks. Copilot can now take action on βmost websites,β Microsoft says, enabling it to book tickets, reserve restaurants, and more. The bot has gained the ability to remember specific things about you, similar to OpenAIβs ChatGPT, like your favorite food and [β¦]
Microsoft's Windows updates over the last couple of years have mostly been focused on adding generative AI features to the operating system, including multiple versions of the Copilot assistant. Copilot has made it into Windows 11 (and even, to a more limited extent, the aging Windows 10) as a native app, and then a wrapper around a web app, and soon as a native app again.
But this month's Windows updates are removing the Copilot app from some Windows 11 PCs and unpinning it from the taskbar, according to this Microsoft support document. This bug obviously won't affect systems where Copilot had already been uninstalled, but it has already led to confusion among some Windows users.
Microsoft says it is "working on a resolution to address the issue" but that users who want to get Copilot back can reinstall the app from the Microsoft Store and repin it to the taskbar, the same process you use to install Copilot on PCs where it has been removed.
A new study from Columbia Journalism Review's Tow Center for Digital Journalism finds serious accuracy issues with generative AI models used for news searches. The research tested eight AI-driven search tools equipped with live search functionality and discovered that the AI models incorrectly answered more than 60 percent of queries about news sources.
Researchers Klaudia JaΕΊwiΕska and Aisvarya Chandrasekar noted in their report that roughly 1 in 4 Americans now use AI models as alternatives to traditional search engines. This raises serious concerns about reliability, given the substantial error rate uncovered in the study.
Error rates varied notably among the tested platforms. Perplexity provided incorrect information in 37 percent of the queries tested, whereas ChatGPT Search incorrectly identified 67 percent (134 out of 200) of articles queried. Grok 3 demonstrated the highest error rate, at 94 percent.
Ahead of the Game Developers Conference (GDC), Xbox revealed on Thursday that itβs experimenting with an AI-powered gaming sidekick. βCopilot for Gaming,β powered by Microsoftβs AI technology, is a voice-activated assistant designed to enhance the gaming experience and is designed to answer questions, complete tasks, and even criticize if youβre playing poorly.Β βIt can trash [β¦]
Microsoft appears to be working on 3D gaming experiences for Copilot, its AI-powered chatbot platform, according to a new job listing. The listing, published this week, seeks a senior software engineer based in Beijing specializing in 3D rendering engines, particularly engines typically used to build web browser-based video games (Babylon.js, three.js, and Unity). βAre you [β¦]
Microsoftβs Copilot AI assistant is exposing the contents of more than 20,000 private GitHub repositories from companies including Google, Intel, Huawei, PayPal, IBM, Tencent and, ironically, Microsoft.
These repositories, belonging to more than 16,000 organizations, were originally posted to GitHub as public, but were later set to private, often after the developers responsible realized they contained authentication credentials allowing unauthorized access or other types of confidential data. Even months later, however, the private pages remain available in their entirety through Copilot.
AI security firm Lasso discovered the behavior in the second half of 2024. After finding in January that Copilot continued to store private repositories and make them available, Lasso set out to measure how big the problem really was.
Microsoft finally released a macOS app for Copilot, its free generative AI chatbot.Β Similar to OpenAIβs ChatGPT and other AI chatbots, Copilot enables users to ask questions and receive responses generated by AI. Copilot is designed to assist users in numerous tasks, such as drafting emails, summarizing documents, writing cover letters, and more. Thereβs also [β¦]
GitHub has announced a slew of updates for Copilot, while also giving a glimpse into a more agentic future for its AI-powered pair programmer. Among the notable updates includes a feature called Vision for Copilot, which allows users to attach a screenshot, photo, or diagram to a chat, with Copilot generating the interface, code, and [β¦]
