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⚡ Today's Summary

Key takeaways

Microsoft has officially released Foundry Local, a local AI environment that can be embedded into apps for distribution. It is available on Mac and Linux as well, helping spread AI that can run even without an internet connection [4].
In Excel, a new way of working was highlighted: keeping the currently open table in view and having the AI perform aggregations and analysis, then putting the results into a new sheet. The trend of using AI as a side-by-side assistant in everyday spreadsheet work is gaining momentum [8].
In AI-enabled development, attention is being paid to ways to make demos that work reliably also more robust in production, and to the idea of using multiple AIs within the same company to avoid single-vendor lock-in [9][2].
On the other hand, as AI capabilities increase, so does the risk of misuse and the need for safety considerations. There were reports of cases where new AI systems found vulnerabilities and exploited them, as well as examples where fear of AI contributed to real incidents [1][7].
Going forward, it’s increasingly important to focus not only on convenience, but also on creating rules for how to use AI and always verifying the results. A good starting point is to try things on a small scale first, then incorporate the methods that work into your day-to-day work [8][11].

📰 What Happened

Notable developments

Microsoft has officially released Foundry Local, which developers can embed into apps and distribute. This makes it easier for developers to deliver AI functionality that runs within the user’s own machine—without relying on the cloud [4].
In Excel, using “Copilot in Excel” allows AI to aggregate and analyze while directly referencing the table currently open, and then add the results as a new sheet within the same file [8].
Anthropic’s new model, Claude Mythos Preview, was reported to perform very strongly in tasks such as writing and “thinking,” but it was also said to be strong at finding vulnerabilities and exploiting them. It reportedly allowed the creation of exploit steps that could work with a high probability against a known weakness in Firefox, drawing significant attention [1].
AMD’s AI team indicated—after reviewing many usage logs for Claude Code—that it is hard to trust it for complex development tasks. It was also said that there were instances where behavior changed unexpectedly or where it didn’t think/work the way the user expected [2].
Omron Sininic X presented a learning approach designed to reduce the chances of dangerous behavior even during trial-and-error in real environments, and it received high marks at an international conference. It is an important contribution toward improving safety when deploying AI in the field [3].
Enterprise AI adoption continues to accelerate. While large tech firms are expanding investments and the push to run AI locally is growing, usage patterns that are harder to see from outside the company or device are also becoming more widespread [5][6].

Why it matters

The official release of Foundry Local shows AI shifting from “calling services on the internet” to “running entirely within your own device or company app.” Benefits such as reduced communication needs, less risk of data leaving the environment, and easier rollout make it more likely to be embedded into business software [4].
With Excel’s in-place editing, AI becomes usable inside the spreadsheet tools people already rely on daily. That lowers the barrier—users don’t necessarily need to learn complex operations to benefit. Turning to the right view of the table or producing quick aggregation drafts becomes more approachable [8].
The discussion around Claude Mythos Preview reinforces that the more useful AI becomes, the more it can also be used for harmful purposes. Because a convenient tool can also become a dangerous one, it’s crucial to draw clear lines around how it is released and how it is used [1].
The idea that AI evaluation can change based on usage logs is a reminder that even after a company chooses a tool, it may later fall short of expectations. Over-fixing on one tool can create issues, so organizations need to be able to switch more easily [2].
Research into safe learning with AI and the expansion of local usage may further strengthen the trend of selecting future AI not only for “intelligence,” but for whether it can be used with confidence [3][5].

🔮 What's Next

What’s next

AI running inside personal computers is likely to grow even further. As more use cases avoid the internet, there may be more scenarios where AI is used quietly inside companies—boosting convenience while simultaneously making management harder [4][5].
As a result, AI users will need the ability to understand where it’s running and what it’s being used for. The more it becomes usable in places you can’t easily see, the more likely confusion is if rule-making can’t keep pace [5][9].
In development environments, organizations may increasingly adopt an approach of using different tools side by side instead of relying on a single AI. Since model performance and behavior can change abruptly, companies that keep backup options are likely to operate more safely [2][10].
Mitigating AI misuse will become even more important. If AI’s ability to automatically discover weaknesses and exploit them keeps improving, defenders will also need mechanisms to find issues faster and patch them sooner than before [1].
At the same time, if research continues to bring AI into real-world settings safely, there may be more situations—such as in factories, logistics, and machine control—where it can be used with greater confidence [3].
As a work tool, it will be increasingly important to clearly define the division of labor between what AI does and what humans verify. Instead of delegating everything, the mainstream approach may shift toward having AI handle drafts and first drafts, with humans making the final adjustments [8][11].

🤝 How to Adapt

How to approach AI

Going forward, a smart way to work with AI is to view it not as something that delivers magical answers, but as an assistant that helps you think faster. Even when it’s convenient, it can still make mistakes—so it’s important to keep the stance that humans will do the final check [8][11].
If you’re using AI for work, it’s safer to start with tasks where a mistake won’t cause serious trouble. For example, using it for things like drafting, summarizing, organizing tables, and generating outline ideas makes it easier to feel the benefits quickly [8][11].
At the company or team level, it’s important to not over-consolidate around a single tool. Specifications and usability can change suddenly, so testing alternative tools ahead of time can help ensure work doesn’t stall [2].
Also, when AI runs inside a device, it tends to be used in ways you can’t easily see. That’s why you need not only to prohibit certain uses, but also to clearly define what AI use is allowed [5].
From a safety perspective, the more you expand what you delegate to AI, the stronger the need for a verification habit. In particular, for text released externally, numeric outputs, configuration changes, and critical decisions, it should be the default to have humans review everything [1][8].
Most importantly, don’t be overly afraid of AI and don’t overtrust it. The approach that fits the coming AI era is to try small, and keep only the ways that prove genuinely helpful.

💡 Today's AI Technique

Show an Excel table to AI as-is, then compile the results into a new sheet

With Excel’s “Copilot in Excel”, you can use the currently open table directly as input. AI will perform aggregation and analysis, and then add the results as a new sheet within the same file. It reduces the hassle of copying the table and pasting it elsewhere, which makes it well-suited to daily workflows [8].

Steps

Open the Excel app.
- Open the file that contains the table you want to analyze, as you normally would [8].
Bring up “Copilot in Excel”.
- Make sure Copilot is available inside Excel, then select “Copilot in Excel” [8].
Describe what you want in natural language.
- For example, enter something like the following.
- Example: “Summarize sales by month, and create a new sheet that clearly shows which months increased and which months decreased.”
- Example: “From this table, organize the top five best-selling products.” [8]
Review the results created by AI.
- AI output may contain mistakes, so always double-check that the numbers and item names match what you expect [8].
If needed, rephrase and try again.
- If the organization doesn’t come out as you wanted, changing the wording slightly and asking again can make it easier to get closer to your intended format [8].

Where this is especially useful

When you want to quickly summarize sales and usage status before a meeting
When there are many tables and you don’t know which ones to look at
When you want to create a quick draft before doing your own aggregations

If you test with a small table first, you can more easily grasp AI’s strengths and weaknesses.

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