From international AI responsibility to new intelligent spreadsheet tools - here are 5 things our AI Business Engineers are excited about this week!
Signal Worth Noticing
At the Axios House event at Davos 2026, leaders including Hewlett Packard Enterprise CEO Antonio Neri and OpenAI’s Chief Global Affairs Officer Chris Lehane focused less on flashy product announcements and more on the strategic implications of AI governance and organizational responsibility.
The discussion underscored that the focus on AI is now fundamentally about how it is managed and controlled. Neri highlighted that companies must actively “take control of their destiny” in a world where geopolitical tensions, cybersecurity risks, and societal expectations are rapidly reshaping what it means to implement AI responsibly. The broader dialogue emphasized that AI governance must move from the margins into the core business strategy if firms hope to scale AI sustainably.
AIBES Tech of the Week
Spreadsheets are where a ton of critical business logic quietly lives: multi-tab models, cross-sheet references, nested formulas, assumptions, and output forecasts. The problem is… that logic is often “tribal knowledge,” and when it’s time to turn the model into product (database tables, APIs, UI), engineers end up reverse-engineering finance’s masterpiece.
This week’s favorite workflow: using Claude in Excel as a “translator” between financial craftsmanship and engineering implementation -- so a 10+ page forecasting workbook can be quickly decomposed into:
the true inputs (assumptions + levers),
the calculation chain (what depends on what), and
the outputs that matter (what the product actually needs to store, compute, and display).
Claude in Excel helps AIBES bridge expertise gaps across roles. Could you be using AI for this in your business?
Framework We're Using
AI is fantastic at UI vibecoding and fast iteration. But robust data architecture is a different sport: it’s long-horizon, constraint-heavy, and loaded with domain nuance (definitions, edge cases, lifecycle, auditing, ownership, and “what happens when this changes down the road?”).
Both research and experience are increasingly pointing to the same conclusion: for schema creation/induction, human-in-the-loop workflows materially improve quality versus “fully automated” approaches (IBMTechXchange).
AIBES views this like an F1 pitstop: AI helps the pit crew fuel the car fast -- but the tire change is the architect: the part that determines whether you actually finish the race. (Fast refuels don’t matter if the wheels fall off.) We're grateful for our architects when the rubber hits the road.
Trending News
OpenAI says it will pay for energy-related infrastructure upgrades tied to its Stargate data centers, aiming to avoid passing costs to local communities.
Nvidia invested $150M in inference startup Baseten (as part of a larger round), a sign that the market’s center of gravity is shifting from training to large-scale deployment/inference.
Google’s Gemini added free, full-length SAT practice exams (announced at the Bett 2026 tech conference in London), pushing AI further into “structured learning workflows,” not just Q&A chatbots.
Quote we're pondering:
“Machines are more than clever assistants; they can be collaborators.”
- Margaret A. Boden, British cognitive scientist, philosopher, and AI researcher