Helping Technology Businesses Tell Their Growth Story With Data.

A critical vulnerability in analytics platform Metabase was actively exploited, allowing attackers to potentially gain administrator access, steal credentials to connected databases and extract data; Framework and Tally both reported customer-data exposure. Metabase patched its cloud customers, but organizations running self-hosted instances have to update themselves. What caught our eye is the path of attack: an analytics application sitting on top of enterprise data can become the doorway into the underlying databases — making third-party data tools a much more consequential piece of the security architecture than many management teams may assume.

A new congressional proposal would give communities far more control over AI data-center development, including proposed restrictions near homes, schools and hospitals as well as disclosure requirements covering electricity demand, utility bills, water consumption, housing impacts, subsidies and local taxes. Similar pressure is already appearing at the state and utility level. The important technology story is that AI's next constraint may not be chips or models — it may be permission to build the physical infrastructure, with power, water, community opposition and local economics increasingly influencing where and how quickly capacity can be added.

Bending Spoons agreed to acquire Airtable for $1.28 billion in cash. The valuation history is what makes the story so interesting: Airtable had raised more than $1.4 billion, was valued above $11 billion in 2021, and is still producing roughly $480 million of ARR growing more than 20% year over year. For technology founders and investors, the enormous difference between peak-era private SaaS valuations and today's acquisition economics is a useful reminder that good growth and a recognizable product do not necessarily preserve a former valuation multiple.

The AI investment boom is creating unexpected competition for electricians, skilled technicians, power infrastructure, memory chips and other constrained resources while simultaneously changing how companies recruit and evaluate employees. Business leaders planning AI investments may therefore find that the largest barriers are not the algorithms themselves but access to the people, physical infrastructure and underlying capacity required to deploy them.

The scale of AI infrastructure investment is beginning to ripple well beyond the technology sector, increasing demand for power, chips, equipment, specialized labor and other constrained resources. Business leaders should consider AI not only as a productivity opportunity but as a new macroeconomic force capable of affecting inflation, capital costs, technology budgets and potentially the direction of interest rates.