Databases 2025 Year in Review

The Databases 2025 Year-in-Review from Carnegie Mellon University is out! As always there are some great insights on database trends across last year: As expected Postgres dominance continues to accelerating, particularly with "postgres-adjacent" products, like Databricks acquiring Neon (~$1B), Snowflake acquiring Crunchy Data (~$250M), and Microsoft launching a new Postgres DBaaS, together with many other new middleware projects (Multigres, Neki, PgDog).  For production ML teams, the biggest operational shift is that databases are becoming "agent-facing" by design, with huge adoption of MCP as a standard way for LLMs to invoke database tools. There are some areas to keep an eye such as the API-compatibility as it's turning into a legal minefield (MongoDB suing FerretDB), as well as open data formats having another re-surgence with multiple new columnar formats challenging Parquet and pushing new ideas. Databases are core to the day-to-day software operations, so we will likely see some exciting new develolpments in the year to come, especially with the craze on agentic development.

Lessons from 21 Years at Google

"21 Lessons From 14 Years at Google" - the top 5 that resonated the most for me: 1) You can win every technical argument and lose the project. 2) Bias towards action. Ship. You can edit a bad page, but you can’t edit a blank one. 3) Your code doesn’t advocate for you. People do. 4) At scale, even your bugs have users. 5) Most “slow” teams are actually misaligned teams. This really is a great list as it reminds all of us how some of the most challenging and useful lessons are often not related to the code but to the soft skills and people interactions.

The State of MLOps 2025 Survey 🔥

The level of ML maturity of organisations in 2025 has slowly increased, and we have interesting insights on organisational departments/functions from our Prod ML Survey:

• In 2025 about 29% of organisations have set up a central machine learning platform team.
• However only 27% organisations have established a data platform / data engineering organisation.
• There is a significant increase with 17% organisations now establishing an AI Risk & Governance Function.
• However only 9% of organisations have established an AI inventory to keep track of all use-cases and models in the organisations.

If you want to dive deeper you can access the full results here: https://ethical.institute/state-of-ml-2025 🔥

Raschka's Big LLM Architectures

Sebastian Raschka has put together an overview on the evolution of LLM architectures, together with the most comprehensive set of intuitive visual diagrams. It is interesting to see that Sebastian Raschka argues that these models are still structurally similar to when they were initially released. Positional embeddings have evolved from absolute to rotational (RoPE), Multi-Head Attention has largely given way to Grouped-Query Attention, and the more efficient SwiGLU has replaced activation functions like GELU, however it is yet to be seen if there's been step-change breakthroughs since inception. Definitely worth diving into what seems to be one of the most comprehensive (intutive) overviews of LLMs out there.

Measuring AI Ability for Long Tasks

METR has released an in-depth study that measures the ability for LLM agents to solve long tasks, and there's quite a lot of interest insights: In this study they first propose a practical way to quantify agentic LLM capability using a task completion horizon, which involves the human time-to-complete where an AI agent succeeds with 50% probability. Across their datasets it shows that task duration strongly predicts reliability, with near-perfect success on very short tasks but steep drop-offs on hour-scale tasks. Although this is intuitive it is great to see some data, which also shows how frontier models has been following a robust exponential trend since 2019, doubling roughly every ~7 months. There are some interesting developments that attribute to this, including better tool use, reasoning, and recovery from mistakes rather than mere knowledge. This is a space that will only expand in 2026, with a lot of exciting developments ahead.

The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning.

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