The State of AI Talent in 2025 The entry-level recruitment pipeline in tech is collapsing; headcounts are shifting to core ML; Elite AI labs are hoovering top talent; there are some key trends in the state of AI Talent in 2025: This latest reports brings together insights from over 650 million professionals and 80+ million organisations on critical topics to keep a close eye if you are in the AI space. 1) Significant reduction on entry-level hiring: Only ~7 % of Big-Tech hires and <6 % of VC-backed-startup hires now come from new grads – roughly half the pre-COVID share. 2) Head-count is being re-balanced toward core ML infrastructure with ML/AI +27%, Data Engineering +3%, Senior Software Eng +3%, Product/Design/Sales/Recruiting −10%. 3) Elite labs are poaching top talent: For every 1 engineer Anthropic loses to DeepMind it gains ~10; the OpenAI-to-Anthropic ratio is 8:1. 4) Hiring is shifting towards big hubs: Bay Area, Seattle and NYC regained Big-Tech head-count in 2024; Austin (-6%), Houston (-11%). |
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Migrations with LLMs at Google Google has published their lessons learned embedding GenAI automation on exa-scale migrations with ~50% task reduction: It is quite interesting to see Google adopting LLMs tuned on internal-code for acceleration of large-scale migrations across their organisation, combining it with a staged validation chain (syntax, build, tests) to automate large-scale “32-bit → 64-bit ID” refactors across their monorepo. Over 12 months Google processed 39 migrations, producing 595 change lists where the LLM authored 74% of CLs and ~70% of all character edits, enabling the three engineers involved to cut total migration time by roughly half while still preserving human review for any diff that fails automated gates. |
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Claudfare's GenAI Development A formerly LLM-skeptical Cloudflare engineer spent two months pair-programming with Anthropic’s Claude to build a production-grade OAuth 2.1 provider for Cloudflare Workers - it is interesting to see the mind-shift discovering that an LLM can act as an effective “diff generator” when tasks are framed in tight, reviewable increments: The Claudflare team has published the codebase as an official package together with the detailed commits that summarise the prompts used for each of the respective changes, as well as the iterations when addressing specific bugs, change requests, etc. As part of this repo we can see that Claude produced most boilerplate, tests, CI actions, metadata and KV storage schema, while the human steered high-risk logic (crypto, PKCE, refresh-token rotation) and enforced security reviews For production ML practitioners this is a perfect example to follow for best practice when it comes to leveraging LLMs as a productive copilot. |
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Apple on Limitations of LLMs Apple has published a fantastic analysis on the capability for LLMs on reasoning tasks through benchmarks that evaluate the chain of reasoning as opposed to purely the final answers, showing that LLMs are still quite limited on reasoning tasks: In safety-critical AI—from self-driving fleets to automated trading—robust machine reasoning is the thin line between insight and disaster, and so far it has been ambiguous to quantify how good LLMs are (despite positive outcomes in various benchmarks). It is clear that vanilla LLMs dominate trivial tasks, Language Reasoning Models excel at moderate complexity, yet both implode to near-zero accuracy once complexity crosses a critical threshold, with tell-tale patterns like accuracy that drops as reasoning lengthens on simple problems and flat-lines on hard ones. For us as practitioners, LRMs remain probabilistic pattern matchers which have to be leveraged where they are fit-for-purpose. |
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Mistral Release Codestral Embed It is now clear that fast and precise code retrieval is critical for next-gen developer tooling, and Mistral has now entered the race with their new code-specialised model: This new model from Mistral (codestral-embed-2505) comes in with impressive performance topping nine retrieval benchmarks, beating Voyage Code 3, Cohere v4, and OpenAI Text-Embedding-3 Large by 5-25 pp, even when compressed to 256-dim int8 (which is now important for efficiency and speed). It will be interesting to see it in practice particularly with RAG-based copilots, semantic/NL code search, duplicate detection, and repo analytics. |
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Upcoming MLOps Events The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below. Upcoming conferences where we're speaking:
Other upcoming MLOps conferences in 2025:
In case you missed our talks:
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Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ⭐ github stars. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. Four featured libraries in the GPU acceleration space are outlined below. - Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced data processing usecases.
- CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it.
- Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
- CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
If you know of any open source and open community events that are not listed do give us a heads up so we can add them! |
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As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following: - MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle as well as best practices.
- AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems.
- An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles.
- ACM's Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018.
If you know of any guidelines that are not in the "Awesome AI Guidelines" list, please do give us a heads up or feel free to add a pull request!
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| | | | 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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