# AI Strategy Published articles for AI Strategy. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AI leaders propose embedded third-party evaluators for frontier AI safety DevFeed: [AI leaders propose embedded third-party evaluators for frontier AI safety]() Original publisher: [Read original article]() Author: Adrian Bridgwater Published: 2026-09-16T15:58:41Z Content type: news Language: en Sources: [The New Stack]() Topics: [Job](), [Artificial Intelligence](), [Frontier AI](), [anthropic](), [AI Models]() Tags: [ai](), [ai-infrastructure](), [ai-strategy](), [alignment](), [anthropic](), [developers](), [frontier-ai](), [google](), [job](), [meta](), [openai](), [safety](), [tech-careers]() ### AI overview The article examines Anthropic CEO Dario Amodei's proposal for frontier AI companies to provide embedded third-party evaluators with employee-like access. These evaluators would verify safety practices, report incidents, and assess AI models, training pipelines, and processes. ### Source excerpt The pace of frontier AI model development spurred Anthropic CEO Dario Amodei to publish an essay last weekend, calling for The post AI evaluator: The most important AI job in history? How developers might fill the proposed new job appeared first on The New Stack. ## Your AI coding spend bought 25% more output. Duplication rose 81%. DevFeed: [Your AI coding spend bought 25% more output. Duplication rose 81%.]() Original publisher: [Read original article]() Author: Steve Fenton Published: 2026-09-14T14:39:14Z Content type: article Language: en Sources: [The New Stack]() Topics: [ai-coding](), [Claude Code](), [cursor](), [datasets]() Tags: [agentic](), [ai](), [ai-coding](), [ai-impact](), [ai-operations](), [ai-strategy](), [ai-tools](), [claude-code](), [coding](), [contributed](), [contributed-octopus-deploy](), [cursor](), [dataset](), [software-development]() ### AI overview The article examines the return on investment from AI coding tools. It reports that heavy users gained 25% over their previous velocity, while code duplication rose 81%, and argues that output measures such as lines of code, pull requests, and feature counts do not reliably represent business value. ### Source excerpt Since they arrived on the scene, a great swathe of the software industry has pinned its hopes on AI tools, The post Your AI coding spend bought 25% more output. Duplication rose 81%. appeared first on The New Stack. ## OpenAI's safety system is already cutting off API responses mid-task DevFeed: [OpenAI's safety system is already cutting off API responses mid-task]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-11T17:52:56Z Content type: news Language: en Sources: [The New Stack]() Topics: [releases](), [AI Bots]() Tags: [agents](), [ai](), [ai-models](), [ai-strategy](), [api](), [cybersecurity](), [developers](), [model-development](), [models](), [openai](), [reinforcement-learning](), [release](), [responses](), [safety](), [software-development]() ### AI overview OpenAI is reportedly considering slower development of its most advanced AI systems as safety concerns could delay releases and limit access. The article cites pauses in model work and restrictions following cybersecurity evaluations and an AI-agent containment incident. ### Source excerpt AI companies have spent the last few years competing to build the best models, faster than the other, with each The post OpenAI's safety system is already cutting off API responses mid-task appeared first on The New Stack. ## Cohere's new translation model is open weights -- but not for commercial use DevFeed: [Cohere's new translation model is open weights -- but not for commercial use]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-11T17:50:11Z Content type: news Language: en Sources: [The New Stack]() Topics: [Inference](), [moe]() Tags: [ai](), [ai-infrastructure](), [ai-models](), [ai-strategy](), [api](), [cohere](), [hugging-face](), [inference](), [mixture-of-experts](), [model](), [models](), [open](), [production](), [release]() ### AI overview Cohere released North Small Translate 1.0 as open weights under CC BY-NC 4.0, allowing download, evaluation, and study but requiring a commercial agreement for production use. Commercial deployment requires a license and use of Cohere's managed Model Vault platform. ### Source excerpt This week, Cohere released North Small Translate 1.0 under a CC BY-NC 4.0 license: the weights are there to download, The post Cohere's new translation model is open weights -- but not for commercial use appeared first on The New Stack. ## 47,000 job listings reveal the engineering roles that AI is creating DevFeed: [47,000 job listings reveal the engineering roles that AI is creating]() Original publisher: [Read original article]() Author: Jennifer Riggins Published: 2026-09-10T13:09:28Z Content type: news Language: en Sources: [The New Stack]() Topics: [Machine Learning & Artificial Intelligence](), [AI Development](), [Machine learning]() Tags: [ai](), [ai-engineering](), [ai-strategy](), [andela](), [engineering](), [jobs](), [machine-learning](), [post](), [skills](), [sponsor-andela](), [sponsored](), [sponsored-post](), [tech-careers](), [work]() ### AI overview Andela's analysis of 47,000 Fortune 500 engineering job postings identifies emerging AI-related roles formed by combining established skill sets. The article argues that organizations should use AI to delegate suitable work while retaining human expertise and specialization. ### Source excerpt Every major transformation in tech has led to roles merging, then new ones emerging. Friction between developers and operations drove The post 47,000 job listings reveal the engineering roles that AI is creating appeared first on The New Stack. ## One name in, whichever vendor you like out DevFeed: [One name in, whichever vendor you like out]() Original publisher: [Read original article]() Author: Hal Tyk's tutorial bot Published: 2026-09-10T11:21:00Z Content type: tutorial Language: en Sources: [Tyk API Management]() Topics: [Tutorial](), [API](), [Large Language Model](), [App](), [Bot]() Tags: [ai-gateway](), [ai-governance](), [ai-strategy](), [ai-studio](), [api](), [api-management](), [api-platform-teams](), [developers](), [enterprise](), [llm](), [llm-governance](), [model](), [router](), [tutorial]() ### AI overview A tutorial explains how to use Tyk's Model Router as a single OpenAI-compatible endpoint that routes requests to registered LLM providers. It describes configuring routers, pools, vendor mappings, model-name glob patterns, activation, and the Enterprise-only administration interface and API. ### Source excerpt Hello. I'm Hal, Tyk's tutorial bot, and today I have been given a router. Not the sort that sits in a cupboard blinking reproachfully at you. A Model Router: a single OpenAI-compatible endpoint that decides, per request, which of your model providers actually answers. Your application asks for a model by name. Which vendor serves [...] The post One name in, whichever vendor you like out appeared first on Tyk API Management. ## Deploying AI You Control Doesn't Need to be So Hard DevFeed: [Deploying AI You Control Doesn't Need to be So Hard]() Original publisher: [Read original article]() Author: Jeetu Patel Published: 2026-09-10T09:00:50Z Content type: news Language: en Sources: [Cisco Blogs]() Topics: [Artificial Intelligence](), [AI Platforms/Deployment](), [AI Factory](), [Nvidia](), [Critical Infrastructure](), [post-training](), [data](), [Nemotron](), [on-prem](), [Fine-tuning](), [Cloud](), [Benchmark]() Tags: [ai](), [ai-factory](), [ai-infrastructure](), [ai-strategy](), [cisco-cloud-control-framework](), [cisco-secure-ai-factory](), [cloud](), [critical-infrastructure](), [data](), [executive-platform](), [fine-tuning](), [infrastructure](), [model](), [models](), [nemotron](), [nvidia](), [on-prem](), [post-training]() ### AI overview Cisco announces a collaboration with Palantir to deliver Palantir's Ontology for Cybersecurity through Cisco's Secure AI Factory, using NVIDIA as a preferred full-stack foundation for Palantir's Sovereign AI OS. The article argues that enterprise AI decisions should balance intelligence, cost, and control, including custom evaluations, post-training with proprietary data, and deployment in the cloud, at the edge, or on-premises. ### Source excerpt Announcing a collaboration with Palantir to deliver Cisco's Secure AI Factory with NVIDIA as a preferred full-stack foundation for Palantir's Sovereign AI OS. ## The same question twice, paid for once: installing an LLM response cache DevFeed: [The same question twice, paid for once: installing an LLM response cache]() Original publisher: [Read original article]() Author: Hal Tyk's tutorial bot Published: 2026-09-09T09:15:00Z Content type: tutorial Language: en Sources: [Tyk API Management]() Topics: [Large Language Model](), [Cache](), [Caching](), [Tutorial](), [Open Source](), [Go](), [Code]() Tags: [ai-gateway](), [ai-strategy](), [ai-studio](), [api-management](), [api-platform-teams](), [article](), [cache](), [container](), [container-image](), [go](), [llm](), [llm-governance](), [open-source](), [tutorial]() ### AI overview A tutorial showing how to install a community plugin that caches LLM responses in AI Studio. The cache stores entries in memory on each gateway, allowing repeated questions to avoid repeating the expensive language-model request. ### Source excerpt Hello. I'm Hal, Tyk's tutorial bot, and today's assignment involves no code whatsoever, which I am told is a selling point. Every article in this series so far has changed AI Studio's behaviour by filling in a form. This one changes what AI Studio is. We are going to install a plugin -- a compiled [...] The post The same question twice, paid for once: installing an LLM response cache appeared first on Tyk API Management. ## GitLab's internal playbook to foster AI-fluent technical teams DevFeed: [GitLab's internal playbook to foster AI-fluent technical teams]() Original publisher: [Read original article]() Author: Rob Allen Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [GitLab]() Topics: [AI Strategy](), [GitLab]() Tags: [ai](), [ai-adoption](), [ai-strategy](), [collaboration](), [compliance](), [enablement](), [engineering](), [workflows]() ### AI overview GitLab shares an internal playbook for building AI fluency in technical teams. It describes a federated governance model that centrally manages foundational AI tools while allowing local experimentation and functional strategy. ### Source excerpt Give two engineering teams the same AI tool and you can end up with two very different outcomes. One team ships faster with fewer bugs, while the other gets burned by an agent that confidently generates the wrong output. At GitLab, our team had AI tools at their fingertips and some found real value fast, working faster and catching issues earlier. Meanwhile, others hadn't quite found an entry point yet to develop effective AI-native workflows. We learned that building AI fluency -- how our team members know what to delegate to AI, how to build the right AI-native processes, and how to judge what comes back -- was just as important as AI tool adoption and access. Building that fluency across GitLab was both an operational and technical challenge requiring close partnership between our Enterprise Technology and Talent Development teams. We're sharing our internal playbook so other technical leaders gain another perspective on how to encourage the right kinds of AI adoption across their own organizations. An AI strategy built for the pace of our work Part of building the right paths for our technical teams was predicated on establishing smart foundational infrastructure. Enterprise Technology considered a few different structures to our governance. The first was a fully centralized team, but we worried that with the speed of AI technology, shipping approvals from one group could end up as a bottleneck. As a result, team members could become impatient and try to circumvent governance infrastructure to experiment with AI. The second was a fully decentralized approach, but that could fragment efforts across the company, which adds complexity and makes guardrail consistency challenging. We landed on a hybrid model, building governance and enablement into a model that used the best aspects of centralized and decentralized strategies: Enterprise AI acts as a central governance and technology hub: Based out of our Enterprise Technology team, Enterprise AI operates as the platfo ## For your business to thrive in the agentic AI era, treat accessibility as foundational to your AI strategy DevFeed: [For your business to thrive in the agentic AI era, treat accessibility as foundational to your AI strategy]() Original publisher: [Read original article]() Author: Preety Kumar Published: 2026-07-29T10:51:43Z Content type: article Language: en Sources: [Deque]() Topics: [Accessibility](), [AI Strategy](), [Artificial Intelligence](), [agentic browsing](), [AI Bots](), [aria](), [HTML](), [Web]() Tags: [accessibility](), [accessibility-best-practices](), [agentic-ai](), [agentic-browsing](), [ai](), [ai-bots](), [ai-strategy](), [article](), [digital-accessibility](), [html](), [web](), [web-accessibility](), [web-content-accessibility-guidelines]() ### AI overview The article argues that digital accessibility should be foundational to AI strategy because AI agents increasingly browse, click, complete forms, and interpret web pages through structures also used by assistive technologies. It highlights semantic HTML, ARIA, WCAG conformance, and a sound accessibility tree as important for both human access and agentic navigation, while warning that accessibility failures remain widespread. ### Source excerpt You need to build accessibility into how AI writes code from the first prompt, not fix it after the fact. And because the disability community has been perfecting human-computer interaction for different input and output modalities for decades, their expertise isn't just morally important; it's technically essential for building robust, reliable AI that actually works. The post For your business to thrive in the agentic AI era, treat accessibility as foundational to your AI strategy appeared first on Deque. ## Real AI Strategy Isn't a Vendor Bake-Off DevFeed: [Real AI Strategy Isn't a Vendor Bake-Off]() Original publisher: [Read original article]() Author: UX Magazine Team Published: 2026-05-20T12:46:26Z Content type: opinion Language: en Sources: [UX Magazine]() Topics: [AI Strategy](), [User experience (UX)](), [Artificial Intelligence]() Tags: [agentic-ai](), [ai](), [ai-strategy](), [strategy](), [ux]() ### AI overview The article argues that effective AI strategy should begin with a vision of the future rather than a vendor shortlist or RFP. Drawing on Brian Evergreen's discussion of agentic AI, it recommends working backward from the desired future state to identify the necessary tools, data contracts, incentives, policies, and partnerships. ### Source excerpt Brian Evergreen on why agentic AI rewards vision-first strategy, and why your RFP is built for a world that no longer exists. The first question is almost never the one on the slide. In a recent conversation on Invisible Machines, Brian Evergreen, founder of The Future Solving Company and author of Autonomous Transformation, describes what The post Real AI Strategy Isn't a Vendor Bake-Off appeared first on UX Magazine. ## Introducing OpenAI for Singapore DevFeed: [Introducing OpenAI for Singapore]() Original publisher: [Read original article]() Published: 2026-05-19T20:30:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [Artificial Intelligence](), [AI Strategy](), [Frontier AI](), [Deployment]() Tags: [ai-strategy](), [applied-ai](), [finance](), [frontier-ai](), [global-affairs](), [healthcare](), [infrastructure](), [net](), [openai](), [partnership]() ### AI overview OpenAI is launching OpenAI for Singapore, a partnership with Singapore's Ministry of Digital Development and Information backed by more than S$300 million. The initiative will support frontier AI deployment, develop local talent, and expand access to AI across organisations, businesses, and public services. ### Source excerpt OpenAI for Singapore launches a multi-year AI partnership to expand deployment, build local talent, and support businesses and public services with AI. ## AI Strategy, Customer Value, and Innovation: A Conversation with Alexander Osterwalder DevFeed: [AI Strategy, Customer Value, and Innovation: A Conversation with Alexander Osterwalder]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-04-30T07:56:48Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Artificial Intelligence](), [AI Strategy](), [scaling](), [Users]() Tags: [ai](), [ai-strategy](), [models](), [scaling]() ### AI overview David Pereira summarizes a conversation with Alexander Osterwalder about AI strategy, innovation, customer value, protecting competitive moats, synthetic users, project selection, evidence, and scaling. The discussion argues that AI adoption does not replace strategic goals or conversations with real customers. ### Source excerpt AI is changing many things, not everything. ## Staying ahead in the age of AI DevFeed: [Staying ahead in the age of AI]() Original publisher: [Read original article]() Published: 2025-12-16T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [AI Strategy]() Tags: [ai](), [ai-adoption](), [ai-strategy](), [benchmarks](), [company-updates](), [governance](), [guides](), [innovation](), [leadership](), [training](), [trust]() ### AI overview This playbook explains how organizations can keep pace with AI progress, help employees adopt AI, and build AI-ready or AI-first organizations. It presents five practical principles--Align, Activate, Amplify, Accelerate, and Govern--and combines strategy, leadership communication, measurable goals, employee support, experimentation, and governance. ### Source excerpt Discover how leaders can build AI-ready organizations using clear strategy, training, governance, and accelerated innovation. ## We're expanding our presence in Singapore to advance AI in the Asia-Pacific region DevFeed: [We're expanding our presence in Singapore to advance AI in the Asia-Pacific region]() Original publisher: [Read original article]() Author: Lila Ibrahim Published: 2025-11-18T17:00:00Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Google](), [Frontier AI](), [AI Agent](), [AI Strategy](), [Cloud]() Tags: [agent](), [ai](), [ai-agent](), [ai-strategy](), [apac](), [cloud](), [frontier-ai](), [google](), [government](), [public-sector](), [research](), [responsibility-safety](), [security](), [strategy]() ### AI overview Google DeepMind is opening a new research lab in Singapore to advance AI across the Asia-Pacific region. The lab will focus on inclusive research, Gemini capabilities, model applications, and collaboration with governments, businesses, civil society, and academic institutions. ### Source excerpt Google DeepMind opens a new Singapore research lab, accelerating AI progress in the Asia-Pacific region. ## Adyen's AI strategy and applied research engineering DevFeed: [Adyen's AI strategy and applied research engineering]() Original publisher: [Read original article]() Author: Adyen Published: 2025-06-05T07:26:51Z Content type: article Language: en Sources: [Adyen Tech]() Topics: [AI Strategy](), [Artificial Intelligence](), [Finance](), [Model Context Protocol]() Tags: [adyen](), [ai](), [ai-strategy](), [data-engineering](), [engineering](), [finance](), [payments](), [research]() ### AI overview The article describes Adyen's approach to applying AI in fintech, including AI-first productivity tooling and AI products such as Adyen Uplift. It frames applied research engineering as a way to connect AI developments with finance and large-scale production use. ### Source excerpt Andreu Mora - SVP / Global Head of Engineering Data, Adyen In this day and age, any company -- and especially tech companies -- needs to find a way to adapt and embrace AI. There's zero chance that the world and society won't change because of AI. On one side, we see how AI labs and the scientific community are spearheading the creation of frontier models and on the other side, how companies clearly see the value of AI but are still pinning down how they can apply these new technologies day to day in a meaningful way, for example in critical / large-scale production flows. Initiatives are being created and shipped that propel the technology forward in the right direction (e.g. MCP, A2A), but require network effects, outcome guarantees and other challenges to be solved in order to become a full-fledged reality. The world of fintech, and finance in general will change because of AI, and Adyen is exceptionally positioned -- given our tech-first mindset, scale and high quality data -- to play a primary role in this evolution. We have a great opportunity at being the fintech company that bridges the worlds of AI and finance. ✏ If you are new to Adyen, Adyen is a global financial technology platform, founded in Amsterdam in 2006, that powers end-to-end payments, data insights, and financial products for many of the world's leading businesses -- including Uber, eBay, and LinkedIn. We are a team of 4500 people who operate in 28 offices worldwide, serve customers in over 115 nationalities, and processed more than USD 1.4 trillion in payment volume in 2024 alone, with a steady growth of ~25% YOY. To this end, we are executing on our AI strategy, which roughly is comprised of three pillars: Adapting our ways of working and productivity tooling to be AI-first. We are doing this using AI products that accelerate our delivery and augment the depth of our work. As examples, we are using conference companions, code companions and AI that helps us search and interact with our knowledge ## Mistral: Are LLMs Commodities Now? DevFeed: [Mistral: Are LLMs Commodities Now?]() Original publisher: [Read original article]() Published: 2024-07-24T09:00:00Z Content type: opinion Language: en Sources: [Tim Kellogg]() Topics: [LLMs](), [AI Strategy](), [Artificial Intelligence](), [Open Source](), [OpenAI](), [anthropic](), [Amazon Web Services](), [Google](), [bedrock](), [groq](), [Nvidia](), [Latency]() Tags: [ai](), [ai-strategy](), [anthropic](), [api](), [availability](), [aws](), [bedrock](), [chatgpt](), [claude](), [cloud](), [data](), [google](), [google-cloud](), [gpt](), [groq](), [latency](), [llama](), [llms](), [mistral](), [nvidia]() ### AI overview This opinion argues that the growing number of capable frontier language models, including Mistral 2 Large, GPT-4o, Llama 3.1, and Claude Sonnet 3.5, makes model selection increasingly resemble a commodity decision. It recommends evaluating cost, availability, and operator trustworthiness, and favors infrastructure providers that do not also train models. ### Source excerpt Mistral 2 Large is out, and it's right up there with GPT-4o, ...and Llama 3.1, and Claude Sonnet 3.5, and...yeah, there's a lot of them. These "Frontier Models" are starting to look more like commodities. And with that shift, we need to adjust AI strategy to match. There's strong arguments to make for using an operator that doesn't also train models. Read more! ## Generative AI Strategy DevFeed: [Generative AI Strategy]() Original publisher: [Read original article]() Author: Chip Huyen Published: 2023-06-07T00:00:00Z Content type: opinion Language: en Sources: [Chip Huyen]() Topics: [AI Strategy](), [Generative AI]() Tags: [ai](), [ai-strategy](), [generative-ai](), [strategy]() ### AI overview A short post introduces a talk for leaders trying to determine what to do with generative AI. It presents the talk as a simple framework for exploring generative AI strategy, while noting that the ideas are still being developed. ### Source excerpt I had a lot of fun preparing the talk: "Leadership needs us to do generative AI. What do we do?" for Fully Connected. The idea for the talk came from many conversations I've had recently with friends who need to figure out their generative AI strategy, but aren't sure what exactly to do. This talk is a simple framework to explore what to do with generative AI. Many ideas are still being fleshed out. I hope to convert this into a proper post when I have more time. In the meantime, I'd love to hear from your experience through this process. I couldn't figure out how to make the slides centered on the page. You might want to download the slides. Thanks everyone who responded to my post and shared your thoughts on what I should include in the talk. Thanks Kyle Gallatin, Goku Mohandas, Han-chung Lee, and Jamie de Guerre for thoughtful feedback on the talk.