Galini is a forward-deployed AI team for mid-market companies that are struggling to realize measurable value from #AI#. They pick 1–2 high-#ROI# workflows and work side-by-side with your teams to scope, design, and build the solution in your environment over 8 weeks with affordable price, so your team builds the capability alongside them. Founders are ex-McKinsey and ex-Bridgewater (SRE), on the path to Anthropic Partner Network. #AIadoption#

Last Friday, together with @e27co, we hosted an exclusive roundtable with founders and senior leaders to discuss how businesses can move from #AI# experimentation to real-world deployment. The discussion was energetic and highly practical, with participants sharing real use cases, deployment challenges, and solution approaches across industries, from SME operators to enterprise teams. One message came through clearly: AI is moving beyond experimentation into execution, integration, and measurable business impact. A few key takeaways stood out: 🤖 AI adoption is gaining traction across customer service, coding, sales, productivity, robotics, IoT, and agentic AI. 🎯 Scaling AI still requires companies to address cost, infrastructure complexity, trust, regulation, data privacy, and internal readiness. 🚀 The fastest-moving companies are shortening planning cycles, launching practical solutions earlier, testing in real-world environments, and iterating quickly. At Bitdeer AI, we believe scalable AI infrastructure is essential to helping businesses turn AI ideas into production-ready outcomes. As our commitment is to eliminate infrastructure complexity and cost barriers, enabling teams to build, deploy, and scale AI with speed and confidence. Thank you to e27 and all participants for the insightful discussion. #AIAdoption# #necloud# #AIInfrastructure# #AI#

AI deployment doesn’t have to mean reinventing your infrastructure. AMD’s Suresh Andani explains how AMD Instinct MI350P PCIe GPUs combine open software, enterprise-ready AI infrastructure, and scalable inference performance to help organizations accelerate AI adoption within existing data centers. Same racks. Same cooling. Dramatically more AI. Watch the full video on YouTube:

AI adoption shouldn’t require rebuilding your data center. AMD Instinct MI350P PCIe GPUs are designed to deliver production AI inference performance in standard air-cooled servers, helping enterprises scale AI with existing infrastructure. Learn more:

Weekly AI Update OPENAI ENTERPRISE AI SURGE OpenAI CRO Denise Dresser said enterprise AI adoption has reached a “tipping point” as the company launched the OpenAI Deployment Company to accelerate corporate onboarding through consulting partnerships and Tomoro’s 150 AI engineers. Enterprise now contributes over 40% of OpenAI revenue and is projected to match consumer revenue by 2026. AMAZON AI SHOPPING PIVOT $AMZN is discontinuing its Rufus chatbot and launching “Alexa for Shopping,” an AI-powered shopping assistant integrated into Amazon search results and Echo devices. The tool combines Alexa+ with Rufus data to personalize recommendations, compare products, track prices, and execute purchases as Amazon intensifies its AI commerce push. OPENAI EXPANDS EU CYBER ACCESS OpenAI said it will grant EU governments, institutions, and cybersecurity teams access to GPT-5.5-Cyber, its new cybersecurity-focused AI model, as competition with Anthropic intensifies. While $GOOGL backed Anthropic released its Mythos model last month, EU officials said discussions for access remain at an earlier stage.

Enterprise AI adoption shouldn’t require rebuilding your data center. AMD’s Suresh Andani shares how the new AMD Instinct MI350P PCIe cards enable enterprises to deploy and scale generative and agentic AI using existing air-cooled infrastructure, while delivering strong AI performance and operational simplicity. Watch the full video on YouTube:

UK sovereign AI is becoming reality. Together with Argyll , we’ve launched a sovereign AI inference cloud built on SambaNova’s full-stack AI platform — designed for performance, efficiency, and control without the trade-offs of traditional infrastructure. As AI adoption scales, sovereignty can’t just be a label. It has to be demonstrated through infrastructure ownership, operational control, energy efficiency, and where intelligence is deployed. This deployment delivers: ⚡ High-performance AI inference 🌍 Renewable-powered infrastructure 🔋 ~10kW rack density with air-cooled systems 🏗️ Disaggregated architecture across UK data centers 🧠 SambaNova RDUs for efficient large-scale AI This is what the next generation of AI infrastructure looks like. Read more via DatacenterDynamics ⤵️

People compare crypto adoption to AI adoption, but the starting conditions are different. AI says: "here's a new capability you didn't have before". Crypto says: "replace the financial system you already use". Very different ask. AI is free to test, instantly useful, and saves time from day one. Crypto often requires an effort to learn and educate yourself, you'll need to pay actual $ just to try it and see what's this all about, and it takes time for you to see that it pays off. AI is a technological innovation. Crypto is a financial change and financial behavior changes slowly.

The businesses getting the most out of AI right now aren't tech companies. They’re plumbers, agency owners, dentists, Etsy sellers. New data from @OpenAI (cc @RonnieChatterji) makes the pattern clear: tech startups account for about 5% of ~active~ U.S. users doing entrepreneurial work with ChatGPT. The other 95% are spread across services, retail, healthcare, and trades. AI adoption for entrepreneurship isn’t concentrated in tech. It’s happening inside everyday businesses, folding into routine work that used to be slower or outsourced or entirely overlooked.

Three more things are still blocking mainstream #AI# adoption: 1. Reliability AI systems are impressive in demos, but inconsistent in the real world. They hallucinate, misinterpret context, and occasionally fail at basic tasks. That’s fine for experimentation. Not fine when you’re handling real operations. Until outcomes are predictable, businesses will hesitate to fully rely on them. 2. Evolving regulations and change management The rules are still being written. Governments are actively shaping policies, and companies don’t want to bet big on something that might be restricted tomorrow. Adopting AI isn’t a plug-and-play upgrade, it reshapes workflows, roles, and accountability. Most organizations aren’t ready for that level of change yet. 3. Integration complexity AI doesn’t live in isolation, it needs to connect with existing systems, data pipelines, #privacy# and #security# layers. These hurdles explain why AI feels everywhere in conversation, but still not fully embedded in everyday business operations. Also there is an AI native vs #Saas# AI competition as well. It will take sometime.