The AI Pulse
Issue #83 — 6th July 2026
Editor: Professor Alan Brown
This week's AI Pulse is supported by AI Week, the ten day event from 12-23 October bringing together leaders from across the UK's public, private, and third sectors to explore what AI means for digital leadership. We're now open for talk submissions. If you have a perspective worth sharing, we want to hear from you. Click here to find out more and submit your talk proposal.
Highlights in this edition include:
AI for Good
Meaningful Outcomes Determine the Winners of the Health AI Race (WEF) - This report argues that success in the health AI race will be determined not by technological sophistication but by which systems first identify and pursue the outcomes that matter most to patients, professionals and health systems.
Government enlists AI to tackle obesity (UKAuthority) - Twelve new projects aimed at transforming obesity care across the UK will receive up to £85 million in grant funding from government and pharmaceutical company Eli Lilly.
Bias and Ethics
When AI builds itself (Anthropic) - Anthropic is exploring how AI systems might improve their own capabilities. A shift that could dramatically speed up development cycles, though it raises fundamental questions about control and alignment as machines begin to reshape themselves.
AI agents are not your “coworkers” (MIT Technology Review) - MIT is warning that branding AI agents as “coworkers” can backfire—people start trusting them too much, miss their mistakes, and dodge responsibility when things go wrong.
Rapid spread of AI may worsen global inequality, UN warns (The Guardian AI) - The UN is sounding the alarm that AI’s explosive growth could deepen the gap between rich and poor nations, as wealthier countries race ahead while others lack access to the technology and its benefits.
Cyber Security
A better way to manage LLM spending (Infoworld) - Organizations are slashing their AI bills by automatically sending straightforward questions to budget-friendly models and reserving expensive ones for complex tasks.
Find your data before AI does (UKAuthority) - Before public bodies connect AI tools to their estates, they need to know what data they hold, where it lives, who can access it, and whether it is accurate, current, and safe to use.
Shadow agents: How IT leaders must govern ‘headless’ AI before it breaks the enterprise (CIO) - IT teams are facing a new security headache: AI agents that autonomously handle tasks across company systems without traditional logins, making them nearly impossible to monitor using standard tools—forcing leaders to rethink how they track and govern these “invisible” workers before they create serious vulnerabilities.
Data & Decision Making
3 metrics to help you measure AI’s impact (FastCompany) - FastCompany proposes three practical metrics for tracking whether AI investments are actually paying off, helping leaders cut through the hype and see if their tools are truly boosting efficiency, revenue, or customer satisfaction.
Teach Your AI How You Make Decisions (Harvard Business) - Harvard Business Review argues that companies must now make their tacit decision-making processes explicit by codifying employee judgment on risk, quality, and tradeoffs so that AI agents can scale that expertise consistently across the organization.
How people are using GenAI chatbots: Evidence from web traffic data (OECD) - The OECD looks into web traffic patterns to see who’s actually using GenAI chatbots and what they’re doing with them. It reveals notable differences across countries and age groups that matter for both product design and policy planning.
Teach Your AI How You Make Decisions (Harvard Business) - Harvard Business Review argues that companies must now make their tacit decision-making processes explicit by codifying employee judgment on risk, quality, and tradeoffs so that AI agents can scale that expertise consistently across the organization.
Beware of AI costs hidden in plain sight (CIO) - Many CIOs are flying blind on AI expenses. Hidden fees are scattered across vendor contracts, pay-per-use models, and departmental budgets, making true cost tracking nearly impossible for most organizations.
Agriculture is ready for AI, but its data isn’t (MIT Technology Review) - MIT Technology Review highlights a key bottleneck in farm tech: AI tools are promising, but the underlying data—often messy, inconsistent, or poorly managed—isn't ready to fuel them effectively yet.
Innovation & Collaboration
Artificial intelligence for food innovation (Nature) - Nature highlights how AI is reshaping food development, from linking molecular structures to taste and texture, to speeding up product innovation. But warns that poor data quality, lack of transparency, and governance gaps could limit its impact without better collaboration and human oversight.
NAO warns HMRC to learn from innovation missteps (UKAuthority) - HMRC has used Home Office travel data to tackle fraud and error in child benefit payments, identifying claimants who may have moved overseas and are no longer eligible for the benefit.
Productivity & Efficiency
The New (And Slightly Smelly) Center of the AI Boom (The Atlantic) - San Francisco’s top AI talent is cramming into shared “hacker houses,” trading luxury for collaboration as the city becomes the center of an industry reshaping how we work and live.
AI companies want to pick your brain for cash (Fastcompany) - AI companies are now paying everyday people to share their thoughts and expertise, using that human input to train chatbots and fix problems their models can’t solve alone.
Achieving operational excellence with AI (MIT Technology Review) - Organizations that have already nailed down solid processes are finding it much easier to adopt AI effectively, proving that strong operational foundations matter more than ever as companies race to automate and scale.
Regulation and Compliance
Independent International Scientific Panel on AI (United Nations) - The United Nations’ Independent International Scientific Panel on AI has published its Preliminary Report from 40 experts. It concludes that we are governing a system we cannot fully measure.
What Trump’s Anthropic AI ban means for Britain (New Statesman) - Trump’s new restrictions on Anthropic’s AI technology could leave Britain and other US allies scrambling for access to cutting-edge tools, raising uncomfortable questions about whether the UK can afford to be sidelined in the global AI race.
The Real Question to Ask About AI Governance (MIT Sloan Review) - MIT Sloan Review asks who’s actually in charge of AI at your company because having governance frameworks is pointless if no one with real authority is enforcing them.
Sustainability
AI’s Volatile Power Use Quietly Tests Grid Limits (IEEE) - Data centers running AI models are creating wild power swings that electric grids weren’t built to handle, surging from idle to full capacity in seconds, straining infrastructure designed for steady, predictable loads.
Sustainable AI guidance for academia launched (UKAuthority) - Jisc and the Environmental Association for Universities and Colleges (EAUC) have launched a new guide to help universities and colleges address the environmental impact of AI.
User Experience
How AI is changing language (The Guardian) - Linguists are breaking down what separates human writing from AI-generated text, while acclaimed novelists like Jennifer Egan and Jeanette Winterson wrestle with what fiction means when machines can mimic our words.
NHS to use AI on its app to direct patients to appropriate services (The Guardian AI) - The NHS is rolling out an AI feature in its app to help around 200,000 patients find the right care faster, whether that’s a GP visit, pharmacy advice, or emergency services. This is part of a broader £10 billion effort to modernize how the health system operates.
Workforce & Skills
4 reasons AI projects fail that have nothing to do with technology (CIO) - Companies sink AI projects by ignoring the human side. Workers fear job loss, cultures resist change, and executives chase headlines instead of fixing real problems.
The AI jobs debate just got messier (TechCrunch AI) - Companies going all-in on AI are actually hiring more people, not fewer, with entry-level roles growing even faster than the overall workforce, challenging fears that automation wipes out junior positions.
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