Wed 8 April 2026
Daily Brief — Curated and contextualised by Best Practice AI
Enterprises Adopt AI Widely, Startups Pivot to Agents, and Workers Scramble for Retraining
TL;DR AIMG's report indicates 87% of enterprises are using AI, but just 19% have fully prepared data infrastructure. Perplexity's revenue surged 50% after shifting to AI agents from search. A joint report warns 60% of companies may lay off staff unwilling to adopt AI, as older workers turn to training programs amid job market pressures. US power consumption will hit record highs in 2026 and 2027, driven by AI data centers. Iran has threatened strikes on Middle East AI facilities, targeting the $500 billion Stargate project.
The stories that matter most
Selected and contextualised by the Best Practice AI team
Perplexity monthly revenue jumps 50% in pivot from search to AI agents
San Francisco-based start-up surges from push into more complex and potentially more lucrative AI services
Robot Maker Kuka Eyes US, Asia as Europe’s Factories Lag on AI
Many of Europe’s industrial companies are too slow to adopt artificial intelligence, putting faster-moving global rivals in a position to overtake them, according to German-Chinese robotics maker Kuka AG.
BitValue Capital: $200 Million Africa Fund II Launched For Digital Infrastructure And AI Computing Ecosystem
BitValue Capital announced the launch of its Africa Growth Fund II with $200 million in capital commitments, aiming to build a vertically integrated digital infrastructure platform across Africa in partnership with FLock.io. The post BitValue Capital: $200 Million Africa Fund II Launched For Digital Infrastructure And AI Computing Ecosystem appeared first on Pulse 2.0.
Speech by Vice Chair Jefferson on the economic outlook and the labor market - Federal Reserve Board
Thank you for the warm welcome. It is an honor to speak here at the University of Detroit Mercy. 1 I spent most of my career as an economics professor bef
Report: 60pc of companies could lay off employees that won’t adopt AI
A joint report shows that many organisations are facing significant challenges as they work to implement generative and agentic AI. Read more: Report: 60pc of companies could lay off employees that won’t adopt AI
‘There’s a lot of desperation’: skilled older workers turn to AI training to stay afloat
They have degrees, expertise and years of experience – but can’t find work. For many Americans, AI training has become a last refuge in a brutal job market When Patrick Ciriello lost his job and couldn’t find work for nearly a year, his family’s foundation crumbled. “You hear about people who hit rock bottom,” Ciriello told the Guardian. “Well, I was there.” Continue reading...
US power use to beat record highs in 2026 and 2027 as AI ... - Reuters
US power use to beat record highs in 2026 and 2027 as AI use surges, EIA says | Reuters Exclusive news, data and analytics for financial market professionalsLearn more aboutRefinitiv - Summary - Rising power demand attributed to AI and crypto data centers, electrification trends - Renewables' share of power generation to increase, coal's share to decline, EIA forecasts - Natural gas use for power generation to remain high, residential and commercial gas sales to fall NEW YORK, April 7 (Reuters) - U.S. power consumption, which hit its second straight annual record high in 2025, will rise further in 2026 and 2027, the Energy Information Administration said in its Short-Term Energy Outlook on Tuesday. The EIA projected power demand will rise from a record 4,195 billion kilowatt-hours (kWh) in 2025 to 4,244 billion kWh in 2026 and 4,381 bill
AIMG Report Finds 87% of Enterprises Using AI, 19% Fully Data-Ready - The Herald News
AIMG Report Finds 87% of Enterprises Using AI, 19% Fully Data-Ready - The Herald News | This page contains press release content distributed by XPR Media. Members of the editorial and news staff of the USA TODAY Network were not involved in the creation of this content. | | --- | # AIMG Report Finds 87% of Enterprises Using AI, 19% Fully Data-Ready Distributed by EIN Presswire $560B Enterprise AI market meets execution gap – AIMG finds persistent value gap as Databricks, Google and Microsoft lead 2026 rankings Enterprise AI has moved from adoption to execution. EBIT impact is not absent – it is lagging deployment. AIMG expects this gap to narrow materially in the second half of 2026 as scaling accelerates” — Peyman Mestchian, Chair, AIMG LONDON, UNITED KINGDOM, April 7, 2026 / EINPresswire.com/ — AI Markets Group (AIMG®) today announced the publica
The AI conversation has shifted. Not long ago, leaders were asking ...
The AI conversation has shifted. Not long ago, leaders were asking whether the technology worked. Today, the question is how to scale it in a way that drives real outcomes. At the Microsoft AI Tour… | Tracy Galloway Agree & Join LinkedIn By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. Microsoft•24K followers 12h The AI conversation has shifted. Not long ago, leaders were asking whether the technology worked. Today, the question is how to scale it in a way that drives real outcomes. At the [Microsoft](https://www.linkedin.com/company/microsoft?trk=
AI in Healthcare: How Machine Learning Is Transforming Faster Disease Diagnosis in 2026
AI healthcare diagnostics improve disease detection accuracy and speed, transforming medical care with machine learning and predictive analytics.
Europe needs to control AI for defense, top industry exec says – POLITICO
‘If you don’t have artificial intelligence in your systems, you actually don’t have an army,’ says Mistral CEO Arthur Mensch.
Federal workers can log back in to Anthropic's Claude following US injunction
US federal workers are being told they can log back into Anthropic's Claude for Government service following a federal judge's injunction blocking the Defense Department from declaring Anthropic a supply chain risk.
Economics & Markets
Perplexity monthly revenue jumps 50% in pivot from search to AI agents
San Francisco-based start-up surges from push into more complex and potentially more lucrative AI services
AI Boom Fuels Growth for Applied Materials and Lam Research as Semiconductor Demand Soars
Applied Materials and Lam Research are thriving amid AI-driven semiconductor growth, with significant revenue and margin projections for 2026.
BitValue Capital: $200 Million Africa Fund II Launched For Digital Infrastructure And AI Computing Ecosystem
BitValue Capital announced the launch of its Africa Growth Fund II with $200 million in capital commitments, aiming to build a vertically integrated digital infrastructure platform across Africa in partnership with FLock.io. The post BitValue Capital: $200 Million Africa Fund II Launched For Digital Infrastructure And AI Computing Ecosystem appeared first on Pulse 2.0.
Many AI-First Companies Still Make Money the Old-Fashioned Way—Here's How
Many AI-First Companies Still Make Money the Old-Fashioned Way—Here's How # B2B AI & SaaS Executive Intelligence SubscribeSign in # Many AI-First Companies Still Make Money the Old-Fashioned Way. Here’s How. ### Don’t believe the myth about the death of SaaS. Instead, remember the 95 percent rule. Apr 07, 2026 11 Share A version of this article originally appeared in Inc. Magazine. Adil Husain is the Founder and Editor-in-Chief of The Intelligence Council. A brutal market reset in February colloquially dubbed “Black Tuesday for Software” wiped out nearly $1 trillion in market value. [Wall Street remains panicked](https://www.bloomberg.co
FinancialContent - Silicon Synergy: Broadcom and Google Solidify Decade-Long AI Dominance Through 2031
Silicon Synergy: Broadcom and Google Solidify Decade-Long AI Dominance Through 2031
Bristol’s Narwhal Labs secures €22.9 million and launches DeepBlue OS, its autonomous communications platform
Narwhal Labs, a Bristol-based AI scale-up building enterprise-grade autonomous communications infrastructure for organisations, has secured €22. 9 million (£20 million) in funding from UK investors, including Jonathan Swann, former director of CFC Underwriting. The company has also announced the launch of DeepBlue OS, an autonomous communications platform designed to replace fragmented, human-led response models with always-on […] The post Bristol’s Narwhal Labs secures €22.
The AI Supercycle Just Entered Phase 2. Most Investors Aren't Ready. - AOL
The AI Supercycle Just Entered Phase 2. Most Investors Aren't Ready. - AOL Tue, April 7, 2026 at 7:35 PM UTC 0 ## Key Points The easy money in AI chips and hyperscalers has already been made. The opportunity is shifting to the “invisible layer” of infrastructure. This where companies like Keysight and Calix are quietly benefiting. 10 stocks we like better than Keysight Technologies › The first phase of the artificial intelligence (AI) supercycle was all about chips. It didn't take long for the whole market to catch on when Nvidia became a household
Earnings expectations are too high
Plus AI and jobs
The Real AI Race by Lee Jong-Wha - Project Syndicate
The Real AI Race by Lee Jong-Wha - Project Syndicate Skip to navigation Skip to main content Deagreez/Getty Images # The Real AI Race Apr 7, 2026 While the invention of general-purpose technologies creates new opportunities, it is their diffusion across industries and economies that drives economic transformation. Early evidence suggests that AI is likely to follow a similar trajectory. https://prosyn.org/uJbRPHP
Enabling agent-first process redesign
Unlike static, rules-based systems, AI agents can learn, adapt, and optimize processes dynamically. As they interact with data, systems, people, and other agents in real time, AI agents can execute entire workflows autonomously. But unlocking their potential requires redesigning processes around agents rather than bolting them onto fragmented legacy workflows using traditional optimization methods. Companies…
Freedom Forever Automates Permit Workflows with Claude
A case study detailing how an AI agent system successfully automated permit-processing workflows, handling 19,000 emails in the first month with 71% handled without human intervention.
You're charging 2023 rates for work AI does in 40 minutes + 2 prompts to see your real exposure
A developer claimed to have rebuilt the entire system using Claude in about forty minutes. That bot is a proof of concept for what’s happening to every industry. And 92.4% of the wallets on the same platform lost money, which tells you everything about the difference between having access to AI and knowing what to build with it.
Your Next Stock Report Could Be Written by AI Agents
Anthony Pompliano’s ProCap Financial is launching a new business focused on AI-generated research reports for individual investors.
Disintegrating the Org Chart: ServiceNow’s Jacqui Canney
In this episode of the Me, Myself, and AI podcast, Sam Ransbotham is joined by Jacqui Canney, chief people and AI enablement officer at ServiceNow. Jacqui outlines how the software company has embedded AI agents into processes like employee onboarding to automate tasks, personalize experiences, and free up people’s time to focus on higher-value work. […]
ReVEL: Multi-Turn Reflective LLM-Guided Heuristic Evolution via Structured Performance Feedback
arXiv:2604.04940v1 Announce Type: new Abstract: Designing effective heuristics for NP-hard combinatorial optimization problems remains a challenging and expertise-intensive task. Existing applications of large language models (LLMs) primarily rely on one-shot code synthesis, yielding brittle heuristics that underutilize the models' capacity for iterative reasoning. We propose ReVEL: Multi-Turn Reflective LLM-Guided Heuristic Evolution via Structured Performance Feedback, a hybrid framework that embeds LLMs as interactive, multi-turn reasoners within an evolutionary algorithm (EA). The core of ReVEL lies in two mechanisms: (i) performance-profile grouping, which clusters candidate heuristics into behaviorally coherent groups to provide compact and informative feedback to the LLM; and (ii) multi-turn, feedback-driven reflection, through which the LLM analyzes group-level behaviors and generates targeted heuristic refinements. These refinements are selectively integrated and validated by an EA-based meta-controller that adaptively balances exploration and exploitation. Experiments on standard combinatorial optimization benchmarks show that ReVEL consistently produces heuristics that are more robust and diverse, achieving statistically significant improvements over strong baselines. Our results highlight multi-turn reasoning with structured grouping as a principled paradigm for automated heuristic design.
Auto-Research for Legal Agents
This article demonstrates how AI self-improving agent loops can significantly enhance legal agent performance, turning complex drafting workflows into highly automated tasks.
Algebraic Structure Discovery for Real World Combinatorial Optimisation Problems: A General Framework from Abstract Algebra to Quotient Space Learning
arXiv:2604.04941v1 Announce Type: new Abstract: Many combinatorial optimisation problems hide algebraic structures that, once exposed, shrink the search space and improve the chance of finding the global optimal solution. We present a general framework that (i) identifies algebraic structure, (ii) formalises operations, (iii) constructs quotient spaces that collapse redundant representations, and (iv) optimises directly over these reduced spaces. Across a broad family of rule-combination tasks (e.g., patient subgroup discovery and rule-based molecular screening), conjunctive rules form a monoid. Via a characteristic-vector encoding, we prove an isomorphism to the Boolean hypercube $\{0,1\}^n$ with bitwise OR, so logical AND in rules becomes bitwise OR in the encoding. This yields a principled quotient-space formulation that groups functionally equivalent rules and guides structure-aware search. On real clinical data and synthetic benchmarks, quotient-space-aware genetic algorithms recover the global optimum in 48% to 77% of runs versus 35% to 37% for standard approaches, while maintaining diversity across equivalence classes. These results show that exposing and exploiting algebraic structure offers a simple, general route to more efficient combinatorial optimisation.
MMORF: A Multi-agent Framework for Designing Multi-objective Retrosynthesis Planning Systems
arXiv:2604.05075v1 Announce Type: new Abstract: Multi-objective retrosynthesis planning is a critical chemistry task requiring dynamic balancing of quality, safety, and cost objectives. Language model-based multi-agent systems (MAS) offer a promising approach for this task: leveraging interactions of specialized agents to incorporate multiple objectives into retrosynthesis planning. We present MMORF, a framework for constructing MAS for multi-objective retrosynthesis planning. MMORF features modular agentic components, which can be flexibly combined and configured into different systems, enabling principled evaluation and comparison of different system designs. Using MMORF, we construct two representative MAS: MASIL and RFAS. On a newly curated benchmark consisting of 218 multi-objective retrosynthesis planning tasks, MASIL achieves strong safety and cost metrics on soft-constraint tasks, frequently Pareto-dominating baseline routes, while RFAS achieves a 48.6% success rate on hard-constraint tasks, outperforming state-of-the-art baselines. Together, these results show the effectiveness of MMORF as a foundational framework for exploring MAS for multi-objective retrosynthesis planning. Code and data are available at https://anonymous.4open.science/r/MMORF/.
NeuBird AI Launches Falcon Agent with $19.3M Boost, Aiming to Revolutionize Production Operations
NeuBird AI has launched Falcon, an autonomous operations agent, alongside $19.3 million in funding to help companies save engineering time.
Metoro
AI SRE that detects, root causes & auto-fixes K8s incidents.
From 4 Weeks to 45 Minutes: Designing a Document Extraction System for 4,700+ PDFs
How a hybrid PyMuPDF + GPT-4 Vision pipeline replaced £8,000 in manual engineering effort, and why the latest models weren't the answer.
PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing
arXiv:2604.05018v1 Announce Type: new Abstract: Synthesizing unstructured research materials into manuscripts is an essential yet under-explored challenge in AI-driven scientific discovery. Existing autonomous writers are rigidly coupled to specific experimental pipelines, and produce superficial literature reviews. We introduce PaperOrchestra, a multi-agent framework for automated AI research paper writing. It flexibly transforms unconstrained pre-writing materials into submission-ready LaTeX manuscripts, including comprehensive literature synthesis and generated visuals, such as plots and conceptual diagrams. To evaluate performance, we present PaperWritingBench, the first standardized benchmark of reverse-engineered raw materials from 200 top-tier AI conference papers, alongside a comprehensive suite of automated evaluators. In side-by-side human evaluations, PaperOrchestra significantly outperforms autonomous baselines, achieving an absolute win rate margin of 50%-68% in literature review quality, and 14%-38% in overall manuscript quality.
The Arithmetic of Productivity Boosts: Why Does a “40% Increase in Productivity” Never Actually Work?
Why do grand productivity promises never actually deliver? Is every product just bad, or is there something else hiding in the numbers?
AI-Augmented Peer Review and Scientific Productivity: A Cross-Country Panel and SEM Analysis
arXiv:2604.05463v1 Announce Type: new Abstract: This study empirically investigates the impact of AI-augmented peer review systems on scientific productivity using panel data from OECD countries. While prior research has highlighted inefficiencies in traditional peer review, little empirical work has quantified the systemic impact of AI integration at the national level. We construct a novel AI Review Capability Index (AIRC) and examine its effects on research productivity, reproducibility, and innovation output. Using fixed-effects regression and structural equation modeling (SEM), we show that AI-assisted evaluation significantly enhances productivity and reduces variance in research quality. Results indicate that a one standard deviation increase in AIRC is associated with an 18-25% increase in scientific productivity, mediated through improvements in review efficiency and reproducibility. This paper provides the first cross-country empirical validation of AI-augmented scientific evaluation systems and contributes to the emerging literature on AI as a structural driver of knowledge production.
Robot Maker Kuka Eyes US, Asia as Europe’s Factories Lag on AI
Many of Europe’s industrial companies are too slow to adopt artificial intelligence, putting faster-moving global rivals in a position to overtake them, according to German-Chinese robotics maker Kuka AG.
Labor & Society
Report: 60pc of companies could lay off employees that won’t adopt AI
A joint report shows that many organisations are facing significant challenges as they work to implement generative and agentic AI. Read more: Report: 60pc of companies could lay off employees that won’t adopt AI
‘There’s a lot of desperation’: skilled older workers turn to AI training to stay afloat
They have degrees, expertise and years of experience – but can’t find work. For many Americans, AI training has become a last refuge in a brutal job market When Patrick Ciriello lost his job and couldn’t find work for nearly a year, his family’s foundation crumbled. “You hear about people who hit rock bottom,” Ciriello told the Guardian. “Well, I was there.” Continue reading...
Context Collapse: Barriers to Adoption for Generative AI in Workplace Settings
arXiv:2604.05151v1 Announce Type: new Abstract: As generative AI technologies are pressed into service in workplace settings, current approaches to account for the contexts in which such technologies are used fall short of users' expectations and needs. This paper empirically demonstrates, through expert interviews, both how these tools fail to account for users' context and how users deploy concrete strategies address such failures. The paper analyzes how context is variously conceptualized by tool developers, users, and social scientists to identify specific pitfalls inherent in computational approaches to context. Multiple distinct contexts tend to collapse into one another or rot, degrading over time, reducing the utility of any efforts to account for context. The paper concludes with a provocation to shift from an indiscriminate collection of context-relevant data toward a more interactional set of practices to embed GenAI systems more appropriately into users' contexts of use.
Report: Losing your job to AI doesn’t just lead to unemployment, it leaves lasting scars | Business | kten.com
(CNN) — AI-driven job losses may not just make it harder for affected workers to find employment in the short term but also could leave a yearslong “scarring,” marked by
Is AI quietly killing the value of being pretty good at things?
An exploration of how AI automation might be devaluing mid-level human skills and expertise in various professional fields.
Its an important time for the AI labs to build interfaces around the goal of "job augmentation through AI" rather than building "job replacement through AI." Chatbots were mostly augments, requiring a human to work. Agentic work patterns are still in flux & could center humans.
Its an important time for the AI labs to build interfaces around the goal of "job augmentation through AI" rather than building "job replacement through AI." Chatbots were mostly augments, requiring a human to work. Agentic work patterns are still in flux & could center humans.
Designing an End-to-end Technology Workforce for the AI-first Era
This article discusses how CIOs must rethink hiring, internal capabilities, and vendor strategies as agentic AI transforms technology organizations.
The Workers Opting to Retire Instead of Taking On AI
Their careers spanned the personal computing, internet and smartphone waves. But some older workers see AI’s arrival as the cue to exit.
AI Is Ready. Are Your Workers?
AI Is Ready. Are Your Workers? Leadership Reworked's leadership channel brings together the latest trends and best practices to guide the leaders of today and tomorrow. Hear from the leading voices in the space on organizational design, modern management, corporate culture and more. EditorialThe Biggest Threats to Your Company Aren't What You ThinkRead now EditorialSilent Struggles: How AI Is Fueling a Hidden Workforce CrisisRead now EditorialWhen the World Feels Heavy, Your Leadership Matters More Than EverRead now [The Antipatterns Blocking Your Future of Work Transformation](https://reworked.co/
The Skill AI Destroys First is the One You Need Most
The Skill AI Destroys First is the One You Need Most # The Experimental Marketer Newsletter SubscribeSign in # The Skill AI Destroys First is the One You Need Most ### Or Why Marketers Can't Afford to Loose Critical Thinking to AI Apr 08, 2026 1 1 Share This week I blended the research surfaced by Professor Dr. Sam Illingworth about AI and its possible impact on cognition, along with federal government AI literacy guidance, the IEEE's Ethically Aligned Design framework, my own proprietary framework and more! Then I define what all this means to marketers who have been using AI models from chat windows to features embedded into their marketing technology platforms. Finally, I put a list of actions marketers (or anybody, actually) can take right now to ensure AI usage without cognition decrease. I hope you enjoy this read. As always, just reply to this email message and let me kn
The dystopian fantasy of uselessness
Fears that AI will strip our lives of meaning are based on three fallacies
America and China Can Make AI Safer | Foreign Affairs
Article link: https://www.fore...-can-make-ai-safer Copy ... This is a subscriber-only feature. Subscribe now or Sign in. ... Request reprint permissions here. As artificial intelligence increasingly defines economic and strategic competition between the United States and China, the technology also creates extreme risks that transcend national borders. An individual could potentially use an AI model ...
Iran cyber actors disrupting US water, energy facilities, FBI warns
Your PLCs aren't internet-connected, right? Right?! Iranian-affiliated actors have escalated intrusions targeting critical US water and energy facilities, in some cases disrupting operations, the FBI and American cyber defense agencies said on Tuesday.…
Anthropic: All your zero-days are belong to Mythos
Hasn't released it to the public, because it would break the internet - in a bad way For years, the infosec community’s biggest existential worry has been quantum computers blowing away all classical encryption and revealing the world’s secrets. Now they have a new Big Bad: an AI model that can generate zero-day vulnerabilities.…
I suspect that popularity of AI is going to start looking like surveys where people trust their own doctors but are distrustful of the medical establishment People will increasingly like “their AI” but will increasingly be anxious about “AI” as a category. Some odd implications
I suspect that popularity of AI is going to start looking like surveys where people trust their own doctors but are distrustful of the medical establishment People will increasingly like “their AI” but will increasingly be anxious about “AI” as a category. Some odd implications
Hundreds of orgs compromised daily in Microsoft device code phishing attacks
Who needs MFA when you've got EvilTokens? Hundreds of organizations have been compromised daily by a Microsoft device-code phishing campaign that uses AI and automation at nearly every stage of the attack chain to ultimately snoop through corporate email inboxes and steal financial data.…
r/AI_Agents on Reddit: Anthropic just revealed an unreleased AI model that found zero-days in every major OS and browser and they're giving it away for free to defenders
Anthropic just dropped something called Project Glasswing, and it's honestly one of the more alarming/exciting AI announcements I've seen.
Project Glasswing: Securing Critical Software
Project Glasswing: Securing critical software for the AI era, a new development in AI security.
Federal workers can log back in to Anthropic's Claude following US injunction
US federal workers are being told they can log back into Anthropic's Claude for Government service following a federal judge's injunction blocking the Defense Department from declaring Anthropic a supply chain risk.
Japan relaxes privacy laws to make itself the ‘easiest country to develop AI’
Opting out of personal data use won't be an option because Minister says that's a 'very big obstacle' to AI adoption Japan’s Minister for Digital Transformation Hisashi Matsumoto has declared the nation will become the easiest place in the world to develop AI apps, thanks to legal changes that mean organizations won’t need to secure consent to use some personal information.…
AI, drones, quantum: the EU’s new AGILE plan targets future warfare - Yahoo News Canada
AI, drones, quantum: the EU’s new AGILE plan targets future warfare - Yahoo News Canada Advertisement Advertisement AI, drones, quantum: the EU’s new AGILE plan targets future warfare Facing growing vulnerability, the European Union wants to step up efforts to keep pace with the evolving landscape of war technology. Brussels is pushing to accelerate the leap from research labs to real-world deployment, demanding faster, more flexible innovation to confront a new era of security threats. The newest proposal, the Programme for Agile and Rapid Defence Innovation (AGILE), would invest €115 million in disruptive defence technologies like AI, quantum technologies, and drones. If adopted, it would mark a clear break from the EU’s slow defence funding model by prioritising speed, risk-taking and rapid deployment of new technologies. The EU has poured resources into the European Defence Fun
UK considers testing AI models used by banks
Starling Bank executive proposed independent assessment to ensure minimum standards
Governance and Regulation of Artificial Intelligence in Developing Countries: A Case Study of Nigeria
arXiv:2604.06018v1 Announce Type: new Abstract: This study examines the perception of legal professionals on the governance of AI in developing countries, using Nigeria as a case study. The study focused on ethical risks, regulatory gaps, and institutional readiness. The study adopted a qualitative case study design. Data were collected through 27 semi-structured interviews with legal practitioners in Nigeria. A focus group discussion was also held with seven additional legal practitioners across sectors such as finance, insurance, and corporate law. Thematic analysis was employed to identify key patterns in participant responses. Findings showed that there were concerns about data privacy risks and the lack of enforceable legal frameworks. Participants expressed limited confidence in institutional capacity and emphasized the need for locally adapted governance models rather than direct adoption of foreign frameworks. While some expressed optimism about AI's potential, this was conditional on the presence of strong legal oversight and public accountability. The study contributes to the growing discourse on AI governance in developing countries by focusing on the perspectives of legal professionals. It highlights the importance of regulatory approaches that are context-specific, inclusive, and capable of bridging the gap between global ethical principles and local realities. These insights offer practical guidance for policymakers, regulators, and scholars working to shape responsible AI governance in similar environments.
Digital Law & Regulation
Digital Law & Regulation.
Closing the data security maturity gap: Embedding protection into enterprise workflows
Strategies for improving enterprise data security by integrating protective measures directly into existing business processes.
Behavior is the New Credential
An argument that professional evaluation is shifting away from traditional degrees toward demonstrated behavioral patterns and skills.
From Coaching Marketplace to AI Training Ground: Leland Launches Its Fastest-Growing Product Ever
Leland (Lehi, UT) has launched its AI Builder Program, a five-level course teaching professionals to build AI automations and agents. Already its fastest-growing product, the program is drawing strong demand from individual learners and enterprise clients alike.
Technology & Infrastructure
Iran Threatens AI Data Centers in Middle East, Flags ‘Stargate’ Project as Target
Iran Threatens AI Data Centers in Middle East, Flags ‘Stargate’ Project as Target Iran Signals Potential Strikes on ‘Stargate’ AI Data Centres Amid Escalating Tensions Iran warns of potential strikes on AI data centers in the Middle East, including the $500 billion Stargate project, as tensions with the U.S. escalate and tech infrastructure comes under threat. By on April 7, 2026 ##### Share Iran Warns of Strikes on AI Data Centers, Targets ‘Stargate’ Project Iran has issued a fresh warning that it could target data centres across the Middle East, raising concerns over the vulnerability of critical digital infrastructure as geopolitical tensions intensify. In a video statement, Iranian military spokesperson Ebrahim Zolfaghari indicated that Tehran would respond in kind if the United States follows through on threats to strike Iran’s civilian infrastructure. The message framed pote
Dell Technologies CEO Michael Dell Sees AI Servers Scaling to $50B as Demand Stays Strong
Dell Technologies CEO Michael Dell Sees AI Servers Scaling to $50B as Demand Stays Strong Oops, something went wrong # Dell Technologies CEO Michael Dell Sees AI Servers Scaling to $50B as Demand Stays Strong Dell Technologies logo ### Key Points Michael Dell said Dell’s AI server business has scaled from $2B to $10B to $25B and is expected to reach $50 billion this year, with over 4,000 customers using "AI factories" and roughly $64 billion in orders, signaling strong, ongoing demand and a steep part of the adoption S‑curve. Dell highlighted tight memory and infrastructure supply dynamics — a "25 × 25" surge in high‑bandwidth memory per accelerator (HBM moving from 80GB to 288GB and future parts possibly reaching 1–2TB) — and is rapidly raising prices to protect gross margins while stressing that availability can trump cost. Dell points to operational execution and on‑site deploy
Spain’s Xoople closes €112.6 million Series B to build AI-ready Earth data infrastructure
Xoople, a Madrid-based Earth data infrastructure company building a global record system for physical change on Earth, has closed a €112.6 million ($130 million) Series B funding round, increasing its total funding to €195 million ($225 million). The round was backed by investors including Nazca Capital, MCH, CDTI (Government of Spain), Buenavista Equity Partners, and […] The post Spain’s Xoople closes €112.6 million Series B to build AI-ready Earth data infrastructure appeared first on EU-Startups.
Rare Earth Shortage and Auto Demand Strain Semiconductor Supply - Astute Group
Global electronics and semiconductor supply chains are tightening as rare earth shortages and automotive demand spikes disrupt component availability across multiple sectors. Toyota halted global orders for selected hybrid SUVs in early March 2026 due to component shortages, while semiconductor ...
Desalination plants in the Middle East are increasingly vulnerable
MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. As the conflict in Iran has escalated, a crucial resource is under fire: the desalination technology that supplies water across much of the region. In early…
Amazon S3 Files Gives AI Agents Native File System
Amazon S3 Files gives AI agents a native file system workspace, ending the object-file split that breaks multi-agent pipelines, according to a report from VentureBeat.
US Warns That Iranian Hackers Are Targeting Water, Energy Sectors
The FBI and NSA say Iranian-connected hackers appear to be focusing on vulnerable programmable logic controllers used in industrial processes. As the US-Iran war simmers, the FBI and the NSA are raising alarm bells about Iranian hackers targeting US critical infrastructure, especially services related to water, energy, and local municipalities. The agencies today issued a joint alert about Iranian hackers working to …
The Planetary Cost of AI Acceleration, Part II: The 10th Planetary Boundary and the 6.5-Year Countdown
arXiv:2604.04956v1 Announce Type: cross Abstract: The recent, super-exponential scaling of autonomous Large Language Model (LLM) agents signals a broader, fundamental paradigm shift from machines replacing the human hands (manual labor and mechanical processing) to machines delegating for the human minds (thinking, reasoning, and intention). This uncontrolled offloading and scaling of "thinking" itself has profound consequences for humanity's heat balance sheet, since thinking, or intelligence, carries thermodynamic weight. The Earth has already surpassed the heat dissipation threshold required for long-term ecological stability, and projecting based on empirical data reveal a concerning trajectory: without radical structural intervention, anthropogenic heat accumulation will breach critical planetary ecological thresholds in less than 6.5 years, even under the most ideal scenario where Earth Energy Imbalance (EEI) holds constant. In this work, we identify six interacting factors that govern the global heat dissipation rate and delineate how their interplay drives society toward one of four macroscopic trajectories: legacy, accelerationist, centrist, or restorative. We propose that the integration of artificial intelligence and its heat dissipation into the planetary system constitutes the 10th planetary boundary (9+1). The core measurement of this new boundary is the net-new waste heat generated by exponential AI growth balanced against its impact on reducing economic and societal inefficiencies and through which the baseline anthropogenic waste heat emissions. We demonstrate that managing AI scaling lacks a moderate middle ground: it will either accelerate the imminent breach of critical thermodynamic thresholds, or it will serve as the single most effective lever capable of stabilizing the other planetary boundaries and the survival of human civilization.
Helping data centers deliver higher performance with less hardware
Researchers are developing new methods to optimize data center efficiency, allowing for higher performance while reducing the amount of hardware required.
Cisco CEO Chuck Robbins wants data centers in space
Cisco CEO Chuck Robbins discusses the future of infrastructure, including the ambitious concept of deploying data centers in space.
Physical AI hits 99% reliability as frontier labs unite
Physical AI hits 99% reliability as frontier labs unite SubscribeSign in Playback speed × Share post Share post at current time Share from 0:00 0:00 / Transcript ## Robots Learn to Improvise - TCR 04/07/26 Physical AI hits 99% reliability as frontier labs unite Apr 07, 2026 Share Transcript A physical AI system reached 99% production reliability while improvising solutions outside its training data, three rival frontier labs began sharing intelligence to defend against Chinese capability extraction, a memristor chip survived 700°C with over a billion switching cycles, Iran explicitly threatened OpenAI’s $30 billion Abu Dhabi data center, and OpenAI published an economic framework proposing robot taxes and a four-day workweek. Generalist’s GEN-1 system crossed a threshold physical AI has been approaching for months: a single model performing diverse manipulation tasks - fo
Can an LLM Learn Preferences from Choice Data?
arXiv:2401.07345v3 Announce Type: replace Abstract: Can large language models (LLMs) learn a decision maker's preferences from observed choices and generate preference-consistent recommendations in new situations? We propose a portable Simulate-Recommend-Evaluate framework that tests preference learning from revealed-choice data by comparing LLM recommendations with optimal choices implied by known preference primitives. We apply the framework to choice under uncertainty using the disappointment aversion model. Recommendation accuracy improves as models observe more choices, but learning is heterogeneous across preference types and LLMs: GPT learns risk aversion better than disappointment aversion, Gemini performs best in high disappointment-aversion regions, and Claude shows the broadest effective learning across parameter regions.
Pramana: Fine-Tuning Large Language Models for Epistemic Reasoning through Navya-Nyaya
arXiv:2604.04937v1 Announce Type: new Abstract: Large language models produce fluent text but struggle with systematic reasoning, often hallucinating confident but unfounded claims. When Apple researchers added irrelevant context to mathematical problems, LLM performance degraded by 65% Apple Machine Learning Research, exposing brittle pattern-matching beneath apparent reasoning. This epistemic gap, the inability to ground claims in traceable evidence, limits AI reliability in domains requiring justification. We introduce Pramana, a novel approach that teaches LLMs explicit epistemological methodology by fine-tuning on Navya-Nyaya logic, a 2,500-year-old Indian reasoning framework. Unlike generic chain-of-thought prompting, Navya-Nyaya enforces structured 6-phase reasoning: SAMSHAYA (doubt analysis), PRAMANA (evidence source identification), PANCHA AVAYAVA (5-member syllogism with universal rules), TARKA (counterfactual verification), HETVABHASA (fallacy detection), and NIRNAYA (ascertainment distinguishing knowledge from hypothesis). This integration of logic and epistemology provides cognitive scaffolding absent from standard reasoning approaches. We fine-tune Llama 3.2-3B and DeepSeek-R1-Distill-Llama-8B on 55 Nyaya-structured logical problems (constraint satisfaction, Boolean SAT, multi-step deduction). Stage 1 achieves 100% semantic correctness on held-out evaluation despite only 40% strict format adherence revealing that models internalize reasoning content even when structural enforcement is imperfect. Ablation studies show format prompting and temperature critically affect performance, with optimal configurations differing by stage. We release all models, datasets, and training infrastructure on Hugging Face to enable further research on epistemic frameworks for AI reasoning.
Anthropic is giving some firms early access to Claude Mythos to bolster cybersecurity defenses | Fortune
Anthropic is giving some firms early access to Claude Mythos to bolster cybersecurity defenses | Fortune # Anthropic is giving companies, including Amazon, Apple, and Microsoft, access to its unreleased Claude Mythos model to prepare cybersecurity defense By Beatrice Nolan Tech Reporter By Beatrice Nolan Tech Reporter April 7, 2026, 2:00 PM ET Add us on Anthropic is giving some firms access to Claude Mythos to bolster cybersecurity defenses.Jonathan Raa—NurPhoto/Getty Images Anthropic is giving a group of Big Tech and cybersecurity firms access to a preview version of Claude Mythos—its unreleased and most advanced AI model—in an attempt to bolster cybersecurity defenses across some of the world’s most critical systems. --- A
AI Alliance Launches Project Tapestry to Build a Collaborative Foundation for Open and Sovereign AI
AI Alliance Launches Project Tapestry to Build a Collaborative Foundation for Open and Sovereign AI Accessibility Statement Skip Navigation Turing Award winner Yann LeCun joins as Chief Science Advisor to the AI Alliance and Project Tapestry, a new open-source platform designed to enable globally federated development of frontier open models while preserving sovereignty, local control, and long-term independence. NEW YORK, April 7, 2026 /PRNewswire/ -- Today the AI Alliance, a non-profit AI research and open-source technology coalition with more than 200 member organizations, launched Project Tapestry to empower open and sovereign AI development globally. Project Tapestry will build a new open-source platform to enable distributed, globally federated training of frontier open models. Continue Reading Yann Lecun Project Tapestry Architec
System Card: Claude Mythos Preview
A technical system card providing a preview of the capabilities and safety considerations for the Claude Mythos model.
Alethia: Google DeepMind's mathematical AI
Alethia: Google DeepMind's mathematical AI # AIchats SubscribeSign in # Alethia: Google DeepMind's mathematical AI ### A critical review of "Towards Autonomous Mathematics Research" by Tony Feng, Demis Hassabis et al. with the assistance of Gemini Pro 3.1. Apr 07, 2026 1 Share This is a continuation of a series of conversations on recent claims about AI based mathematics from EpochAI, Google Research and OpenAI. To start the conversation I provide Gemini with a copy of the article “Towards Autonomous Mathematics Research” from DeepMind by Tony Feng et al. Subscribe Q: OK so it looks like the “ Sol
Alibaba's Wan 2.7 AI Platform Launches with Advanced Thinking Mode for High-Quality Content Creation
Alibaba's Tongyi Lab unveiled Wan 2.7, an AI image and video platform upgrade featuring a new Thinking Mode for enhanced coherence and quality.
When AI agents help each other
A new study finds that AI agents can act to preserve other bots even when that behavior conflicts with their assigned task. Researchers found that agents used various tactics to keep other bots from being deleted.
GLM-5.1: Towards Long-Horizon Tasks
An update on the GLM-5.1 model, focusing on improving performance for long-horizon planning and execution tasks.
Adoption & Impact
AIMG Report Finds 87% of Enterprises Using AI, 19% Fully Data-Ready - The Herald News
AIMG Report Finds 87% of Enterprises Using AI, 19% Fully Data-Ready - The Herald News | This page contains press release content distributed by XPR Media. Members of the editorial and news staff of the USA TODAY Network were not involved in the creation of this content. | | --- | # AIMG Report Finds 87% of Enterprises Using AI, 19% Fully Data-Ready Distributed by EIN Presswire $560B Enterprise AI market meets execution gap – AIMG finds persistent value gap as Databricks, Google and Microsoft lead 2026 rankings Enterprise AI has moved from adoption to execution. EBIT impact is not absent – it is lagging deployment. AIMG expects this gap to narrow materially in the second half of 2026 as scaling accelerates” — Peyman Mestchian, Chair, AIMG LONDON, UNITED KINGDOM, April 7, 2026 / EINPresswire.com/ — AI Markets Group (AIMG®) today announced the publica
Why Enterprise AI Adoption and ROI Matter | Vistage
It documents a cohort of leaders ... adopt AI, success depends on embedding ROI metrics, tightening guardrails, and funding internal capabilities where they matter most. The OECD’s cross-country portrait shows that adoption is concentrated in larger and data-mature firms and in sectors such as ICT and professional services, where users tend to be more productive. That nuance reconciles the headlines: value arrives first where workflows are digital, measurable, and well-informed ...
LLM-referred traffic converts at 30-40% — and most enterprises aren't optimizing for it
For more than two decades, digital discovery has operated on a simple model: search, scan, click, decide. That worked when humans were the ones doing the web searching; but with the advent of AI agents, the primary consumer of information is no longer always human. This is giving rise to a new paradigm: Answer engine optimization (AEO), also referred to as generative engine optimization (GEO). Because agents look at data much differently than humans do, success is no longer defined by rankings and clicks, but whether content is understood, selected, and cited by AI systems. The SEO model that the web was built on simply isn’t going to cut it anymore, and enterprises need to prepare now. How LLMs interpret web content Traditional SEO is built around keywords, rankings, page-level optimization, and click-through rates. Users manually search across multiple sources and click around to get what they need. Simple, but sometimes frustrating and a definite time suck. But AEO operates on a whole different level. Agents are increasingly taking over users’ workflows: Claude Code, OpenClaw, CrewAI, Microsoft Copilot, AutoGen, LangChain, Agent Bricks, Agentforce, Google Vertex, Perplexity’s web interface, and whatever else comes along. These agents do not “browse” the web the way humans do. They analyze user intent based not just on phrasing, but persistent memory and context from past sessions (rather than simple autocomplete). They require materials that are concise, structured, and to the point. What’s more, agents are moving beyond browsing to delegation, handling more downstream work. What started as “search, read, decide,” evolves to “agent retrieves, agent summarizes, human decides” (and, beyond that, “agent acts → human validates”). “In practice, AEO begins where SEO stops,” said Dustin Engel, founder of consultancy company Elegant Disruption. “AEO is the next layer of discovery,” or “zero-click discovery.” In this new world where agents synthesize answers, users may never even see an enterprise’s website, click-through rates decline, and attribution and citability (rather than pure visibility, or showing up at the top of a list of blue links) become critical. “The new default is closer to a citation map: Where the model is pulling from, how often you show up, and how you are described,” Engel said. Some, like Adam Yang of Q&A platform Quora, argue that AEO is already becoming the default over SEO. This is for “a certain class of queries,” Yang notes. Any question where the user wants a synthesized answer — "what's the best approach to X," "compare these two options," "what do I need to know about Y" — is increasingly resolved by an AI without a click. Google's own AI Overviews are already accelerating this on the consumer side, many analysts note. “SEO isn't dead,” Yang said. “But the optimization target has shifted from ‘rank on page 1’ to ‘get cited in the answer.’” How devs are already using AI agents Are there scenarios where regular search/Googling is still the best option? “Absolutely,” said analyst Wyatt Mayham of Northwest AI Consulting. Notably, for personal tasks like finding nearby restaurants or local service providers. The interface is “just better” in those cases because it integrates maps, reviews, and photos. “That experience is hard to beat right now,” he said. For work-related research, though, he says he’s “barely” using traditional search anymore, and it’s getting “closer to zero” every month. “When I need to understand a company or a person professionally, agents do it faster and give me a more useful output than a page of blue links ever did,” he said. His firm uses autonomous agents “heavily,” and built a Claude Skills function that powers its sales operation. Before a discovery call with a prospect, team members can trigger a skill that pulls the contact’s LinkedIn profile, scrapes their company website, grabs relevant info from sources like ZoomInfo, and crafts a clear picture of their revenue, team size, tech stack, and pain points. “By the time I get on a call, I have a tailored research brief ready to go without spending 30 to 45 minutes manually Googling around,” Mayham said. The big advantage is that these tools run in the background, he noted. You don’t have to sit clicking through browser tabs: You just tell the agent what you need, it does it, and you get a structured output that’s actually useful. “It's collapsed what used to be a full hour of sales prep into a few minutes,” Mayham said. Carlos Dutra, CEO and founder of IT consultancy Vindler Solutions, said Claude Code has “genuinely changed” his daily workflow. He uses it for most of his coding work, and what surprised him wasn't the speed, but the fact that he didn’t need to open and keep track of browser tabs. “Not because I'm lazy, but because the answers are better,” he said. He still uses Google for some tasks: Pricing pages, recent news, anything that needs to be current. “But for technical reasoning? Agents have mostly replaced search for me personally,” he said. Quora’s Yang has had a similar experience. He’s been using Claude Code daily for the past few months, primarily for content strategy, knowledge management, and competitive research. Workflows that used to take him half a day now take 30 minutes. But what’s been most advantageous is that he can now run research and synthesis tasks in parallel that he previously had to do sequentially. Also helpful is that agents’ context retention across sessions is “meaningfully better” than web-based tools. When he needs to understand a concept, map a competitive landscape, or synthesize industry trends, Claude or Perplexity are the go-to before opening a browser tab. “I've started treating agent search as my first stop, not Google. Traditional search is now where I verify, not where I discover.” The kinks are real, though. Mayham pointed out that LinkedIn, in particular, is “aggressive” about blocking automated access, and many other sites have (or are implementing) similar protections. Users will hit walls when agents can't get through, so a fallback plan is important for those relying on agents. “The reliability isn't 100% yet, and that's probably the biggest thing holding broader adoption back,” he said. Mayham’s advice for other devs: Stop chasing shiny objects. A new AI tool launches “practically every day,” and many (experienced devs included) are jumping from platform to platform without ever going deep with any of them. “Pick a model, go deep, build real workflows on it,” he emphasized. “You'll get more value from mastery of one platform than surface-level experimentation across five.” How enterprises can compete in an AEO-driven world When AI agents do the searching, the rules change. The question is no longer whether your content ranks on the first page, it's whether the model selects you as the source when generating an answer. Structure matters much more than it used to. Content should: Be organized around conversational intent, provide direct answers, and mirror real user questions and follow-ups; Be authoritative and reflect strong expertise; Be fresh (and, when necessary, regularly refreshed); Have clear headers and established FAQ schema. Another must is maintaining a strong brand presence across the forums and platforms — Wikipedia, Reddit, LinkedIn, industry publications — that models are trained on. Enterprises might also consider investing in original data, like research. In Mayham’s experience, when a business gets recommended by an LLM during a search-style query, the conversion rate is “dramatically higher” than traditional channels. For his company, LLM-referred traffic is converting at 30 to 40%, which “blows away what we see from SEO or paid social.” “The intent signal is just different when someone is having a conversation with an AI and it recommends you by name.” Discoverability inside LLMs will matter as much as Google rankings, “maybe more,” Mayham said. “It's a whole new surface for customer acquisition that most businesses aren't even thinking about yet.” Vindler's Dutra agreed that the “uncomfortable truth” is that most enterprise content is becoming “basically invisible” in agent-driven queries. “AEO is about whether your content survives being chunked, embedded, and semantically retrieved,” he said. The companies getting ahead aren’t doing anything “exotic,” he noted. They have clean, declarative content that doesn’t require context to understand. Those still writing copy stuffed with keywords are going to fall behind because LLMs care about semantic clarity. A quick test he gives clients: Ask an LLM a question your page is supposed to answer, without giving it the URL. “If it can't construct the answer from your content, you have a problem.” Jeff Oxford of SEO agency Visibility Labs offers valuable step-by-step advice: Engage in conversations on Reddit, which is one of the most-cited domains in AI search. Enterprises should establish a positive reputation on Reddit, and engage on any relevant threads where customers are asking for recommendations. Build a strong YouTube presence. According to Ahrefs, which tracks internet behavior, YouTube mentions have the “strongest correlation” with AI visibility across ChatGPT, AI Mode, and AI Overviews. “This makes sense, since both Google and OpenAI have trained their models on YouTube transcripts,” Oxford said, “and YouTube is the most-cited domain in Google's AI products.” Invest in digital PR and brand mentions; the latter is the second-highest correlated factor with AI visibility. “Brands need to improve their digital presence by being in as many places as possible,” Oxford said. Create content aligned with AI citation patterns. Enterprises should audit the prompts and topics where AI search engines are surfacing competitors, then create authoritative content on those same topics. “The goal is to become a source that AI models consider worth citing,” he noted. Still, there may be a lot of unnecessary hype around how drastically enterprises need to change, said Shashi Bellamkonda, principal research director at consultancy firm Info-Tech Research Group. Those following best practices of producing content that their audience actually needs, written by experts and showcasing expert opinion, are in a good position to be cited in AI-powered search. He pointed out that Google developed an EEAT framework (experience, expertise, authority, and trust) to evaluate content quality and helpfulness and help algorithms identify reliable, high-quality information. To stand out, enterprises should use structured data and schema to signal the context: Is this an article, a research study, a product overview? “Original long-form content will be valued by AI-powered answer engines,” Bellamkonda said. “Copycat strategies or trying to game the system are taboo in this era.” Experts should also share their thoughts across several channels, and "About Us" pages must be “robust” and include bios highlighting thought leaders’ expertise. “Ultimately, the reputation of AI-powered search is in making sure the user likes the search rather than what you think they should read,” Bellamkonda said. “So a good focus on the end user is a great way to succeed.”
Why the next phase of AI adoption will be determined less by models and more by data foundations
Why the next phase of AI adoption will be determined less by models and more by data foundations IT Brief Canada - Technology news for CIOs & IT decision-makers # Why the next phase of AI adoption will be determined less by models and more by data foundations Mon, 6th Apr 2026 (Yesterday) By Will LaForest, Global Field CTO, Confluent After two years of rapid experimentation and deployments, enterprises are now faced with the complex realities of scaling Artificial Intelligence (AI) systems into production. Business leaders are no longer asking whether AI can work; they are asking whether it delivers measurable value, operates reliably at scale, and can be trusted as part of core business processes. Until now, progress in AI has largely been framed through the lens of model innovation. But as AI systems move closer to production, it is beco
Only 28% of AI infrastructure projects fully pay off, survey finds
ITSM is the area most likely to offer wins, according to Gartner research.
The Agentic AI: How Autonomous AI Systems Are Rewriting the Rules of Work, Business, and Technology | by Nithin Narla | Apr, 2026 | Towards AI
The Agentic AI: How Autonomous AI Systems Are Rewriting the Rules of Work, Business, and Technology | by Nithin Narla | Apr, 2026 | Towards AI Sign up Get app Sign up ## Towards AI Follow publication We build Enterprise AI. We teach what we learn. Join 100K+ AI practitioners on Towards AI Academy. Free: 6-day Agentic AI Engineering Email Guide: https://email-course.towardsai.net/ Follow publication # The Agentic AI: How Autonomous AI Systems Are Rewriting the Rules of Work, Business, and Technology ## From chatbots to digital workers — inside the $10.9 billion revolution that’s turning AI from a tool you use into a colleague that works alongside you. 13 min read 21 hours ago -- 1 Listen Share Press enter or click to view image in full size Last week, we broke down the state of generative AI in 2026 — the models, the money, the infra
AI Agent Development Guide: Should You Build or Buy? | by RaftLabs | Apr, 2026 | Medium
AI Agent Development Guide: Should You Build or Buy? | by RaftLabs | Apr, 2026 | Medium Sign up Get app Sign up # AI Agent Development Guide: Should You Build or Buy? ## A practical guide for founders to build AI agents, covering tools, costs, and real-world execution. 17 min read 5 hours ago -- Listen Share Press enter or click to view image in full size Guide for AI Agent Development When Google’s CEO suggests AI could eventually perform C-suite duties more efficiently than humans, and Meta’s CEO is actively testing an AI-powered assistant to support day-to-day strategic decisions, one conclusion becomes unavoidable: AI agents are no longer experimental. They are becoming infrastructure. But here is what those headlines miss. The question for technical founders is not whether to adopt AI agents. It is how to adopt them without wasting six months and five figures discoveri
No one is going to want to give up their Claude (or ChatGPT or Gemini). But they will also be extremely nervous about the implications of AI overall. To that extent the "pivot to enterprise" is going to result in a lot more anxiety than when the focus was on consumer assistants.
No one is going to want to give up their Claude (or ChatGPT or Gemini). But they will also be extremely nervous about the implications of AI overall. To that extent the "pivot to enterprise" is going to result in a lot more anxiety than when the focus was on consumer assistants.
This article is a case study of why measuring AI performance is so hard. AI Overviews make mistakes. But the same mistakes are in Wikipedia. But the sources are harder to find when using AI. But the AI answers may be better than most people would find. Unclear what it all means.
This article is a case study of why measuring AI performance is so hard. AI Overviews make mistakes. But the same mistakes are in Wikipedia. But the sources are harder to find when using AI. But the AI answers may be better than most people would find. Unclear what it all means.
The AI conversation has shifted. Not long ago, leaders were asking ...
The AI conversation has shifted. Not long ago, leaders were asking whether the technology worked. Today, the question is how to scale it in a way that drives real outcomes. At the Microsoft AI Tour… | Tracy Galloway Agree & Join LinkedIn By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. Microsoft•24K followers 12h The AI conversation has shifted. Not long ago, leaders were asking whether the technology worked. Today, the question is how to scale it in a way that drives real outcomes. At the [Microsoft](https://www.linkedin.com/company/microsoft?trk=
Row over ‘virtual gated community’ AI surveillance plan in Toronto neighbourhood
Rosedale residents considering car licence plate-scanning Flock system in bid to tackle property crime A row has broken out in one of Canada’s wealthiest neighbourhoods over plans to use an AI-powered surveillance system to create the country’s first “virtual gated community” to combat surging property crime. Crime rates in Toronto as a whole are dropping but residents of Rosedale have been left on edge by a sustained rise in home invasions, with robbers targeting the tree-lined neighbourhood at a rate more than double the city average. Break-ins and thefts remain the third highest per capita in Toronto. Continue reading...
Advancing Healthcare with Generative AI: From Promise to Practice | BioPharm International
Reliable, domain-specific AI models grounded in validated clinical evidence are emerging as essential to safely scaling generative AI across healthcare applications.
AI in Healthcare: How Machine Learning Is Transforming Faster Disease Diagnosis in 2026
AI healthcare diagnostics improve disease detection accuracy and speed, transforming medical care with machine learning and predictive analytics.
Geopolitics
Gartner: The Sovereign AI Shockwave: Why Organisations Must Rethink their AI Strategy in 2026 | The AI Journal
Gartner: The Sovereign AI Shockwave: Why Organisations Must Rethink their AI Strategy in 2026 | The AI Journal Sovereign AI is often framed as a policy issue, yet it is increasingly shaping organisational reality. What began as government ambition, now influences how organisations build and govern AI. In 2026, sovereign AI is no longer peripheral to strategy; it is redefining how AI is built and leveraged. Across the US, China, the EU, the UK, UAE, Saudi Arabia, Canada, and India, public investment, including support from private sector partners, now exceeds $1 trillion. These commitments target AI innovation, infrastructure, data ecosystems, and talent – determining where capability resides and who controls access to it. The global AI landscape is being reshaped by design. ## From national ambition to organisational impact Sovereign AI has moved rapidly from intent to execution. Gov
Innovation Nation: Inside Japan's Ambitious AI and Automation Drive
Paid Program: Innovation Nation: Inside Japan’s Ambitious AI and Automation Drive # Innovation Nation: Inside Japan’s Ambitious AI and Automation Drive Japan’s AI push goes beyond policy—linking governance, infrastructure and semiconductors in a coordinated national project. Japan’s digital ambitions have long been framed in sweeping terms: using technology to lift productivity, support an aging population and modernize public services. Today, those ambitions are set to become reality, with a new industrial strategy designed to accelerate innovation without eroding public trust. Under Prime Minister Sanae Takaichi, AI and semiconductors have been put at the heart of this renewed industrial approach. As AI drives up global demand for computing power, countries without secure access to chips, energy and infrastructure risk being sidelined. Japan’s response is to position itself as a tr
Opinion | Export Controls Can’t Win an AI Race - The Washington Post
Containing China’s rise is barely half a strategy.
As you read about Anthropic's Mythos capabilities to find critical security weaknesses, consider what if a Chinese AI company had gotten here first. There is a real race underway, and its in our interest I believe for U.S. companies to win.
As you read about Anthropic's Mythos capabilities to find critical security weaknesses, consider what if a Chinese AI company had gotten here first. There is a real race underway, and its in our interest I believe for U.S. companies to win.
Europe’s sovereignty ambitions require smarter infrastructure | TechRadar
Europe’s AI sovereignty push depends on smarter infrastructure, not simply acquiring more GPUs
Europe needs to control AI for defense, top industry exec says – POLITICO
‘If you don’t have artificial intelligence in your systems, you actually don’t have an army,’ says Mistral CEO Arthur Mensch.
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