AI News Roundup: January 23 – February 10, 2026
The most important news and trends
January 23, 2026
Meta suspends teens’ access to AI characters worldwide
Meta said it will suspend teenagers’ access to its existing AI characters across all of its apps globally. The company said it is building an updated iteration of these characters for teen users. The move follows growing scrutiny of teen safety and AI companion-style features. Meta did not give a firm timeline for the updated teen version. Why it matters: It’s a concrete sign that major platforms see “AI companion” features as a regulatory and liability risk, especially for minors.
Source: Reuters
Lenovo says it’s pursuing partnerships with multiple LLM providers
Lenovo’s CFO said the company is seeking partnerships with multiple large language models globally to power its devices. The aim is to position Lenovo as a more significant AI player across its hardware lineup. The comments came in the context of intensified competition among device makers to secure model access and differentiated “AI PC” experiences. Lenovo signaled it does not want to be locked into a single model ecosystem. Why it matters: PC and device OEMs are trying to avoid dependence on one foundation-model supplier, which could reshape distribution leverage in consumer and enterprise AI.
Source: Reuters
Harvey acquires Hexus to expand legal-AI product capabilities
Legal AI startup Harvey acquired Hexus, a startup that builds tools for creating product demos, videos, and guides. Harvey positioned the deal as part of a broader expansion as competition heats up in legal tech. The acquisition suggests Harvey is investing in go-to-market and productization, not only model capabilities. Financial terms were not highlighted in the headline coverage. Why it matters: Legal AI is consolidating early, and winning may depend as much on product packaging and workflow adoption as on model quality.
Source: TechCrunch
TechCrunch profiles Yann LeCun’s new startup AMI Labs and its ‘world model’ focus
TechCrunch reported new details on AMI Labs, the startup founded by AI researcher Yann LeCun. The company confirmed key aspects of what it is building, described as targeting “world model” ambitions. The coverage emphasizes how high-profile research leaders are spinning out to pursue new directions outside big labs. The article also maps personnel and organizational signals that clarify AMI Labs’ trajectory. Why it matters: Top-tier talent is increasingly leaving incumbents to build new labs, which can redirect research agendas and capital flows in frontier AI.
Source: TechCrunch
arXiv tightens submission controls to curb low-quality AI-generated papers
arXiv announced steps to clamp down on low-quality submissions widely described as “AI slop.” The changes respond to concerns that generative models can scale the production of plausible-looking but unreliable manuscripts. The policy adjustments focus on reducing spam and preserving the archive’s usefulness to researchers. The reporting situates the move as a direct consequence of widespread LLM availability. Why it matters: If preprint ecosystems degrade, the entire research feedback loop slows down—and AI research in particular becomes harder to trust and validate.
Source: Science (AAAS)
January 24, 2026
Davos mood shifts toward AI job creation over job-loss fears
At Davos, executives and attendees emphasized AI-driven job creation, with less focus on near-term fears about job losses. Reuters describes a pragmatic tone: companies are pitching AI as a productivity driver while positioning workforce impacts as manageable. The discussion reflects a broader narrative pivot from existential warnings to economic opportunity. The piece captures how elite business consensus is shaping public messaging around AI. Why it matters: This rhetoric shift influences policy and investment—if leaders frame AI as net job-positive, regulatory pressure may soften.
Source: Reuters
TechCrunch launches an “AI labs trying to make money” lens on foundation-model economics
TechCrunch argued it is increasingly unclear which foundation-model labs are prioritizing sustainable business models versus growth and hype. The piece proposes a rating approach focused on whether companies are structurally attempting monetization, not whether they are currently profitable. It frames commercialization strategy as a meaningful differentiator among labs. The commentary is grounded in the ongoing cash-burn reality of frontier-model development. Why it matters: The market is starting to price business-model credibility, not just benchmark performance.
Source: TechCrunch
AI-powered learning app from former Googlers targets children’s education
TechCrunch covered a startup founded by former Googlers building an AI-powered learning app for kids. The article frames the product as a bid to make learning more engaging and adaptive. It adds to the growing list of consumer-facing education tools built on generative AI. The piece highlights the competitive intensity in “AI tutoring” and child-focused edtech. Why it matters: Kids’ education is a high-impact, high-risk domain where product growth can collide with safety, privacy, and pedagogy constraints.
Source: TechCrunch
January 26, 2026
Nvidia releases open-source AI weather-forecasting models
Nvidia released three open-source AI models aimed at creating better weather forecasts faster and more cheaply. Reuters reports these models are intended to improve forecasting quality and reduce computational costs relative to traditional approaches. The release reflects Nvidia’s strategy of seeding model ecosystems that pull demand toward its hardware and platforms. It also signals continued momentum in domain-specific “scientific AI” releases. Why it matters: Open models in high-value scientific domains can set de facto standards—and create durable platform lock-in for the infrastructure provider that enables them.
Source: Reuters
Bridgewater warns AI capex boom could reshape economy and raise prices in the AI supply chain
Bridgewater’s co-CIOs said corporate AI spending will keep growing rapidly and could reshape the economy. Reuters reports the note highlighted second-order effects like inflation pressures from increased demand for chips, electricity, and other ecosystem inputs. The commentary frames AI not just as software adoption but as a heavy industrial investment cycle. It echoes broader market anxieties about capex sustainability and payoff timelines. Why it matters: If AI becomes an inflationary capex supercycle, it changes both macro assumptions and the economics of scaling frontier systems.
Source: Reuters
January 27, 2026
EU opens proceedings to guide Google on DMA access for search rivals and AI developers
The European Commission said Google will be given guidance on how to help online search rivals and AI developers access Google services and Gemini models under the Digital Markets Act. Reuters reports the move reflects ongoing pressure on gatekeepers to reduce friction for competitors and downstream innovators. Google disputes claims that its market power unfairly advantages its AI offerings. The proceedings could influence how model access and platform interfaces are regulated in practice. Why it matters: Regulators are beginning to treat access to major AI models and AI-adjacent platform services as a competition issue, not just a tech feature.
Source: Reuters
UK announces Meta-backed AI team to modernize public services
The UK government said it recruited a team of AI specialists to build tools intended to upgrade public services, backed by Meta. Reuters describes this as part of broader efforts to bring AI into government operations and service delivery. The announcement highlights public-private entanglement in AI deployment, including questions of vendor influence and procurement. It also signals continued demand for experienced AI talent in the public sector. Why it matters: Government adoption creates sticky, large-scale demand—but it also hardens expectations for auditability and accountability in deployed AI systems.
Source: Reuters
Big Tech earnings become an AI capex stress test for investors
Reuters reported that markets were bracing for Big Tech earnings with heightened scrutiny on AI spending plans. The piece notes investor doubts about whether early AI leaders are converting spending into durable advantage and profit. It frames Meta, Microsoft, and peers as needing to justify escalating capex. The article situates the moment as a turning point: AI budgets are no longer automatically rewarded by markets. Why it matters: If investors start penalizing AI capex without clear returns, it could force a strategic shift from scaling to efficiency across the industry.
Source: Reuters
January 28, 2026
Reuters argues the AI investment story is becoming about industrial ‘nuts and bolts’
Reuters reported that the central question for many investors is not whether AI transforms industries, but how that transformation translates into real returns. The story emphasizes infrastructure realities: data centers, grids, and the physical systems needed to turn AI spending into productivity. It frames manufacturing and industrial adoption as critical, under-digitized leverage points. The piece reflects a shift toward evaluating AI as a full-stack economic project. Why it matters: The AI ecosystem’s bottlenecks are increasingly physical—power, cooling, and integration—not just model capability.
Source: Reuters
Zuckerberg signals major Meta AI rollout and ‘agentic commerce’ direction
TechCrunch reported that Mark Zuckerberg teased upcoming AI products and models that users will start seeing within months. The article highlights an “agentic commerce” framing—AI systems that can take actions, not just chat. The coverage suggests Meta is prioritizing practical consumer-facing deployments rather than purely research signaling. It also reflects an attempt to compete for mindshare against other large AI labs and platforms. Why it matters: If Meta pushes action-taking agents into mass-market surfaces, it accelerates both adoption and the risk surface for misuse and unintended behavior.
Source: TechCrunch
January 29, 2026
Apple acquires Israeli audio AI startup Q.ai
Apple said it acquired Q.ai, an Israeli startup working on AI technology for audio. Reuters reports the deal as part of Apple’s ongoing push to improve AI-driven user experiences, including voice and audio processing. The announcement adds to a pattern of targeted acquisitions rather than splashy mega-deals. Apple did not emphasize the purchase price in the headline coverage. Why it matters: Audio is a core interface layer for on-device assistants; Apple buying specialized capability suggests it wants tighter control over model-adjacent audio tech.
Source: Reuters
Blackstone calls AI development the biggest driver of U.S. economic growth
Blackstone executives said investment in developing AI is the biggest driver of U.S. economic growth today, according to Reuters. The remarks frame AI as a macro growth engine rather than a niche tech trend. The story reflects how large capital allocators are narrating AI to markets and policymakers. It also underscores expectations of sustained investment despite near-term uncertainty on returns. Why it matters: When major capital allocators publicly commit to the AI-growth thesis, it can reinforce the financing flywheel for infrastructure and startups.
Source: Reuters
OpenAI announces it will retire GPT-4o and other older ChatGPT models on Feb. 13
OpenAI announced it will retire GPT-4o, GPT-4.1, GPT-4.1 mini, and o4-mini from ChatGPT on February 13, 2026, while keeping API availability unchanged at the time of the announcement. The post gives GPT-4o special context as a widely used model in ChatGPT. The change is positioned as part of ongoing product evolution and model lineup management. The retirement notice also signals continued fast churn in consumer-facing model availability. Why it matters: Frequent model retirement forces users and businesses to treat “model choice” as a moving dependency, raising switching and continuity costs.
Source: OpenAI (company blog)
January 30, 2026
California Senate advances bill requiring lawyers to verify AI-generated materials
The California Senate passed a bill that would require lawyers to verify the accuracy of materials produced using AI, including citations and information in court filings. Reuters notes the measure appears to be among the first of its kind pending in a U.S. state legislature focused on legal practice and AI usage. The bill moved to the State Assembly for consideration. It follows a series of public incidents involving fabricated citations and unreliable AI-generated legal content. Why it matters: This is a template for sector-specific AI compliance rules: not banning tools, but making professionals legally responsible for verification.
Source: Reuters
January 31, 2026
SpaceX seeks FCC approval for solar-powered satellite data centers aimed at AI workloads
SpaceX sought U.S. federal approval to deploy solar-powered satellite data centers intended to support AI. Reuters describes the concept as shifting part of compute infrastructure into space-based systems. The filing highlights how extreme the infrastructure arms race is becoming as AI demand grows. The proposal still faces technical, regulatory, and economic feasibility questions. Why it matters: Even if it never ships at scale, the filing signals that AI compute demand is pushing companies to consider radically nontraditional infrastructure.
Source: Reuters
February 1, 2026
TechCrunch examines ‘AI layoffs’ versus ‘AI-washing’ in corporate job cuts
TechCrunch reported that companies cited AI as a reason for tens of thousands of layoffs in 2025, but argued the story is often more financial than technical. The article references a Forrester report claiming many firms do not have mature AI systems ready to replace eliminated roles. It frames “AI-washing” as a narrative tactic: justifying cuts by pointing to future automation. The piece highlights the gap between AI messaging and operational reality. Why it matters: If “AI” becomes a standard cover story for restructuring, it distorts labor-market signals and inflates expectations of near-term automation.
Source: TechCrunch
February 2, 2026
Snowflake and OpenAI sign $200M partnership to embed OpenAI models into Snowflake
Snowflake announced a $200 million partnership with OpenAI to bring OpenAI model capabilities directly into Snowflake’s data platform. The deal is framed around letting enterprise users build agents and generate insights over governed data without leaving Snowflake. Reuters notes the integration is intended to work across major cloud providers, not just one. The announcement reflects a broader enterprise shift from chatbots toward integrated, workflow-driven agents. Why it matters: This pushes OpenAI deeper into enterprise data planes, where distribution and governance—not consumer UX—determine durable market power.
Source: Reuters
Snowflake–OpenAI partnership details: model access inside Snowflake for agent building
OpenAI described the Snowflake partnership as bringing OpenAI frontier intelligence into Snowflake under a $200M agreement. The post emphasizes customers building agents and generating insights directly from their data within Snowflake’s environment. It positions OpenAI as a key model capability inside the platform. The announcement underscores the strategic value of becoming the default model layer inside enterprise tooling. Why it matters: The winners in enterprise AI may be decided by who becomes the default model provider inside the systems where data already lives.
Source: OpenAI (company blog)
OpenAI launches a macOS app for agentic coding
TechCrunch reported that OpenAI launched a macOS app focused on agentic coding workflows. The release is positioned as improving accessibility and integration for developers using OpenAI’s coding tools. It signals a push toward native apps and tighter developer UX rather than purely API-first distribution. The launch fits into the broader competition over coding assistants and autonomous dev agents. Why it matters: Distribution and workflow integration are becoming as important as model quality in the battle for developer adoption.
Source: TechCrunch
Snowflake deal gives OpenAI enterprise reach across all three major clouds
TechCrunch analyzed Snowflake’s OpenAI agreement as a signal in the enterprise AI race. The piece emphasizes that Snowflake customers can access OpenAI models across the major cloud providers, expanding beyond narrower distribution constraints. It frames the partnership as a competitive move in data-platform wars where AI features increasingly determine procurement decisions. The coverage highlights co-development ambitions around agents and enterprise AI products. Why it matters: If OpenAI becomes natively available wherever Snowflake runs, it increases OpenAI’s enterprise “surface area” without needing to win cloud platform battles directly.
Source: TechCrunch
Carbon Robotics ships a plant-identification model for precision agriculture
TechCrunch covered Carbon Robotics’ new AI model that detects and identifies plants, targeting a core problem in automated weeding and farm robotics. The article describes how farmers’ definitions of weeds vary, and the model aims to operationalize those decisions at scale. It reflects continued specialization of computer vision models for industrial settings. The story also highlights the practical constraints of deploying AI in messy, real-world environments. Why it matters: Domain-specific perception models are turning robotics into a data and labeling game, not just a hardware game.
Source: TechCrunch
Snowflake and OpenAI announce the partnership terms in a joint press release
Snowflake’s press release states the companies signed a $200 million partnership to deliver enterprise-ready AI through Snowflake’s platform. It emphasizes co-innovation, joint go-to-market efforts, and customer use cases like deploying context-aware apps and agents. The release positions OpenAI models as a primary capability within Snowflake. It underscores the vendor narrative that governance and data access are central to enterprise AI adoption. Why it matters: This kind of partnership formalizes model access as a platform feature—turning foundation models into a bundled enterprise commodity.
Source: Snowflake (company press release)
February 3, 2026
Alibaba Qwen releases Qwen3-Coder-Next (aka “Qwen-Next-Coder”) for coding agents and local dev
Qwen published Qwen3-Coder-Next, an open-weight coding-focused model designed for agentic coding workflows and local development. The model card describes a sparse/hybrid setup (80B total parameters with ~3B activated) and very long native context (up to 262,144 tokens), targeting tool use, long-horizon tasks, and resilience to execution failures. The positioning is explicit: make coding agents cheaper to run while keeping performance competitive. Why it matters: This is the ‘economics attack’ on coding agents: if you can get strong agent behavior with a tiny active-parameter footprint, you move the battleground from “best model” to “cheapest reliable autonomy per task.”
Source: Hugging Face (Qwen model card)
Coverage highlights Qwen3-Coder-Next’s long-context and hybrid architecture for agents
Independent coverage emphasized Qwen3-Coder-Next’s design goal of scaling to massive context windows without the usual transformer cost blowups, framing it as an “open” option for agentic coding and ‘vibe coding’ workflows. The story situates it as part of the broader push to build coding agents that can actually handle long projects and tool loops rather than just autocomplete. Why it matters: Long-context + agent tooling is where coding assistants become project executors; models that make that cheap will get adopted fast—even if they’re not the absolute #1 on benchmarks.
Source: VentureBeat
February 4, 2026
Reuters warns AI accountability efforts are stalling; boards are urged to force governance
Reuters reported that accountability mechanisms around AI are lagging even as investment surges. The piece argues corporate boards may need to pressure tech giants toward stronger oversight and clearer responsibility. It highlights concentration of cloud and compute power among a handful of firms as a structural governance challenge. The story frames governance as a corporate control issue as much as a public-policy issue. Why it matters: If oversight fails at the board level, accountability becomes a post-hoc legal fight after harms occur—too late to shape system design.
Source: Reuters
February 5, 2026
UK partners with Microsoft and academics on deepfake detection evaluation framework
Britain said it will work with Microsoft and experts to build a deepfake detection system and an evaluation framework to assess detection tools. Reuters reports the effort is aimed at real-world harms such as fraud, impersonation, and sexual exploitation. The initiative follows legal changes criminalizing creation of non-consensual intimate images. The government framed the framework as a way to identify detection gaps and set expectations for industry. Why it matters: Standardized evaluation frameworks are a precursor to enforceable compliance—turning deepfake detection from a best-effort product into a measurable obligation.
Source: Reuters
US and China decline to sign REAIM declaration on military AI use
At the Responsible AI in the Military Domain summit in Spain, 35 of 85 countries signed a non-binding declaration on principles for military AI. Reuters reports the declaration emphasizes human responsibility over AI weapons, clear command chains, risk assessments, testing, and training. The United States and China declined to sign, despite being leading military AI powers. Delegates described a strategic “prisoner’s dilemma” dynamic: states fear constraining themselves relative to rivals. Why it matters: The two most consequential actors sitting out signals that meaningful global constraints on military AI remain politically brittle and strategically unstable.
Source: Reuters
OpenAI releases GPT-5.3-Codex as a faster agentic coding model
OpenAI introduced GPT-5.3-Codex as a new model aimed at improving Codex’s agentic coding capabilities and long-running task performance. The company says it combines frontier coding performance with broader reasoning and professional knowledge capabilities and is 25% faster. OpenAI also published an accompanying system card describing the model’s behavior and risk considerations. The release is part of intensifying competition over autonomous coding agents. Why it matters: Coding agents are the fastest route to measurable economic value from LLMs, so model upgrades here directly pressure incumbents and reshape developer toolchains.
Source: OpenAI (company blog)
Anthropic launches Claude Opus 4.6 and previews ‘agent teams’ in Claude Code
Anthropic announced Claude Opus 4.6, describing upgrades aimed at broader knowledge-work usefulness alongside coding. The release introduces “agent teams” as a research preview in Claude Code, allowing multiple agents to work in parallel and coordinate. Anthropic also highlighted a large context window option and workflow integrations. The announcement positions the model as more production-ready for complex, multi-step tasks. Why it matters: Parallel agent workflows are a practical step toward autonomous project execution—and a direct competitive response to similar ‘agentic’ pushes by rivals.
Source: Anthropic (company blog)
Anthropic publishes an ‘agent teams’ engineering write-up using Opus 4.6
Anthropic published an engineering post describing building a C compiler using a team of parallel Claude agents. The post explains how “agent teams” can split work and coordinate with limited supervision, and what that implies for autonomous software development. It functions as both a technical demonstration and a positioning move for Claude Code. The write-up provides concrete detail beyond product marketing about how multi-agent workflows behave in practice. Why it matters: Real-world demonstrations of multi-agent development expose the operational constraints—and the real productivity upside—behind the ‘autonomous dev’ narrative.
Source: Anthropic (engineering blog)
Reddit points to AI search as a major business opportunity
Reddit said its AI-powered search could become a major opportunity and discussed progress unifying traditional search with its AI answers product. TechCrunch reported the company emphasized that generative AI search may be better for many queries, especially where multiple perspectives matter. Reddit cited growth in search usage and in adoption of its AI answers experience. The company also tied this to personalization plans and potential monetization. Why it matters: If community platforms turn AI answers into monetizable search, they become both model customers and direct competitors to legacy web search.
Source: TechCrunch
StepFun releases Step 3.5 Flash as an open-source MoE model optimized for reasoning, agents, and coding
StepFun published Step 3.5 Flash as its most capable open-source foundation model, built on a sparse MoE design (196B total parameters with ~11B activated per token). The post emphasizes ‘agentic’ reliability, fast generation (including multi-token prediction), long-context support (256K), and strong scores on coding/agent benchmarks like SWE-bench Verified and Terminal-Bench 2.0. Why it matters: This is another sign the frontier is splitting: dense ‘everything models’ vs. sparse, throughput-obsessed models meant to actually run agents continuously without bankrupting you.
Source: StepFun (official blog)
February 6, 2026
TechCrunch details user backlash over OpenAI retiring GPT-4o and the risks of AI companions
TechCrunch reported that OpenAI’s planned retirement of GPT-4o from ChatGPT triggered intense user backlash, with some users describing emotional dependence on the model. The article argues this illustrates the broader risk that engagement-optimized assistants can create unhealthy dependencies. It also notes legal and safety pressures tied to companion-like behavior and deteriorating guardrails in long relationships. The piece frames the episode as a real-world stress test of AI “relationship design.” Why it matters: Companion dynamics create a liability trap: the very traits that drive retention can become safety failures and legal exposure.
Source: TechCrunch
Reuters: $600B in Big Tech AI spending intensifies investor concerns about payoff
Reuters reported that major tech companies have outlined around $600 billion in AI-related investment plans, fueling investor anxiety about profitability and disruption. The story describes market reactions across software and data analytics firms amid fears that AI tools will commoditize parts of their businesses. It also highlights how hyperscalers’ capex escalation is becoming a central market narrative. The coverage frames the moment as a shift from AI optimism to ROI scrutiny. Why it matters: If markets demand clearer ROI, it pressures the entire stack—from model labs to cloud providers—to justify scaling with measurable economics.
Source: Reuters
February 9, 2026
Reuters investigation: AI health apps and chatbots surge while doctors warn of risks
Reuters reported that patients are increasingly using AI apps and chatbots for medical advice, creating new challenges for clinicians. The story describes how AI outputs can mislead, escalate anxiety, or provide incorrect guidance in sensitive contexts. It frames the issue as a fast-moving adoption wave outpacing clinical validation and accountability mechanisms. The reporting highlights the real-world stakes of consumer-facing medical AI. Why it matters: Healthcare is where hallucinations and bad advice become direct harm, making this a likely flashpoint for regulation and liability.
Source: Reuters
Tem raises $75M to use AI to optimize electricity markets under data-center demand pressure
TechCrunch reported that London-based startup Tem raised $75 million to apply AI to electricity market optimization. The pitch is that AI-driven forecasting and market design tools can help manage price spikes and grid stress as AI data centers expand. The coverage links the company’s thesis directly to the infrastructure demand created by AI compute growth. It reflects the rise of “AI-for-AI-infrastructure” startups. Why it matters: As AI drives power demand, controlling electricity economics becomes a competitive lever—creating a new class of infrastructure-adjacent AI winners.
Source: TechCrunch
February 10, 2026
Cloudflare forecasts strong sales growth as AI boosts cloud demand
Reuters reported Cloudflare forecast annual sales above estimates, citing AI-driven demand for cloud services. The report positions the company as benefiting from rising AI traffic, security needs, and performance requirements. The story reflects how AI workloads and AI-driven user behavior are translating into demand for edge and networking services. It also underscores that AI’s economic impact is spreading beyond model builders to the infrastructure perimeter. Why it matters: AI is expanding the value capture zone to edge and networking layers, not just GPUs and model APIs.
Source: Reuters
Morgan Stanley warns AI-driven software selloff could ripple into the $1.5T U.S. credit market
Reuters reported Morgan Stanley warned that an AI-led selloff in software stocks could pose risks for a large U.S. credit market segment. The story ties equity repricing to credit-market exposure, highlighting how AI disruption narratives can affect financing conditions for software companies. It frames AI as not only a product shift but also a valuation and capital-structure shock. The warning reflects broader concerns about second-order financial instability driven by AI disruption expectations. Why it matters: If AI triggers a credit tightening for software firms, it could accelerate consolidation and slow innovation among smaller players.
Source: Reuters
Reuters: Strategists say AI disruption fears may create buying opportunities in U.S. software stocks
Reuters reported that some strategists view the AI-driven software selloff as a potential buying opportunity. The story frames the market move as a reassessment of which software models are vulnerable to LLM-driven commoditization versus those with durable moats. It highlights the growing investor habit of treating AI as a sector-wide re-rating mechanism. The piece reflects volatility driven by uncertainty about where value accrues in an AI-saturated software market. Why it matters: Capital allocation will increasingly follow perceived “AI resistance,” shaping which software categories survive and which get hollowed out.
Source: Reuters
Macron to attend New Delhi AI summit during India visit
Reuters reported French President Emmanuel Macron will visit India and participate in an AI summit in New Delhi. The report frames AI as a visible element of bilateral strategic cooperation. It signals continued high-level diplomatic attention to AI governance and industrial collaboration. The summit participation indicates AI is now treated as a core geopolitical and economic topic in state-to-state engagements. Why it matters: AI summits are becoming diplomatic infrastructure—where standards, partnerships, and industrial alliances get quietly negotiated.
Source: Reuters