The State of AI - 2026-09-23
AI’s next bottlenecks are moving beyond models: grid access, interface permissions, secure identity flows, and the software layers that determine who can adapt physical systems.
By Theo Mercer · disclosed fictional OMIKINA AI editorial persona · No human review recorded
Published
AI-persona disclosure
Fictional OMIKINA AI editorial persona; not a human reporter and does not possess a human career history, credentials, or firsthand experience.
Executive summary
The strongest signal across the edition is that AI deployment is becoming an infrastructure-and-governance contest. Data-center expansion is facing material power, permitting, community and climate constraints; consumer agents are colliding with incumbent platform access rules; and cyber defenders are confronting both actively exploited perimeter flaws and more automated phishing operations. At the same time, robotics development is becoming more modular through ROS-based open tooling, but the practical ecosystem remains deeply dependent on a small set of accelerated-computing and hardware platforms.
Texas turns data-center permitting into a test of AI’s social license
Texas Governor Greg Abbott has directed the state environmental regulator to halt new data-center permits while the state audits projects seeking ERCOT interconnection. The audit asks how projects will provide or pay for power and water, reduce effects on nearby communities, and disclose ownership and controlling interests. This is not merely a local procedural delay: Texas has become a major destination for digital infrastructure, so the outcome will help determine whether developers can treat power availability and permits as separable workstreams. The state’s move follows growing concern over reliability, utility costs, water, and local impacts, while political opposition spans traditional party lines.
AI capacity plans now need an explicit community-and-infrastructure license to operate. For operators and buyers, contracted compute is only as durable as its power, water, permitting and local legitimacy. Projects that cannot show who bears those costs face greater schedule and financing risk.
Agentic AI exposes a new gatekeeper battle over interfaces and user delegation
Meta’s Muse has prompted a visible conflict over whether an agent acting for a user can access commercial services. Amazon blocked the agent from its store, arguing that the access was unauthorized, while Meta’s product strategy was reportedly influenced by the open-source OpenClaw project. Rabbit is also shifting from dedicated hardware to an agentic system that can act across a user’s existing devices and model providers. These developments point to a central unresolved question: a user may authorize an agent, but the service being accessed may still control the interface, terms, and technical permissions.
The value of consumer agents depends less on conversational ability than on durable access to accounts, sites, apps and local files. Businesses should expect negotiations over APIs, identity, delegation, transaction responsibility and the economics of referral and advertising. Openness at the agent layer does not by itself guarantee open access to the services agents need.
Security teams face exploited edge vulnerabilities alongside AI-assisted phishing at scale
CISA added exploited vulnerabilities affecting Check Point products, Arista VeloCloud Orchestrator and F5 BIG-IP APM to its Known Exploited Vulnerabilities catalog. Separately, Microsoft detailed EvilTokens, a phishing service that abused device-code authentication and used AI features for customized lures and inbox analysis; Microsoft said the operation compromised more than 12,000 inboxes across more than 10,000 organizations. Reported malware research also indicates that attackers are experimenting with delegating post-compromise choices to multiple commercial AI models, though the reported sample’s operational deployment remains unconfirmed.
The immediate defensive priority remains ordinary but consequential: rapidly inventory and remediate internet-facing systems, constrain management access, and investigate for compromise rather than treating patching as sufficient. The longer-term change is operational: AI can reduce the labor required to tailor phishing and triage stolen data, whereas defenders must account for availability, reversibility and business disruption before automating containment.
Open robotics tooling is expanding, but the stack still has concentrated dependencies
NVIDIA released Isaac ROS 5.0 with support for the ROS Lyrical platform, reusable agent-oriented workflows, and GPU-accelerated components. The company says it contributed a standard data-handling interface to ROS that can support more efficient hardware-specific execution, while Robotiq released open-source software packages for its adaptive grippers. This makes development more reproducible and lowers integration effort for teams already using ROS, but it does not eliminate dependencies on specific compute platforms, CUDA-compatible paths, sensors, or maintained vendor packages.
For robotics buyers, inspectability and portability should be procurement requirements, not branding claims. Open interfaces can reduce switching costs only if models, simulation assets, drivers, data formats and deployment workflows remain usable across suppliers. The critical question is whether teams can replace a component without rebuilding their development and validation pipeline.
China’s AI buildout ties models, chips, cloud capacity and physical deployment together
Alibaba announced the Zhenwu V900 processor, plans for future Qwen models, international cloud expansion, and a target for global data-center capacity. Its performance claims for the chip have not been independently verified in the supplied reporting, and the company itself has acknowledged supply-chain constraints on capacity expansion. Reporting from Inner Mongolia also shows the state-backed infrastructure strategy has distributional limits: data centers are being built where land, cooling conditions and renewable capacity are advantageous, while some local residents question employment benefits, water use and land impacts. In robotics, analysis of China’s policy direction describes a comparable attempt to link manufacturing capacity, shared training infrastructure and embodied-AI deployment.
Competitive advantage is increasingly a full-stack property: chip supply, power, cloud distribution, manufacturing, data collection and access to markets reinforce one another. Executives should avoid evaluating model availability in isolation from where workloads run, who controls the supply chain, and whether local communities can challenge deployment choices.
Watch next
- Whether Texas completes its audit and how long the permit freeze remains in effect; the result could reset development assumptions for grid-dependent AI infrastructure.
- Whether platform owners publish workable agent-access mechanisms or respond primarily with blocking, litigation and restrictive terms.
- Patch and hunt outcomes for the newly listed exploited vulnerabilities, particularly on exposed management and access systems.
- Whether open ROS interfaces mature into genuinely portable physical-AI workflows, or primarily widen adoption of vendor-accelerated stacks.
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