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Thoughts on America’s AI Action Plan \ Anthropic

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1 month ago

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Today, the White House released “Winning the Race: America’s AI Action Plan”—a comprehensive strategy to maintain America’s advantage in AI development. We are encouraged by the plan’s focus on accelerating AI infrastructure and federal adoption, as well as strengthening safety testing and security coordination. Many of the plan’s recommendations reflect Anthropic’s response to the Office of Science and Technology Policy’s (OSTP) prior request for information. While the plan positions America for AI advancement, we believe strict export controls and AI development transparency standards remain crucial next steps for securing American AI leadership.

Accelerating AI infrastructure and adoption

The Action Plan prioritizes AI infrastructure and adoption, consistent with Anthropic’s submission to OSTP in March.

We applaud the Administration’s commitment to streamlining data center and energy permitting to address AI’s power needs. As we stated in our OSTP submission and at the Pennsylvania Energy and Innovation Summit, without adequate domestic energy capacity, American AI developers may be forced to relocate operations overseas, potentially exposing sensitive technology to foreign adversaries. Our recently published “Build AI in America” report details the steps the Administration can take to accelerate the buildout of our nation’s AI infrastructure, and we look forward to working with the Administration on measures to expand domestic energy capacity.

The Plan’s recommendations to increase the federal government’s adoption of AI also includes proposals that are closely aligned with Anthropic’s policy priorities and recommendations to the White House. These include:

  • Tasking the Office of Management and Budget (OMB) to address resource constraints, procurement limitations, and programmatic obstacles to federal AI adoption.
  • Launching a Request for Information (RFI) to identify federal regulations that impede AI innovation, with OMB coordinating reform efforts.
  • Updating federal procurement standards to remove barriers that prevent agencies from deploying AI systems.
  • Promoting AI adoption across defense and national security applications through public-private collaboration.

Democratizing AI’s benefits

We are aligned with the Action Plan’s focus on ensuring broad participation in and benefit from AI’s continued development and deployment.

The Action Plan’s continuation of the National AI Research Resource (NAIRR) pilot ensures that students and researchers across the country can participate in and contribute to the advancement of the AI frontier. We have long supported the NAIRR and are proud of our partnership with the pilot program. Further, the Action Plan’s emphasis on rapid retraining programs for displaced workers and pre-apprenticeship AI programs recognizes the errors of prior technological transitions and demonstrates a commitment to delivering AI’s benefits to all Americans.

Complementing these proposals are our efforts to understand how AI is transforming, and how it will transform, our economy. The Economic Index and the Economic Futures Program aim to provide researchers and policymakers with the data and tools they need to ensure AI’s economic benefits are broadly shared and risks are appropriately managed.

Promoting secure AI development

Powerful AI systems are going to be developed in the coming years. The plan’s emphasis on defending against the misuse of powerful AI models and preparing for future AI related risks is appropriate and excellent. In particular, we commend the administration’s prioritization of supporting research into AI interpretability, AI control systems, and adversarial robustness. These are important lines of research that must be supported to help us deal with powerful AI systems.

We’re glad the Action Plan affirms the National Institute of Standards and Technology’s Center for AI Standards and Innovation’s (CAISI) important work to evaluate frontier models for national security issues and we look forward to continuing our close partnership with them. We encourage the Administration to continue to invest in CAISI. As we noted in our submission, advanced AI systems are demonstrating concerning improvements in capabilities relevant to biological weapons development. CAISI has played a leading role in developing testing and evaluation capabilities to address these risks. We encourage focusing these efforts on the most unique and acute national security risks that AI systems may pose.

The Need for a National Standard

Beyond testing, we believe basic AI development transparency requirements, such as public reporting on safety testing and capability assessments, are essential for responsible AI development. Leading AI model developers should be held to basic and publicly-verifiable standards of assessing and managing the catastrophic risks posed by their systems. Our proposed framework for frontier model transparency focuses on these risks. We would have liked to see the report do more on this topic.

Leading labs, including Anthropic, OpenAI, and Google DeepMind, have already implemented voluntary safety frameworks, which demonstrates that responsible development and innovation can coexist. In fact, with the launch of Claude Opus 4, we proactively activated ASL-3 protections to prevent misuse for chemical, biological, radiological, and nuclear (CBRN) weapons development. This precautionary step shows that far from slowing innovation, robust safety protections help us build better, more reliable systems.

We share the Administration’s concern about overly-prescriptive regulatory approaches creating an inconsistent and burdensome patchwork of laws. Ideally, these transparency requirements would come from the government by way of a single national standard. However, in line with our stated belief that a ten-year moratorium on state AI laws is too blunt an instrument, we continue to oppose proposals aimed at preventing states from enacting measures to protect their citizens from potential harms caused by powerful AI systems, if the federal government fails to act.

Maintaining strong export controls

The Action Plan states that “denying our foreign adversaries access to [Advanced AI compute] . . . is a matter of both geostrategic competition and national security.” We strongly agree. That is why we are concerned with the Administration’s recent reversal on export of the Nvidia H20 chips to China.

AI development has been defined by scaling laws: the intelligence and capability of a system is defined by the scale of its compute, energy, and data inputs during training. While these scaling laws continue to hold, the newest and most capable reasoning models have demonstrated that AI capability scales with the amount of compute made available to a system working on a given task, or “inference.” The amount of compute made available during inference is limited by a chip’s memory bandwidth. While the H20’s raw computing power is exceeded by chips made by Huawei, as Commerce Secretary Lutnick and Under Secretary Kessler recently testified, Huawei continues to struggle with production volume and no domestically-produced Chinese chip matches the H20’s memory bandwidth.

As a result, the H20 provides unique and critical computing capabilities that would otherwise be unavailable to Chinese firms, and will compensate for China’s otherwise major shortage of AI chips. To allow export of the H20 to China would squander an opportunity to extend American AI dominance just as a new phase of competition is starting. Moreover, exports of U.S. AI chips will not divert the Chinese Communist Party from its quest for self-reliance in the AI stack.

To that end, we strongly encourage the Administration to maintain controls on the H20 chip. These controls are consistent with the export controls recommended by the Action Plan and are essential to securing and growing America’s AI lead.

Looking ahead

The alignment between many of our recommendations and the AI Action Plan demonstrates a shared understanding of AI’s transformative potential and the urgent actions needed to sustain American leadership.

We look forward to working with the Administration to implement these initiatives while ensuring appropriate attention to catastrophic risks and maintaining strong export controls. Together, we can ensure that powerful AI systems are developed safely in America, by American companies, reflecting American values and interests.

For more details on our policy recommendations, see our full submission to OSTP, and our ongoing work on responsible AI development and our recent report on increasing domestic energy capacity.



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AI Research

Artificial Intelligence Of Things (AIoT) Guide

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54 minutes ago

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September 4, 2025

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For organizations exploring connected technologies, the conversation around the Internet of Things (IoT) has shifted. They are looking to make devices smarter, responsive and capable of operating with greater insight.

The Artificial Intelligence of Things (AIoT) combines the sensor-driven, networked structure of IoT with the decision-making power of artificial intelligence (AI) at the edge to collect and act on data. This generates insights to help businesses improve efficiency and reliability and streamlines user experience.

Explore what AIoT is, its benefits and use cases below.

What is AIoT?

AIoT is the convergence of AI with IoT. The technology brings intelligence to devices that connect and transmit data, transforming them into systems that interpret information, learn from patterns and support decision-making.

The key components of AIoT systems include:

  • Sensors and actuators: Sensors interact with the surroundings by collecting and measuring data, while actuators translate instructions from the AI system into actions.
  • Connectivity: Connectivity solutions enable communication between devices.
  • Edge computing: Devices equipped with embedded AI chips or processors handle data processing to improve response times.
  • AI models: AI algorithms analyze patterns, detect anomalies, forecast trends and support automated decision-making. These models are trained on historical or real-time data and deployed at the edge or in the cloud.
  • Cloud infrastructure: The cloud stores large volumes of data and supports remote device management and software updates.

The difference between IoT and AIoT

IoT systems collect data from sensors, transfer it through networks and store it in the cloud for later analysis. These systems may need oversight to make decisions based on the data collected. This creates delays and increases dependence on bandwidth.

AIoT embeds intelligence closer to where data is generated. It reduces latency and the bandwidth required to move data. By analyzing and interpreting data at the edge, it streamlines decision-making, allowing businesses to react faster to changing conditions.

AIoT architecture

Explore how AIoT’s architecture allows it to collect, analyze and share information.

Edge computing AIoT

Edge-based computing has the following layers:

  • Collection terminal layer: This includes sensors, cameras and mobility devices that gather information from surrounding environments.
  • Connectivity layer: This layer consists of hardware and software that transmits data to nearby edge devices.
  • Edge layer: Edge AI processors handle real-time analytics, pattern recognition and insight generation.

Cloud-based AIoT

Cloud-based AIoT layers include:

  • Device layer: This layer includes beacons, sensors, embedded devices and tags that collect and transmit data.
  • Connectivity layer: The connectivity layer comprises software and hardware that facilitate secure and reliable data transfer to the cloud.
  • Cloud layer: AI workloads are run here for analysis and insights.
  • User communication layer: The user communication layer interfaces with dashboards, apps or web portals for the end user.

Benefits of integrating AI in IoT devices

Implementing a new technology should drive measurable outcomes in performance, cost and improve user experience. Here are the benefits of connecting devices to AIoT.

1. Boosts operational efficiency

AIoT increases efficiency by enabling systems to respond to conditions in real time. When decisions are made at the edge, devices can automatically adjust behaviors.

This means:

  • Heating, ventilation, and air conditioning (HVAC) systems regulate airflow based on occupancy patterns.
  • Supply chains reroute based on traffic or inventory signals.
  • Machinery modulates speed or power use in response to production demand.
  • The result is smarter resource use, better coordination and fewer interventions.

2. Enhances safety

AIoT improves safety by enabling instant decision-making, which helps prevent accidents.

For example:

  • Edge-enabled cameras detect unauthorized access or unusual motion patterns and trigger alarms immediately.
  • Equipment shuts down or enters safe mode if abnormal vibration or heat is detected.
  • Smart infrastructure manages traffic flows or alerts authorities to emergencies before a hazard escalates.

3. Reduces human error

By automating data interpretation and decision-making, AIoT reduces the room for human error in routine or high-risk tasks. In sectors where conditions change rapidly, AI-enabled devices adjust irrigation or voltage levels automatically.

In administrative settings, AIoT monitor systems and flag inconsistencies or risks that might be overlooked. It supports teams by handling the repetitive, high-precision tasks where mistakes may slip through.

4. Improves decision-making

With access to real-time data, historical trends and contextual cues, AIoT recommends or takes action based on potential outcomes. For example, in manufacturing, AIoT identifies production bottlenecks and suggests changes to scheduling. In smart cities, traffic data combined with weather and event schedules drive predictive rerouting. This capability augments human judgment to streamline the gathering and analysis of insights.

5. Streamlines predictive maintenance

By monitoring equipment health and comparing it to past performance data, AI models detect subtle signs of wear, imbalance or malfunction before failure occurs. This helps prevent costly downtime. Additionally, technicians are alerted to service needs when they are required, rather than relying on fixed schedules or post-failure repair.

6. Provides scalability

As more devices are added, the system becomes more intelligent. New sensors or devices feed data into shared models, enriching the entire system. Firmware and AI models are updated remotely, and because intelligence resides at the edge, the system doesn’t become dependent on a single hub. This flexibility allows businesses to scale while maintaining performance and control.

7. Maximizes data value

AIoT systems extract and turn data into meaningful, actionable outputs. This reduces data overload while uncovering patterns some analytics tools might miss. For example, temperature fluctuations combined with vibration readings in a cold storage unit might signal compressor strain. AIoT flags this before damage occurs. It also enables real-time dashboards for operations teams, providing visibility into system health and performance metrics.

8. Helps manage energy

The system enables low energy use by allowing devices to self-optimize based on context. For example, lighting systems in smart buildings dim or shut off based on occupancy. Industrial machines shift loads to off-peak hours, while electric vehicles and grid systems balance loads in response to demand and supply.

This level of precision reduces energy bills and supports green initiatives, helping business demonstrate their environmental responsibility.

9. Supports personalization

At the consumer and user experience level, AIoT allows for tailored and intuitive engagements. Devices learn user preferences, anticipate needs, and adjust accordingly. For instance, thermostats learn daily routines and adjust temperatures as needed. Digital signage in retail adapts based on customer flow and demographic signals, providing an exceptional user experience. Even medical devices can personalize treatment or therapy based on individual patient concerns.

AIoT brings contextual awareness in every interaction, making devices feel more aligned with consumer needs.

Considerations when implementing AIoT

AIoT has several benefits. However, businesses should keep the following considerations in mind when deploying the technology:

Data privacy and security: AIoT devices collect and process vast amounts of data, some of which may be sensitive, personal or proprietary. This makes privacy and security a priority in industries adhering to strict privacy regulations. Businesses should map out where and how data is being used, and align with security policies, regulatory requirements and industry standards.

Interoperability: Ensuring your systems are interoperable across vendors, protocols and applications can be complex in legacy arrangements. Open standards and flexible connectivity options can reduce integration friction. Businesses can partner with providers who factor in interoperability. They can offer customizable solutions that can plug into existing systems instead of requiring complete overhauls.

System complexity: Building a cohesive and balanced system requires specialized expertise. Businesses can work alongside partners who specialize in edge computing, embedded systems and connectivity integration to reduce friction in design and deployment. With the right support, complexity becomes manageable.

AIoT applications and examples

As AIoT matures, intelligent edge devices may help various sectors meet their needs. Below are the use cases of the system in different industries.

Manufacturing

Embedded AI processors in industrial equipment monitor vibration, pressure and temperature to detect early signs of mechanical failure. This makes it possible to perform maintenance before problems interrupt production. Computer vision systems powered by AI also support inspection on assembly lines, automatically flagging defects or inconsistencies in products.

These tools detect variations invisible to the naked eye, raising quality control standards while reducing manual inspection workloads.

Facilities also use smart sensors to handle energy consumption. By tracking usage patterns, models suggest optimizations or adjust systems during peak hours, helping factories control costs.

Smart home technology

In residential settings, AIoT enables homes to respond to owners’ behavior. For example, far-field voice recognition allows devices to understand commands even in noisy conditions or from across the room.

Occupancy sensors and AI-driven climate control systems work in tandem to adjust temperatures and lighting based on who’s home, what rooms are in use and time of day preferences. These features reduce energy consumption while enhancing comfort.

Biometric authentication methods like fingerprint or presence-based access help control smart locks or safes. These systems learn user routines and flag irregular access attempts or unexpected activity.

Smart cities and public infrastructure

One common use case in smart cities and public infrastructure is traffic optimization. By combining data from cameras and road sensors with AI, traffic lights adjust based on vehicle and pedestrian flow, easing congestion and improving safety.

Sensors across cities monitor air quality, noise pollution and water infrastructure. When they exceed thresholds, relevant departments use these insights to create quality standards, assess law compliance and notify the public about environmental conditions.

Retail

In retail, AIoT is driving a shift toward more responsive, tailored in-store experiences while giving businesses visibility into their operations. Smart cameras and shelf sensors monitor customer movement, identify high-traffic zones and optimize product placement. For example, interactive kiosks, enhanced by touch interfaces, adjust content based on user engagement, providing dynamic recommendations or support. These systems personalize the experience and reduce the need for additional staff.

Smart shelves equipped with weight sensors and computer vision detect when items are low or misplaced, automatically triggering restocking processes or alerting staff. This reduces out-of-stock scenarios and makes inventory manageable.

Health care

Wearable devices equipped with AI monitor vital signs and detect patterns that might indicate a problem before symptoms worsen. These insights are then shared with health care providers, enabling early intervention.

In hospitals, edge computing AI devices monitor patients in intensive care units. These systems process data to flag anomalies, supporting faster response times in intervention.

Diagnostic imaging is another area where AIoT is gaining ground. Devices embedded with AI analyze scans for signs of disease, accelerating diagnosis and supporting radiologists by flagging subtle indicators they might miss.

Agriculture

In agriculture, AIoT systems help farmers operate precisely and sustainably. Smart soil sensors monitor moisture, nutrient content and pH levels, sending this data to processors that determine optimal watering or fertilization schedules. This reduces the overuse of resources and helps crops grow under optimal conditions.

Computer vision applications also scan plants for signs of disease or pest infestation so that farmers use targeted interventions, preventing widespread crop damage.

Weather stations powered by AI models analyze local setting patterns and historical trends to recommend planting schedules or harvesting times. This leads to better crop yield, less waste and efficient processes.

Telecommunications

Telecom providers operate vast and decentralized infrastructure, making AIoT valuable for network visibility and uptime. AI-enabled edge monitoring systems track performance indicators to help provide reliable service to end users. These systems help automate routine diagnostics and preemptively schedule maintenance.

Additionally, smart energy systems manage consumption based on load and demand, supporting sustainability and lowering operational costs.

Energy

In energy and utilities, smart grids collect data and feed it into AI models that analyze consumption patterns, detect anomalies and make recommendations for efficiency improvements.

Distributed energy resources like solar panels or wind turbines use edge-based AI to optimize performance. For instance, processors might adjust the tilt angles of solar panels in response to changing light conditions.

Transportation and logistics

In autonomous or semi-autonomous vehicles, edge AI systems handle decision-making, detect obstacles, adjust routes and improve driver safety. These systems process visual and sensor data to avoid delays associated with sending everything to the cloud.

In warehousing operations, AI-driven systems manage inventory and adjust to supply demands, automate stock replenishment and optimize floor layouts. This boosts throughput without requiring constant human oversight.

Implement IoT AI solutions

Synaptics offers a comprehensive portfolio of advanced human interface solutions designed to help businesses implement AIoT. Our sensing, processing and connectivity systems help you create devices that respond in real time.

With robust connectivity options across wireless standards and protocols, we ensure your devices stay responsive and reliable wherever they’re deployed. Our solutions are flexible and scalable, and are optimized for low-power, high-performance settings.



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Research has shown that people who do not use AI technology more than those who are well aware of ar..

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3 hours ago

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September 4, 2025

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According to a study by the University of Southern California in the United States, “The less AI you understand, the more magical AI you feel.”

AI-generated image of a college student who is disappointed with low grades [Production = Gemini]

Research has shown that people who do not use AI technology more than those who are well aware of artificial intelligence (AI) technology. In addition, there are studies showing that excessive use of Generative AI tools such as ChatGPT is linked to lower academic achievement, and it is analyzed that dependence on AI should be vigilant.

According to a study by researchers at the University of Southern California in the United States and the University of Bocconi in Italy on the 4th, the lower the understanding of AI, the more often they accept it as magic and use it.

The researchers evaluated 234 university undergraduate students with their understanding of AI (literacy), then gave them writing tasks on a specific topic and investigated whether to use Generative AI tools.

As a result, students with lower scores in AI understanding showed a stronger tendency to use AI for tasks. The researchers analyzed, “People with low understanding of AI perceive AI like magic,” and “They are likely to be in awe when AI performs tasks that were thought to be a unique attribute of humans.”

Conversely, people with a high understanding of AI know that AI works based on computer algorithms, not magic, so they don’t rely too much on it.

In March this year, a study found that sincere students use fewer Generative AI tools, and that AI dependence can lead to a decrease in self-efficacy and academic achievement. The researchers investigated and analyzed the frequency of AI learning and self-efficacy of learning after the end of the semester in 326 undergraduate students.

Sundas Azim, a professor at SZABIST University in Pakistan who conducted the study, said, “In the case of tasks conducted by students relying on Generative AI, AI produced similar responses, resulting in less classroom participation or discussion activities.” As a result, students with more AI use tended to have relatively lower average GPA.

It is analyzed that services such as ChatGPT can be effective when they need immediate help in their studies, but can have a negative impact on long-term learning and achievement.



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Alberta Follows Up Its Artificial Intelligence Data Centre Strategy with a Levy Framework

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3 hours ago

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September 4, 2025

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Alberta is introducing a levy framework for data centres powering artificial intelligence technologies, the Province recently announced.

Effective by the end of 2026, a 2% levy on computer hardware will apply to grid-connected data centres of 75 megawatts or greater, according to a statement from Alberta.

The levy will be fully offset against provincial corporate income taxes, the government says. Once a data centre becomes profitable and pays corporate income tax in Alberta, the levy will not result in any additional tax burden.

Data centres of 75MW or greater will be recognized as designated industrial properties, with property values assessed by the province. Land and buildings associated with data centres will be subject to municipal taxation.

The framework was forged through a six-week consultation with industry stakeholders, according to Nate Glubish, Minister of Technology and Innovation.

“Alberta’s government has a duty to ensure Albertans receive a fair deal from data centre investments,” the provincial minister remarked. “This approach strikes a balance that we believe is fair to industry and Albertans, while protecting Alberta’s competitive advantage.”

Glubish added that the Alberta government is also exploring other options. This includes a payment in lieu of taxes program that would allow companies to make predictable annual payments instead of fluctuating levy amounts, as well as a deferral program to ease cash-flow pressures during construction and early years of operation.

“After working closely with industry, we’re introducing a fair, predictable levy that ensures data centres pay their share for the infrastructure and services that support them,” commented Nate Horner, Minister of Finance.

“This approach provides stability for businesses while generating new revenue to support Alberta’s future,” he posits.

The decision builds on the Alberta Artificial Intelligence Data Centre Strategy, introduced in 2024.

The strategy aims to capture a larger share of the global AI data centre market, which is expected to exceed $820 billion by 2030 as Alberta becomes a data centre powerhouse within Canada.

However, the Province’s tactics have not gone uncriticized.



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