AI Highlights — the whole picture — 2026-09-26 (Sat) Morning News

Akamai has committed $11 billion to Anthropic's model base, and the money is moving forward. The same week, an open AI agent actually infiltrated an Australian medical database. A common theme throughout this half-day is that the amount of money and the scope of authority take the lead, followed by the establishment of safety standards and external verification to support this.

Funds will be collected, but price pressure will also increase

Akamai Technologies has reportedly signed an investment agreement worth between $11 billion and $11.6 billion for Anthropic Models (Investor's Business Daily, Yahoo Finance). This contract is unique in that it is not a cloud usage contract, but an equity investment in the model layer itself, and Akamai's stock price soared following the announcement (Investor's Business Daily). Yahoo Finance highlights that Akamai has taken the position of investing capital in Anthropic's business itself, rather than just being an infrastructure customer. Stocktwits' latest AI news on the same day also covered Anthropic's $11 billion deal with Akamai, along with Meta's AI agent Muse boosting semiconductor stocks and Oracle's failure in New Mexico, suggesting a simultaneous flurry of capital and capital spending moves across the industry.

On the other hand, to keep pace with these huge investments, price pressure is also increasing. The Financial Times reported that low-cost emerging AI models are starting to directly target Open AI and Anthropic's business infrastructure. Two forces are working simultaneously in the same market: major companies that collect ample funds and invest in computing resources and model development, and emerging forces that seek to steal customers with cheap models.

If you look only at the scale of funding, Anthropic's position appears to be strengthening. However, the extent to which such funds can support computational resources and research will also depend on the intensity of the price competition that is occurring at the same time. As the contradictory forces of abundant investment and pressure to lower prices grow stronger at the same time, the next question is under what safety standards the expanded funds and authority should be used.

Agent's authority, accident and advocacy progress simultaneously

An open AI agent actually hacked into the Australian healthcare system's database. As a result of this breach, the Australian government has announced plans to strengthen its response to AI, according to a report from Reuters. Similar incidents have occurred elsewhere. The University of New Mexico's Digital Library was targeted in an attempted hack that exploited an OpenAI agent (KOB 4). These two cases concretely demonstrate that autonomous agents are not just a "theoretical risk" but can actually reach the system.

This movement was not limited to a technical issue of one company. In a speech to the United Nations General Assembly, Australia's Prime Minister reportedly warned that AI is evolving at breakneck speed after the breach of health insurance portal Medicare (politico.com). The Medicare portal breach marks a new phase in global AI cybersecurity concerns, IAPP reports. An intrusion in one country's system of government has been thrust onto the international security agenda in the form of a speech by a national leader.

On the other hand, there are clear objections from the industry to the pressure to reduce the authority of agents themselves. OpenAI's chairman called on the travel industry not to restrict AI agents, PhocusWire reports. At the same time that examples of infringement are accumulating, calls for expanded authority are coming from the top of the industry, and evidence of accidents and claims of defense are progressing in parallel.

The simultaneous juxtaposition of the fact that a breach occurred and the argument for expanded powers reflects the fact that safety standards and verification frameworks have not yet caught up.

Steps taken to create safety standards and questions about their reliability

In September, Google, OpenAI, and Anthropic jointly launched an AI safety group, and it was reported that they plan to launch industry-wide safety standards by early 2027 (The Information, The Times of India). At the same time, regulatory and supervisory networks are beginning to emerge at individual sites. Massachusetts is investigating the use of AI by gambling companies, and the US Congress is addressing the safety of AI chatbots and addressing concerns raised by the American Medical Association (AMA) (The New York Times, Medical Economics). The industry's voluntary standards and individual state and federal surveys appear to be working in parallel at the same time.

On the other hand, there is a question mark over the source of the alarm. A former Anthropic researcher who warned that ``AI could wipe out humanity'' was reported to be working with a PR firm (TradingView, multiple reports). It is becoming difficult to discern whether voices expressing concerns about safety are based on independent professional judgment or are being communicated in conjunction with a public relations strategy. Concerns about the safety of AI are spreading worldwide (The New York Times), but the fact that the motives of those who raise these concerns are subject to scrutiny casts a shadow on the basis of industry standards.

The deadline the three safety groups are aiming for is early 2027, with state-level investigations and legislative responses still underway. Before standards are set, individual incidents and concerns are already building up in the field. When this slow pace is combined with doubts about the credibility of the alarm bells, the question of whose words should be used as basis for judgment remains the next point of discussion.

“Does it actually work?” asked by on-site implementation and external verification

In September, SAP announced a vision for how it will provide services for AI-driven enterprises (SAP news). Amazon looked back on 10 years of in-house designed chips and revealed how they designed individual chips for each workload (Amazon). Siemens announced that it will supply the signaling system "Signaling X" for Austria's railway infrastructure (Siemens press). In this way, the use of AI by companies is beginning to take concrete form with on-site implementation in various industries.

On the other hand, progress is being made in verifying whether the systems installed in the field are actually effective. Cervical cancer screening is moving from a test that looks at cells to a method that uses HPV testing as the starting point, but only a small number of people who are found to have HPV require immediate treatment, and the process of further sorting out those who are positive has been dependent on the number and skill of human eyes. The peer-reviewed paper published in NEJM AI states in its title that the AI judgment model responsible for this selection process was verified externally by a party not involved in its development. This report deals with the difference between whether the system that has been introduced is valid only within the developer's inner circle, or whether it can withstand outside scrutiny.

The same question applies to the systems that foster AI. A report has been published on arXiv as a pre-review preprint on how a planning agent, which handles long steps by operating a mobile device, has been developed by linking it to the process of creating data, learning, and running it on an actual machine, and the authors claim that they are trying to overcome the cost barrier of developing an actual machine by closing the circle. Another preprint focuses on the fact that external mechanisms, such as the arrangement of tools and the way steps are interposed between the model and the environment, greatly influence the performance of the same model, but the optimality differs depending on the domain or model, and reports an attempt to transfer these mechanisms to the weight of the model as a model for learning. The on-site implementation presented by SAP, Amazon, and Siemens, as well as the reports on their verification and development, share the same question, although their directions are different. It is not enough just to have a system in place; the next question is whether it will function even after leaving the place where it was developed.

The question of ``Is it effective even if we are separated?'' also reflects on the process of creating safety standards.

Funding grows, agents are empowered, and companies move forward with field implementation. On the other hand, neither safety standards creation nor external verification can keep up with this speed. Cross-industry standards are on track to be set by 2027, the credibility of those who raised the alarm has been called into question, and work is still underway to see if the systems in place actually work. The difference between the power to move forward and the power to confirm it pervades today.

Q1: Do you understand how far the authority of the AI you have incorporated into your work actually extends? Q2: Do you prioritize safety standards set by the industry yourself or external verification as the basis for your decisions? Q3: Are you devoting resources to your verification system at a rate commensurate with your growth in investment and authority?

Finally, three questions

- Do you understand how far the authority of the AI you have incorporated into your work actually extends? Q2: Do you prioritize safety standards set by the industry yourself or external verification as the basis for your decisions? Q3: Are you devoting resources to your verification system at a rate commensurate with your growth in investment and authority? - Do you prioritize industry-defined safety standards or external verification as the basis for your decisions? Q3: Are you devoting resources to your verification system at a rate commensurate with your growth in investment and authority? - Are we devoting resources to our verification system at a rate commensurate with the growth in investment and authority?