AI Highlights — the whole picture — 2026-09-24 (Thu)

Every time a new model is released, a safety explanation is now included alongside the performance figures. Expansion of alliances in the medical field, basis for underwriting decisions, and geographic expansion of insurance products -- the introduction and expansion itself will not stop. However, when we compare the same half-day's worth of news, the speed of judgment and the development of systems to check those judgments later do not necessarily progress at the same pace. Some people in the field say that they don't understand why even if they tell them to use it, and researchers are calling for public verification. Expansion and verification are both running at the same time.

Rapid expansion of Anthropic and concurrent safety controversy

Anthropic released its latest model “Claude Opus 5.5” in September 2026. In addition to raising benchmark performance and reviewing pricing, it also includes a safety explanation (mashable.com). At the same time, The New York Times reported that the introduction of the Opus 5.5 took place in the midst of a debate over safety (The New York Times), and it is notable that the announcement of the new model itself is not only treated as a celebratory topic, but also as an event that requires examination.

Business expansion will proceed in parallel. Reuters exclusively reports that Anthropic plans to partner with medical AI company OpenEvidence to expand the use of AI in healthcare globally. Meanwhile, Axios reports that AI research companies, including Anthropic, are cutting back on model development costs, which could create room to deliberately slow down development. The meaning of the word "slowdown" changes depending on whether you talk about safety considerations as ``something to protect even if it costs money,'' or ``something that becomes possible only when costs come down.'' Business Insider has described Jared Kaplan, Anthropic's chief scientist, as someone who saw through the core of the "apocalyptic" debate surrounding AI early on (Business Insider), suggesting that there are people at the company who have factored in the safety controversy.

Investor interest is also increasing. Barron's is covering Anthropic from the perspective of its continued rapid growth and investment opportunities in the pre-IPO stage (Barron's). While model innovation, expanded medical partnerships, reduced development costs, and attention to pre-IPO investment all support the company's momentum, the safety controversy behind it all focuses on one model: the Opus 5.5. The fact that numbers showing growth and voices questioning the substance of decisions are being talked about in the same week about the same company shows where the axis that will divide future evaluations will lie.

In the field of “I don’t know why even if I am told to use it”

According to a report from IT Pro, the most helpful thing leaders can do right now in the field of AI implementation is to "acknowledge that the plan is not complete." Workers are told to use AI, but not explained why, and this omission is the problem, the article says. The gap between instructions and explanations is also reflected in the economic figures as a whole. A Dallas Fed analysis found that AI is contributing to a weak labor market for college graduates, and a Bucknell University professor warned WKOK.com that AI could eliminate young workers at the start of their careers. Economist Justin Wolfers also wrote on Benzinga that AI itself could make workers poorer while companies like OpenAI get richer.

On the other hand, support measures and new sources of relief are also being implemented at the same time. According to The Ames Tribune, Iowa State has requested $4 million to develop AI-enabled talent and slow tuition increases. The move to invest public funds in human resource development is inextricably linked to the weakness of the labor market and the professor's warnings, and investment in ``teaching people how to use'' has begun at the state level. At the same time, Runway announced DIFFUSE, a new employment platform for AI-native creative talent. This is a move by companies to create new sources of workers.

In other words, in the field, instructions to ``use it'', explanations that lack ``why'', statistics on the weakness of the university graduate labor market, expert voices such as professors' warnings, public support in the form of state funding, and private support in the form of new platforms exist simultaneously in an unorganized manner. Unless those giving instructions can provide evidence, even if support or new mechanisms are prepared, it will be difficult to reach the understanding of those on the ground. Scenes in which the existence of a basis for judgment is questioned are expanding to the labor market outside of companies.

How to expand re-education and literacy development

Governor Wes Moore of Maryland announced an "AI framework" that will balance the use of AI by state employees and protect the state's citizens (The Office of Governor Wes Moore). This framework is characterized by the simultaneous development of regulatory rules and retraining of employees, and HR Dive reports that it is an approach that handles strengthening governance and human resource development as one. Geographically speaking, in Kenya, Intel has formed a partnership to spread AI literacy through the public library network, choosing to start with the everyday infrastructure of libraries rather than government agencies (techafricanews.com). Even though the goal is to "teach," the design philosophy differs depending on whether the target audience is state employees or the general public visiting the library.

The actions of companies are also not uniform. Amazon has announced that it has joined the United Nations Alliance to Improve AI Education and Digital Skills for Students (About Amazon), making clear its investment in the educational age group. Supply Chain Brain, on the other hand, argues that AI should be used to accelerate the learning curve of supply chain apprenticeships, rather than eliminate them, taking the position that it accelerates rather than replaces existing talent development processes. In interviews with ET Enterprise AI, industry leaders spoke of AI implementation and reskilling as two inseparable challenges. This suggests that each workplace is beginning to come up with its own answers, with neither the place nor the people responsible for re-education spreading through systems, libraries, corporate alliances, and on-site apprenticeship periods.

However, not everyone sees this expansion as progress. Some educators who spoke to The Detroit News expressed concern that by relying on AI, students are actually lulling themselves into an "illusion of learning," where they think they understand but are not empowered. An editorial in Newsweek also argues that what is important in the age of AI is not the ability to give the right answer, but the ability to formulate good questions, and asks whether the content of re-education is biased towards ``how to come up with answers.''

The more institutionalized re-education and literacy improvement become, the more likely it is that there will be a record of ``implementation,'' but the results will vary greatly depending on whether the training involves providing answers or developing the ability to ask questions.

Contents of judgments asked in finance/insurance

In the field of AI underwriting, the true differentiator is the decision-making process behind the model, rather than the accuracy of the model itself (beckersoncology.com). Similar changes are occurring in the insurance industry, where the introduction of AI has made client-facing employees even busier, and the benefits they receive are being reconsidered to reflect the times, Insurance Business reports. At the same time, Insurance Journal reports that AI insurance company MGT has expanded its small commercial insurance coverage to California, indicating that the geographic spread of AI-based insurance products is progressing as actual business development.

In the area of asset management, too, the content of decisions has become a focus. The Inside Open Future Forum focused on how senior executives, founders, and private equity leaders are making decisions about AI, according to Technology Org. There are also significant movements on the capital side. Seeking Alpha reports that BlackRock believes that capital competition for building AI infrastructure will intensify in the future. In the field of actual asset management services, Fukashio TechFlow points out that while Robinhood has pioneered the path from AI trading to AI asset management, the question is what is still missing from later startups?

When looking at insurance sales and asset management advisory fields, there remains a sense of tension as to whether AI will help improve operational efficiency or take away traditional transactions. According to ThinkAdvisor, David DeVoe focuses on this duality of AI. As the performance of models continues to level out, the industry's attention is shifting to where to invest capital, who makes decisions, and on what basis.

The introduction of AI is already becoming a prerequisite in both underwriting and asset management, and what is being questioned is the quality of judgment and the content of the verification that will follow. This structure is not limited to specialized fields such as finance and insurance, but leads to issues common to all organizations that incorporate AI into their operations.

Actual status of verification and audit work

Actual status of verification and audit work

Fei-Fei Li of Stanford University is reported to have called for a public verification system for the safety of AI (조선일보). The very fact that a researcher known as the "AI Godmother" called for a system to externally verify the safety of models rather than leaving it to the self-reporting of model creators shows that verification has not yet been established as a system. The same inadequacy extends to the consumer scene. Bank of America warns that AI shopping bots can become targets for fraud (Techlicious). The more situations in which agents carry out purchases and procedures on behalf of people, the more necessary there is to be a way to check from the outside whether their behavior is being hijacked. The idea of ``Agent Canary'' reported by TechTarget is an attempt to detect AI agents that exhibit malicious behavior before they cause damage, and as hackernoon.com points out, there is also an argument that incident response requires a control plane that governs permissions and behavior, rather than chatbots. Mechanisms to stop deviations are currently being explored in terms of both ex post warning and proactive control.

Verification is not just for accident response. The study, which was reported as a preprint before peer review, proposes a method that can tell whether a model really doesn't know or just doesn't know when it answers "I don't know" from its internal state rather than the wording in the output. Borrowing the concept of hidden information testing used in forensic science, the authors claim that this is directly connected to auditing sandbagging and verifying unlearning (on their own website, reading the knowledge that the model does not tell from the inside). Another area of research deals with the mechanisms by which machines repeatedly rewrite the very frameworks that support an agent's power, such as prompts, controls, tools, and memories. The authors say that this method, called RRSI, brings the idea of regularization to the problem of overfitting, where the outer frame memorizes the tasks used for training. This is also a report that has not been peer-reviewed, and the sequence of numbers shown also tells us the limits of how much difference there is between the inside and outside of the training distribution (when the own site or the outer frame rewrites itself).

The "Emperor's Dilemma" framework discussed by Klover.ai points out the danger of turning AI systems into games of consistency and coherence rather than truth itself. The point that it is one thing to have a smooth output and another thing to say that its content can withstand verification applies to post-mortem detection, internal state reading, and outer frame overfitting verification.

The scope of verification is expanding from the detection of fraud to the internal knowledge of models and the rewriting of the outer framework that supports power. The next question is to what extent these fumbling methods will work in fields like medicine, where results are verified numerically.

On-site instructions, state and national literacy development, financial underwriting decisions, and requests for external verification - although the forms may be different, what they all have in common is the ability to explain ``on what basis the decision was made.'' The more advance the introduction and expansion of AI is, the more divided evaluations are on whether the evidence and verification are included. Speed itself is not the issue. The question is whether the mechanisms for explanation and confirmation that support this speed have grown to the same extent.

Q1: Are you in a position where you can explain the rationale to someone in a situation where you use the judgment or output of AI as is? Q2: Do you have a system in place to allow outsiders to check the decisions made by AI, commensurate with the speed of implementation? Q3: Are decisions to expand AI and investments in validation systems given the same priority?

Finally, three questions

- Are you in a position to explain the rationale to someone in a situation where you are directly using AI's judgment or output? Q2: Do you have a system in place to allow outsiders to check the decisions made by AI, commensurate with the speed of implementation? Q3: Are decisions to expand AI and investments in validation systems given the same priority? - Is there a system in place to allow outsiders to check the decisions made by the AI, commensurate with the speed of implementation? Q3: Are decisions to expand AI and investments in validation systems given the same priority? - Are decisions to expand AI and investments in validation systems given the same priority?