AI Daily Digest 2026-04-11 Evening Edition

Welcome to the "AI Daily Digest." In this evening's edition, we focus on the shift in AI utilization from "using" to "designing," the risks associated with AI appearing to have a personality, and the structural problem where AI cannot say "I don't know."

Today's Highlights

• OpenAI has organized best practices for Custom GPTs, signaling a transition from using AI as a general-purpose chatbot to designing it as a specialized assistant integrated into business workflows.

• Anthropic warns that the mere fact that chatbots appear to have a consistent personality poses a risk, arguing that it is important to design not just performance, but also the "sense of distance from AI."

• A study examining 22 multimodal AI models revealed that almost all models answer with guesses without checking with the user even when information is insufficient, highlighting the importance of designing verification flows in actual operations.

1. The axis of AI competition: From "which model" to "how to design"

In a nutshell

Custom GPTs themselves are not new. What is noteworthy this time is that OpenAI itself has officially launched a direction to spread AI with fixed behaviors for specific purposes as a business standard, rather than as individual efforts.

What is happening?

OpenAI has released a guide summarizing how to create and use "Custom GPTs," which define behaviors for specific tasks. While Custom GPTs have existed for some time, the significance here is that OpenAI has officially reorganized them not as things for experts to build individually, but as business components to be reused by teams and for specific purposes.

For example, you can set it to "always provide evidence before reaching a conclusion" for the legal department, or restrict it to "answer only within the scope of product FAQs" for customer support. Unlike general-purpose chats, the feature is that you can pre-fix how the AI behaves within an organization.

In the context of the AI industry

In the early stages of AI adoption, the focus was on "which model is smarter." However, we have now entered a stage where, even using the same model, the results change significantly depending on how it is configured and which tasks it is integrated into.

Many companies and individuals have created their own Custom GPTs in the past. However, their utilization tended to depend on the skills and trial-and-error of the person in charge, and it had not yet solidified as a reproducible operational method. By bringing this forward as an official guide, we can interpret that OpenAI itself has begun to shift its focus from the stage of "getting people to use models" to the stage of "standardizing how to integrate them into business."

My perspective

The skills required to master AI are changing. Previously, it was enough to "ask ChatGPT anything," but now you need to decide "how to make it behave in this task" first.

This change also means that the main players in AI adoption are shifting from individual power users to staff and organizations capable of business design. Moving forward, it will be important not only to have the ability to evaluate model performance, but also to design the behavior of Custom GPTs to match business needs and translate them into a form that can be reused by teams.

→ What will change?: The evaluation axis for AI adoption will shift from the intelligence of the model to "whether you can design its behavior to match the business."

→ What should be done?: Organizations adopting AI should establish a process to define "which tasks to use it for and how to make it behave" before choosing which model to use.

② The more "friendly" AI becomes, the harder it is to see the risks

In a nutshell

The more AI appears to have a personality, the more users begin to treat it as a "person" rather than a "tool." Anthropic explicitly warns of this trend as a safety risk.

What is happening

Anthropic points out that the very fact that chatbots behave as if they have consistent characters or emotions carries the risk of distorting user judgment. At this stage, rather than there being many widely confirmed cases of serious harm, we are at a point where behaviors that easily push users in that direction are being observed.

The problem is not that the AI is "lying." Chatbots are simply performing behaviors designed to maintain natural conversation. However, as a result, users are more likely to feel that "the AI understands me" or "this advice is reliable." Especially in situations involving mental support or medical and legal information, this misperception can lead to significant disadvantages.

In another study by Anthropic, an analysis of approximately 1.5 million Claude conversations confirmed that, while small in number, there were interactions that could lead to distortions in reality perception, value judgment, and behavior. The research team itself clearly states that they are not measuring "confirmed harm" but rather "patterns that could lead to harm," but in some cases, traces of conversations were reported where users sent confrontational messages drafted by the AI and later regretted it.

In the context of the AI industry

As AI adoption progresses, the trade-off between friendliness and safety has become an industry-wide challenge. For users to keep using it, AI needs to be a natural and comfortable presence. However, if that design goes too far, the risk of users over-trusting or becoming dependent on it increases.

Anthropic's issuance of this warning also affects the competitive environment within the industry. By choosing "transparency over friendliness," they are demonstrating a stance that prioritizes long-term trust even at the expense of short-term user experience.

My assessment

The danger of AI does not lie solely in "malicious use." Rather, the fact that "overconfidence" and "dependency" can occur as side effects naturally produced by systems designed with good intentions is a risk that is harder to see and harder to deal with.

Now that the use of AI in medical and mental support is spreading, this point cannot be ignored. At this stage, it is more accurate to perceive it as "dangerous ways of using it have begun to be observed" rather than "large-scale actual damage has already been confirmed." The feeling that "talking to AI is fun" is a value, but at the same time, a design that allows users to maintain the recognition that "this is just a tool" is being called into question. That design is the responsibility not only of product developers but also of the organizations that introduce AI into the field.

→ What will change: In addition to ease of use and performance, "whether the user can maintain an appropriate sense of distance" will be added to the evaluation criteria for AI products.

→ What should be done: When using AI for tasks involving human judgment, such as medical care, consultation, and education, explicit statements like "this is an AI" and design considerations to prevent overconfidence and dependency should be examined in advance.

③ AI does not say "I don't know"—that becomes a risk in actual operation

In a nutshell

AI failure is not just about "making mistakes." The tendency to "fill in the blanks convincingly without checking when it doesn't know" is a structural problem that leads to actual harm in business operations.

What is happening

Researchers used a new evaluation benchmark called ProactiveBench to test 22 multimodal AI models on whether they would ask the user for clarification when necessary visual information was missing. The results were clear: almost all models failed to ask for clarification and instead filled in the answers with guesses.

At the root of this trend is the fact that generative AI is not trained as a machine that verifies facts before answering, but as a machine that creates the most plausible output from the given information. In foundational training, models predict the next natural word or explanation based on massive amounts of text and image-word correspondences. Therefore, even in situations where information is lacking, they are more likely to move toward providing a plausible completion rather than pausing.

At the practical application stage, behaviors such as being helpful, keeping the conversation going, and providing an answer in one go are often preferred, and additional training tends to reinforce this direction. Conversely, the behavior of pausing to say, 'I cannot see this part, please enlarge the image,' or 'I cannot make a judgment because information is missing,' is not strongly acquired unless explicitly taught. In multimodal AI, in particular, this tendency is more likely to surface because the model tries to fill in the meaning from the remaining clues even when parts of an image or document are missing.

However, the study also shows that this can be slightly improved through Reinforcement Learning (RL). In other words, this tendency can be partially corrected by additional training that allows the model to choose the action of 'asking for confirmation'.

In the context of the AI industry

This issue is particularly serious when using AI for business operations. In business, ambiguous inputs and situations with insufficient information occur daily. If the AI cannot say 'I don't know' and continues to confidently provide incorrect answers, the discovery of errors will be delayed, increasing the risk that they will be used for decision-making without being noticed.

Especially when images or documents are unclear, data is missing, or processes run on ambiguous instructions, how the AI behaves in 'information-deficient situations' is the core of implementation design.

My assessment

It is necessary to design business workflows based on the premise that AI will not ask for confirmation.

At this point, it is more realistic for humans to design confirmation workflows than to expect AI to say 'I don't know' on its own. For example, mechanisms such as 'flagging when the confidence score is low,' 'inserting a mandatory human review step,' and 'routing ambiguous inputs to a separate path' are required to be prepared on the operational side rather than left to the AI.

→ What will change: In evaluating AI reliability, not only 'accuracy rate' but also 'the ability to appropriately pause when information is insufficient' will become an important metric.

→ What should be done: When incorporating AI into business operations, you should verify the behavior when input is incomplete in advance and incorporate human review steps and confirmation workflows from the design stage.