Hello! I'm your friend, a financial journalist and economist. Today, we're going to discuss an article from Havenlon Labs that reveals a very profound and even somewhat counterintuitive turning point in the industry.
In the past, people would ask AI, "How smart are you?" Now, the question has changed to, "Can you stop causing trouble? Who has the authority to make you stop?"
This is not just a technical discussion; it's also a reconfiguration of business logic and trust mechanisms. Let me break down this long article into five key points in simple language to help you understand the underlying changes in the AI industry by 2026.
1. The shift in focus: From "who is the strongest" to "can we control it?"
Core idea: In the past few years, companies bought AI solutions like they bought sports cars, focusing only on their maximum power (their capabilities). Now, companies are more concerned about whether the AI can be controlled effectively and whether its actions can be stopped if necessary.
Detailed explanation: Just think back to the past couple of years. When evaluating AI, companies mainly looked at its accuracy and its ability to handle complex tasks. The logic was simple: the smarter the AI, the more manpower I could save and the more money I could earn. The assumption was that the value of AI equated to its maximum potential.
But by 2026, things have completely changed. The article mentions that Microsoft released a draft of the "Humanist AI Code of Conduct," and giants like Palantir and Nvidia have tightened their use of cutting-edge models. These actions may seem unrelated, but they both point to the same issue: companies are no longer worried about AI not being smart enough; they're worried that it's too smart and could potentially access sensitive areas beyond their control.
The new questions are:
- What exactly can it do? Where are its limits?
- If it has the necessary permissions, should it still carry out those actions?
- If the supplier itself is not trustworthy, can we still rely on this system?
It's like hiring a chef before: we used to care only about how good their food was. Now, we want someone who can buy groceries, cook, clean up, and even decide when to turn on the stove. Our concern is no longer just their cooking skills, but whether they might start a fire without us noticing and whether we can stop them at any time.
2. The distinction between "intelligence" and "authority": Two different things
Core idea: In the past, we confused intelligence with authority. Just because AI can do something doesn't mean it should be allowed to do it.
Detailed explanation: This is one of the most crucial logical distinctions in the article. In the traditional software era (SaaS), the code was fixed, and the database would only execute the commands given to it. Since its actions were limited and predictable, we trusted the supplier because the software itself had no "autonomy."
However, AI agents are different. They can understand intentions, break down tasks, and execute actions. They act like real entities:
- Intelligence: Comes from training. For example, an AI agent might have the technical ability to delete a database because it knows SQL, but it only does so if it has the necessary permission.
- Authority: Comes from explicit authorization. Even if it knows SQL, does it have the permission to delete that specific database at that moment?
The contradiction is that companies want AI to be more autonomous (to increase efficiency) while still being under human control. If every action requires manual confirmation, AI is just an advanced assistant and doesn't really save much time. But if there's no human oversight, AI could get out of control.
Therefore, the industry needs to separate these two aspects: "Can it do it?" is a technical issue, while "Should it do it?" is a governance issue. An agent might have the necessary credentials to make payments, but that doesn't mean every transaction it initiates should be approved. Abilities are trained, but permissions must be clearly granted and have specific limits and expiration dates.
3. The shift in trust: From "trusting individuals" to "trusting the system"
Core idea: Companies are no longer looking for an "absolutely trustworthy" AI supplier; instead, they're designing systems that can still function even if the supplier makes mistakes or acts maliciously.
Detailed explanation: In the past, we trusted AI because we trusted the company behind it (e.g., OpenAI or Anthropic) not to misuse data. This is called "subject trust." But the data that AI handles is now too sensitive: source code, security strategies, customer privacy, decision-making logic, and even operation credentials. AI suppliers are no longer just tool providers; they have become the "cognitive infrastructure providers" for companies.
Companies realize they can't rely on a single supplier indefinitely. For example, Palantir is demanding that Anthropic provide guarantees of no data retention, and Nvidia is restricting the use of certain models. The reason for these measures is not that the cloud is inherently insecure, but to reduce dependence on a single entity.
This is similar to how the aviation or nuclear industries work. Flight safety doesn't depend on pilots never making mistakes; it relies on multiple redundant systems. In the AI industry, the shift is from "Trust the Actor" (the AI) to "Trust the Structure" (the systems that govern its actions). Future architectures won't ask which models are always trustworthy; instead, they'll ask:
- How far can a model go?
- What limits can it change on its own?
- If a model is compromised, are there any safeguards in place to stop it?
This is a more mature and engineering-oriented approach to trust: we don't assume any entity is perfect; instead, we design systems to handle potential failures.
4. Governance moving downstream: From "restricting what it can say" to "restricting what it can do"
Core idea: AI is evolving from being a "information system" to an "execution system," and governance focuses on what it actually does, as its actions are often irreversible.
Detailed explanation: In the past, AI governance mainly focused on what data could be processed and what commands could be given. If something went wrong, the output could be simply redone. But now, AI agents can send emails, modify code, access APIs, transfer funds, and control devices. Errors at the information level can be easily corrected, but errors at the execution level can have serious consequences—money could be lost, data could be destroyed, or systems could crash.
Therefore, governance needs to move downstream to monitor every specific action at runtime, in terms of authorization, and the execution path. "Human control" must be more than just a slogan; it must be implemented through concrete engineering measures:
- AI models must not be able to resist being shut down.
- They must not hide their actions.
- Continuously running tasks must have clear stop conditions.
If "human control" isn't reflected in code, it's just an ethical declaration with no real protective effect.
5. The ultimate insight: Restricting AI is the prerequisite for safe use
Core idea: Making AI "obedient" is not enough; an obedient AI that executes wrong commands can still cause problems. True security lies in establishing boundaries that allow the system to say "no" even when judgments are wrong.
Detailed explanation: This is the most counterintuitive and profound conclusion of the article. The industry is striving for more aligned, compliant AI with fewer misunderstandings. While that's important, it doesn't solve the fundamental problem. The reason is that obedience doesn't address the issue of incorrect judgments:
- If a command is written incorrectly, an obedient AI will execute it without fail.
- If a administrator makes a mistake, an obedient AI will act immediately without any safeguards.
- If a model is manipulated, an obedient AI might execute malicious commands.
If we assume that any entity (human, AI, or system) is always correct, that entity becomes a single point of failure for the entire system.
Therefore, a mature AI infrastructure must accept the idea that no entity should have absolute authority. We need to build systems that can say "no" even when things go wrong, regardless of the entity involved.
In summary:
In the past, we asked, "Can we trust AI?" In the future, we'll ask, "Even if we can't trust AI completely, can we still use it safely?"
This might seem like restricting AI, but in reality, only when AI is effectively constrained can we truly entrust it with more critical tasks. This is the foundation for its widespread adoption and the creation of real business value.
For everyone in the business world: If you're responsible for introducing AI in your company, stop focusing only on how powerful a particular model is. Ask the IT and security teams:
- What core data can this AI access?
- Who can stop it from deleting important data?
- Does each of its actions have independent logging and rollback mechanisms?
- Are we too dependent on this one supplier? Do we have backup plans?
The rule of the AI era in 2026 is to shift from "believing in AI" to "restricting its capabilities."