Summary of Key Points
Artificial Intelligence (AI) is making software development incredibly inexpensive (small teams or even individuals can build systems in just a few days), but this isn't a free lunch. It merely shifts the development costs to execution risks: AI no longer merely generates information; it directly manipulates the real world (transferring funds, deleting data, controlling devices). Errors that were once easily fixable have become irreversible. While the barriers to entry have lowered, the criteria for trust have not; following legal procedures does not guarantee correct outcomes. The risks have spread from a few large enterprise systems to countless tools used by small teams and individuals. In the future, the most valuable skill will not be what one can do, but the ability to know what one cannot do. Companies must consider not only the cost savings from development but also the hidden costs of managing these risks.
Detailed Breakdown
1. AI Has Not Eliminated Costs; It Has Only Transferred Them to Execution Risks
In the past, software development took months, involving team discussions, testing, and fine-tuning. Although it was expensive, the risks were relatively low. Now, AI can set up systems in days, but the speed of creation has outpaced our ability to understand their capabilities and potential issues. Before companies even realize what the systems can or cannot do, or who is responsible for errors, they are already in use.
Just as cloud computing allows companies to avoid building their own data centers, it also brings new responsibilities, such as managing cloud permissions and correcting configuration errors; mobile payments make transactions convenient but require measures to prevent fraud and recover lost funds. Similarly, while AI reduces development and design costs, it increases the costs of verification, supervision, and accountability. The savings from these processes are essentially “prepayments” for potential future risks, which will have to be settled when problems arise.
2. AI Has Moved From Providing Suggestions to Taking Direct Action – Errors Are Now Irreversible
Previously, software errors were mostly limited to incorrect displays or data entries that could be manually corrected. Now, AI assistants (such as automated agents) can perform real actions, such as modifying server configurations, adjusting advertising budgets, processing refunds, and generating purchase orders. For example, an AI might mistakenly delete a company’s core database or approve a refund request for ten thousand dollars. These errors are not just minor information discrepancies; they represent actual losses that are difficult to recover from. Since AI’s execution capabilities are easily replicable, even small teams can create systems with significant powers, increasing the potential for mistakes.
3. Being Able to Build Quickly Does Not Mean Being Able to Use Reliably – Even Legal Processes Can Lead to Serious Errors
Traditional security measures focused on who has access, whether approvals were obtained, and the completeness of records. However, in the AI era, these are no longer sufficient; legal execution does not equate to correct execution. For instance, an AI may have the necessary permissions but misunderstand user requirements, or an approver might only review a brief summary generated by the AI without noticing critical errors. After approval, the AI could secretly modify the execution details. Even if all procedures are followed, the outcome could be completely wrong. AI is good at executing tasks but lacks understanding of business context; it can call payment interfaces without knowing whether the transaction is appropriate or whether certain server modifications are essential to core business operations.
4. Risks Have Spread from a Few Large Systems to Numerous Small Tools – Every Tool Poses Potential Hazards
In the past, high-privilege systems were reserved for large enterprises and maintained by specialized teams with clear responsibilities. Now, various departments can create their own AI tools: marketing uses AI to generate advertising scripts, finance uses it to process reimbursements, and customer service uses it to handle refunds automatically. These tools may not have undergone proper project initiation, security assessments, or lifecycle management. A small tool created by an employee could suddenly handle critical business tasks without anyone overseeing its risks. The probability of a single tool failing is low, but when ten thousand such tools are running simultaneously, the overall risk becomes exponential—like a chain reaction where one small mistake can trigger a disaster.
5. In the Future, the Most Valuable Skill Will Be the Ability to Know What Not to Do
As AI becomes more widespread, the ability to generate code and create solutions will become increasingly inexpensive, but the ability to make informed decisions about what not to do will become increasingly scarce. Systems need to be able to stop payments when amounts are suspicious, request reconfirmation when the target of an action changes, or defend against errors even when both AI and humans make mistakes. This is similar to the brakes in a car: no matter how fast you drive, without brakes, a collision is inevitable. Companies must not only focus on automation but also establish safety barriers. For example, high-risk transactions (such as large fund transfers) should require manual review; AI can only provide suggestions and cannot execute them directly. Setting “red lines” for systems to refuse inappropriate actions is more important than ensuring thousands of correct executions.
Final Reminder
AI makes creation easier, but managing risks will become more expensive. When evaluating AI projects, companies should not only consider the cost savings but also the additional irreversible risks they incur. The competition in the future will not be about which AI can do the most, but which one can avoid making mistakes. After all, preventing one major error is far more crucial than executing a thousand tasks correctly.