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The flow to get right first

• A key issue has resurfaced: AI can look convincing while being “only smart-looking.” Examples show AI describing images it hasn’t actually seen, and answers shifting based on nothing more than the conversation flow—highlighting the need for a way of thinking that doesn’t take results at face value [1][2][4].
• On the corporate side, the race to secure the electricity, buildings, and funding needed to run AI is getting even more intense. With major construction investments and large-scale fundraising coming in waves, AI is moving from a “technology to try” toward a technology that expands the foundations of society [3][5].
• In day-to-day work, AI is starting to be treated less like a tool and more like a collaborating partner. Along with moves by Microsoft and Mercari, more companies are also entrusting AI with substantial tasks using small teams—suggesting that work itself may change significantly [6][9][13][14].
• At the same time, the more useful AI becomes, the more important safety checks and usage rules are. Fixes to prevent data exfiltration, revisions to development practices, and improvements to review processes all point to the need for preparedness on the user side [7][12][15][20].
• A practical way to start experimenting immediately is to use a method that doesn’t just correct things with a long sentence, but instead sets pass/fail criteria first and verifies against them. By clearly defining what “success” looks like before handing the task to AI, you can reduce unnecessary rework [15].

Going forward, it’s less about “can we use it?” and more about designing so you can’t blindly trust it

• AI is likely to move further into more jobs. In addition to writing text and organizing meetings, it may increasingly assist with judgment and management tasks as well [6][13][16].
• However, because more weaknesses like the ones seen in this round are hard to detect just from appearances, it won’t be easy to use AI in a way that expects one-shot correctness. Instead of adopting AI outputs directly, the trend is likely to shift toward using them in formats that are easier to verify [1][2][7][15].
• For companies, investment in electricity and facilities required to run AI will likely keep growing. That suggests that companies that secure the foundations—not just win on small features—may have the edge [3][5].
• Work processes may also split: what gets delegated to AI versus what humans review. Particularly, the value of final checks, decision-making, and human-to-human responses may rise—meaning human roles could shift from “making” to “choosing and verifying.” [13][14][20]
• On the safety side, the more AI you use, the more important it becomes to clarify who did what and where it stops. Going forward, designing to avoid cutting too many corners on verification—trading away convenience for safety will likely be key [10][17][19].

A 5-minute way to use AI by setting “pass criteria” up front

Instead of repeatedly asking AI to rewrite its answer, this method sets what counts as success first. Just doing this reduces the number of times you have to redo work and makes it easier to judge the quality of AI’s output [15].

Steps

1. Write in one sentence what you want AI to do

   • Example: “I want you to rewrite this text to be clearer for someone who’s reading it for the first time.”

2. Define three pass criteria

   • Example: “Short,” “Don’t use difficult words,” and “Don’t change the original meaning.”
   • If possible, include numbers—e.g., “Within 3 paragraphs” or “No more than 5 bullet points.”

3. Tell AI those conditions first

   • Example: “Rewrite the text following these conditions: 1. Within 200 characters 2. Don’t use difficult words 3. Don’t change the original meaning.”

4. Test with three examples

   • Use three examples: one that seems likely to work, one that’s slightly more challenging, and one that might fail—then confirm whether AI can comply with the criteria.

5. Fix only what falls apart

   • For instance, if it’s “short but the meaning shifts,” add guidance about how to paraphrase without changing meaning.
   • If it’s “easy to understand but too long,” clarify the character limit.

Where this is especially useful

• Drafting emails
• Summarizing meeting notes
• Rewriting and paraphrasing text
• Creating work procedure manuals

The key point of this approach is that you share the correct format at the start rather than forcing AI to make the same kind of corrections over and over. As a result, you can use AI not as an “always helpful, but inconsistent partner,” but as a true work buddy that follows your conditions.