Make the Judgment Visible

There is a kind of AI career advice that sounds true and still does not help much.

Learn the tools. Use agents. Automate your work. Build a portfolio. Publish online. Network. Stay current.

None of that is wrong. It is just too generic. Everyone serious is already doing some version of it, and most of it collapses into the same weak hiring signal: this person is aware that AI exists.

The more useful question is narrower:

What kind of work makes a hiring manager believe you can operate above execution?

I was thinking about this after watching Silicon Valley Girl’s interview with Shishir Mehrotra, the CEO of Superhuman and the company behind Grammarly. The video is framed around how to position yourself before the next AI phase. The strongest parts are not the tool demos. The strongest parts are about promotion ladders, judgment, and how to avoid being processed as just another applicant.

Mehrotra’s advice is blunt: the most interesting opportunities often do not start inside the recruiting funnel. They start because someone encountered an interesting piece of work, an argument, a project, or a conversation and thought: I want to spend time with this person.

That lands differently in an AI hiring market.

If execution is getting cheaper, the job search cannot only prove that you can execute. It has to prove that you can see the right problem, frame it, build around it, and judge the result.

The Recruiting Folder Is a Bad Interface

Mehrotra uses a simple image: the recruiting folder.

Once you apply cold, you are in a pile with everyone else who is looking for a job. That does not mean applications are useless. It means the interface is hostile to unusual candidates.

A hiring pipeline wants comparable units. It wants titles, keywords, years, tools, levels, and clean mappings between past role and open req. That works for some people. It is weaker for people whose value sits between product, design, engineering, research, and infrastructure.

The more interdisciplinary the work, the worse the recruiting folder gets at reading it.

A resume can say:

Senior Design Technologist
AI Infrastructure & Prototyping
Ex-Google

But that still leaves the real question unanswered:

Can this person walk into an ambiguous AI/product/workflow mess and make it legible?

The recruiting folder rarely discovers that on its own. Visible work does.

A good essay, teardown, prototype, demo, or technical note gives the hiring manager a better interface. It does not ask them to infer your judgment from a title. It shows the judgment directly.

AI Pushes the Ladder Up

The most useful framework in the interview is Mehrotra’s career ladder:

Problem -> Solution -> How -> Execution

At the beginning of a career, someone else may hand you the problem, the solution, and the how. Your job is to execute.

Then you start owning the how. You organize the work, sequence it, recruit help, manage dependencies, and get the thing shipped.

Then you start owning solutions. Someone hands you a problem, and you bring back approaches they did not already see.

At the top, you are handed a space and you identify the problem. The assignment is no longer “improve activation.” It becomes “we thought activation was the problem, but the deeper issue is trust, onboarding language, pricing, data access, or internal review latency.”

AI does not erase this ladder. It compresses the lower rungs and makes the upper rungs matter sooner.

If an agent can draft the first version, generate variants, write boilerplate, summarize research, sketch a component, produce code, test a path, and prepare a memo, then the scarce part is not typing. The scarce part is deciding what deserves to be typed.

That means the hiring signal moves from output volume to judgment:

• Did you find the real problem?
• Did you choose the right abstraction?
• Did you notice the constraint everyone else skipped?
• Did you design the review loop?
• Did you make the system safer, clearer, or easier to operate?
• Did you know when the AI output was plausible but wrong?

This is uncomfortable because many careers trained people to prove reliability through execution. The new market still needs execution, but it rewards people who can show command one or two levels above it.

Tool Fluency Is Not Enough

A lot of candidates will say some version of:

I use ChatGPT, Claude, Cursor, Gemini, Codex, Perplexity, NotebookLM, and agent workflows.

That is quickly becoming table stakes. It is like saying you use email, spreadsheets, and search.

The better signal is not that you use AI. It is that you understand where AI breaks when it enters real work.

Real work has context problems:

• Where does the agent get current information?
• What is allowed to be read?
• What is allowed to be changed?
• Which source wins when systems disagree?
• What must be reviewed by a human?
• What counts as evidence?
• What happens tomorrow when the conversation is gone?

Real work has timing problems:

• Does the assistant appear when the user needs it?
• Does it require the user to remember to ask?
• Does it interrupt or support flow?
• Does it catch mistakes early or only summarize after the damage is done?

Real work has trust problems:

• Can the user see why a suggestion appeared?
• Can the system cite the source of a claim?
• Can the user accept, reject, or defer cleanly?
• Can the organization audit what happened?

This is why the Superhuman Go part of the interview is more interesting than another agent demo. The product pitch is specific to Superhuman and Grammarly, but the underlying pattern matters: AI is moving from chat to situated assistance.

The assistant is not a place you go. It is a layer that appears where work is already happening.

Chat, Do, Assist

Mehrotra describes three metaphors for AI products:

chat:   I ask an AI something
do:     I give an AI a task to complete
assist: AI shows up inside the work before I ask

Most of the public imagination is still around chat. It feels magical because the interface is simple and anthropomorphic. You talk to the machine and it talks back.

The second metaphor is task execution. Give the system a queue. Ask it to take items off the list. This is where many coding agents, research agents, and operations agents are heading.

The third metaphor is the one I think matters most for product work: assist.

Assistive AI is not waiting in a separate tab. It is watching the work surface, comparing what you are doing against the relevant context, and intervening at the right moment.

Examples from the interview include:

• a fact checker that notices a claim while you write
• a legal or brand guardrail that flags risky language in place
• a placeholder filler that finds the quote, stat, or source you left in brackets
• a calendar assistant that notices a proposed time and checks availability
• a source checker that catches stale or unsupported claims before review

The important part is not the specific product. The important part is the interface shift.

If AI requires the user to remember to open a chat, paste context, ask the right question, copy the answer back, and verify it manually, then AI itself becomes another chore. The next useful systems reduce that burden by meeting the user inside the workflow.

That is a product and infrastructure problem, not just a model problem.

The Job Search Version of This

The job-search lesson is not simply “publish more.”

The lesson is: make your judgment visible in the same medium as the work you want to be hired for.

For me, that has become a daily operating practice.

I use AI tools every day, but the biggest shift is not that I am typing with a smarter assistant. The bigger shift is that I am designing the setup that lets agents do useful work while giving me a fast feedback loop.

Practically, that means I spend a lot of attention on task decomposition, project context, task/run contracts, workspace boundaries, and verification. If I am building or changing a feature, I frame the goal, give the agent enough repo context, define stop and acceptance conditions, and choose the right model surface for the moment.

Codex is usually where I do implementation. Claude or Gemini are useful for critique and alternate framing. Hermes or Pi are useful for longer or routed workflows. The point is not tool loyalty. The point is choosing the execution surface that matches the work.

Soul is the infrastructure I built because agent work scatters very quickly otherwise: one thread in Codex, one in Gemini, one in Claude, shell output somewhere else, and then a human has to remember what changed and whether it was checked. Soul keeps the task, run, session record, tool trace, diff, verifier output, review or waiver, and final handoff tied to the project.

So the daily loop is:

frame the work
-> delegate bounded execution
-> keep the agent running against fast feedback
-> inspect the evidence
-> make the human judgment about what gets accepted

That is the concrete version of visible judgment. It is not just having opinions about AI products. It is building and using the operating loop that makes agent work reviewable.

If you want to be hired for AI product strategy, publish product teardowns that show how you think about user workflow, risk, and adoption.

If you want to be hired for agent infrastructure, publish working notes that show how you think about memory, permissions, review, state, and receipts.

If you want to be hired as a design technologist, publish prototypes and explain the interaction model, not just the pixels.

If you want to be hired for applied AI inside enterprises, show how a messy work item becomes prepared enough for human judgment.

A generic portfolio says:

Here is what I made.

A stronger artifact says:

Here is the problem I noticed.
Here is the frame I used.
Here is the system I built to test the frame.
Here is what changed after contact with reality.
Here is what I would do next.

That is the difference between showing output and showing judgment.

Practice in Low-Stakes Rooms

Another useful point from the interview is that judgment needs practice, and practice needs low-stakes settings.

Mehrotra compares it to sports or music. You do not learn only during the recital. You learn in the basement, the driveway, the rehearsal room, the scrimmage.

Professional life is often the opposite. People try to learn judgment in the highest-stakes meeting, in front of the promotion committee, inside the critical launch, during the interview loop. That is a bad learning environment. Everyone is watching, so people become defensive. The cost of being wrong is too high.

Side projects, essays, teardown memos, demos, and small prototypes are lower-stakes rooms.

They let you practice questions like:

• What is the real user workflow?
• Which constraint matters most?
• What would make this system trustworthy?
• Where should the human stay in control?
• What evidence would change my mind?
• Which part should be automated, and which part should be made more legible?

That practice compounds. It also leaves a trail.

When someone later asks how you think, you do not have to claim good judgment. You can point to it.

The Eigen Question

Mehrotra also talks about what he calls an eigenquestion: the question that answers the most other questions.

That is a useful hiring lens.

In an interview, many candidates rush to answer. Senior people often slow down and search for the discriminating question.

If a team says, “We need an AI assistant for customer support,” the obvious questions are about model choice, integrations, latency, cost, and UX.

The better first question might be:

Which support decisions are reversible, and which ones create risk if the assistant is wrong?

That question changes the product shape. It separates drafting from action. It separates low-risk summarization from policy-bearing decisions. It affects permissions, audit logs, escalation, interface design, and rollout strategy.

Or if a team says, “We need agents to speed up engineering,” the useful first question may be:

What evidence would let us safely reduce review latency without reducing accountability?

That points toward prepared work items, verifier artifacts, diff scopes, ownership routes, rollback plans, and review receipts. It moves the conversation from agent autonomy theater to operational design.

This is the level of thinking that visible work should expose.

A Better Artifact Packet

If I were turning this into a practical job-search system, I would not start with a full personal rebrand. I would start with a small packet of artifacts.

Three pieces are enough:

1. A point-of-view essay

Not a prediction essay. A position essay.

For example:

AI assistants are moving from chat to situated workflow.
The hard parts are memory, permissions, provenance, review, and timing.
The winners will not be the products with the most generic agents.
They will be the products that understand where human judgment sits in the loop.

2. A product teardown

Pick one company or workflow and show how you would improve it.

Do not write fan fiction. Write like an operator:

• current workflow
• user pain
• hidden constraint
• proposed assistant behavior
• required context
• review and safety model
• prototype sketch
• risks
• next experiment

3. A working demo

A short screen recording can do more than another credential.

It does not have to be polished. It has to make the judgment visible:

• what did you choose to automate?
• where did you keep the human in control?
• what did the system remember?
• how did it cite or verify?
• what changed because the assistant was embedded in the work instead of sitting in a chat tab?

This packet does not replace a resume. It gives the resume somewhere to point.

The Better Outreach Note

The practical consequence is that outreach should stop sounding like a request for rescue.

Weak note:

I am looking for roles and would love to chat.

Better note:

I have been thinking about the shift from chatbots to workflow assistants.
I wrote a short teardown of where I think your product could make AI useful inside the work surface rather than beside it.
I also built a small prototype in a related direction.
Curious if this matches what you are seeing.

That is not begging to be evaluated. It is offering a useful thought.

It also changes the conversation. Instead of asking someone to decide whether you match a job description, you are inviting them to compare notes on a problem they probably care about.

That is how you get out of the folder.

The Takeaway

AI is making execution easier to produce and harder to evaluate. That means the valuable career signal moves upstream.

The market will still hire executors. But the interesting roles, especially in AI product and infrastructure, will go to people who can show:

• problem finding
• solution framing
• taste
• systems thinking
• review design
• situated prototypes
• judgment under ambiguity

Those qualities are difficult to prove inside a conventional application.

So the work has to travel ahead of the application.

Not as personal branding. Not as content marketing. As evidence.

The goal is simple:

Make it easy for the right person to encounter your judgment before they encounter your resume.

That is the real job-search lesson. Not “use AI.” Not “learn every tool.” Not “apply harder.”

Make the judgment visible.