Markets are pricing in the cheap, cheap cost of building (goodbye, SaaS giants!). CXOs (chief executives of every stripe) are wringing their hands and sprouting AI investments like dandelions in a field. Experiments are running amok. Productivity has 10xed, or is it 1.1xed, or is it just spending that is x-ing at all? (View Highlight)
Being an employee at the center of an “AI transformation” feels like whiplash crossed with Wile E. Coyote chasing the Road Runner. Impossible to keep up. Claude is the best thing since sliced bread! No wait, Codex is king! No wait, the future is open source! And my security team still has not greenlit a frontier model. (True story I heard last week.) (View Highlight)
These stories have played in my head for a few years, exploding into a Times Square-level cacophony this year. My company, Sundial.ai, was formed before ChatGPT. Our sundial.so email addresses are relics of ye olden days. We had to cross the chasm into an AI-native way of working ourselves. (View Highlight)
Working with our customers, I have spent hundreds of hours with everyone from the savviest post-AI-boom startups to decades-old Fortune 500 companies. I am just as riveted by how adoption happens as by what the technology enables. Even if AI improves zilch for the next decade, we are still in the Stone Age of using what already exists. The bottleneck is our human capacity to adapt. (View Highlight)
• Part 1: Discover positive belief. Step 1: automate something you hate.
• Part 2: Get some tangible wins. Steps 2 to 4: automate a small, unloved team process, goal on the outcome, make the win loud.
• Part 3: Turn wins into systems. Steps 5 to 7: map the work, earn trust with evals, refactor what repeats.
• Part 4: Recompose for abundance. Steps 8 to 10: tell an abundant story, find the new bottleneck, change what the organization rewards. (View Highlight)
Part 1: Discover positive belief
I see this pattern over and over. A CEO reads that competitors are cutting costs with AI and blasts the company with a directive: “Become AI-first or get left behind.” Everyone scrambles, because fear makes for great ultimatums. By the time it reaches middle management, the directive has congealed into a mandate with key performance indicators attached. Now the manager’s job is to look like the team is transforming. So they buy the licenses, schedule the trainings, and put “AI” on the roadmap.
It does not work. Fear makes people optimize for appearances instead of spelunking the true edges of transformation. The prize is not “yay, we cut 30% of costs” or “hooray, we are not getting left behind.” You will never win the popular vote on those. The prize is a way of working that is more creative, more impactful and, dare we say it, fun.
That is the hypothesis worth chasing. CEO, vice president, middle manager, IC: you have to stay open to the possibility that AI gives people more room to create, decide, connect, and do work they care about. You do not have to believe it yet. Skepticism is fine. Let the possibility marinate like sweet, savory teriyaki readying for the grill. You only need to loosen your guard enough to find out. (View Highlight)
Step 1: Automate something you hate
The first experiment matters. Most people sit down to “try AI” and ask ChatGPT or Claude to do a part of their job they like and are good at. I used to lob over prompts such as “design me an app that…” or “write me an essay that…”
Two predictable reactions:
A) “HAH, this thing sucks. It is nowhere near as good as me.”
B) “SHIT, this result is actually passable. What does this mean for my future?”
Neither one helped. Do not begin a diet by giving up the foods you love. Our souls need proof of work, and the effort worth keeping is the effort you would have chosen anyway.
Instead, stop doing what you hate.
I am the opposite of a detailed person. Remembering where I need to follow up is my version of the third level of hell. One of the first things I built was a little system that pings me when I have left something unanswered too long. Then I thought, you know what else I do not love? Drafting the follow-up message. So that went next. Then the forms. I would rather do burpees than fill out long, fiddly forms.
Piece by piece, the things I am not into started taking less of my time: contracts, balance sheets, research, to-do lists, digital dumpster diving. My excitement grew in proportion to my relief, and my ideas got bolder.
Aim AI at the chores instead of the craft and the whole thing flips. It stops being the thing coming for what you are good at. It becomes the thing clearing junk out of the way so you can do more of it.
You cannot argue anyone into that flip. They have to feel it.
I was at an executive roundtable where the conversation about AI was bleak. Everyone was being squeezed to do three times more with half the time or people. Then someone asked, “What has been a good use of AI in your life?” One vice president after another told the same story: “I hate writing status updates. Now AI does most of it and gives me back my Friday afternoon.” The mood in the room shifted.
A leader cannot mandate enthusiasm. A leader can arrange for relief. (View Highlight)
What to do If you manage a team
• Automate one thing you hate this week. Show your team the result.
• Ask each person to name one task they dread. Give them time and cover to make it disappear. If you are an individual contributor
• Pick the most tedious part of your week. Work or home, either counts.
• If you do not know where to start, ask the AI itself: “I hate doing [task]. Give me a step-by-step plan to automate it.”
Easy starting points: choosing a restaurant for date night, loading a school calendar, summarizing competitor news, turning a grocery plan into a shopping list, drafting a follow-up. (View Highlight)
Part 2: Get some tangible wins
Once a few people have felt that relief, turn the feeling into a win on real work. Do the same thing again, one level up.
Step 2: Automate a small, unloved team process
The magic words are team, small, and unloved.
Team, because personal automation convinces one person and a shared process convinces a group. Small, because you want a win in days, not months. Unloved, for the same reason as Part 1.
Meetings are a fertile place to look. They generate the same little pile of work every time. Beforehand, someone researches the people, digs up old context, pulls the latest numbers. Afterward, someone writes notes, updates a system of record, chases follow-ups. And nobody ever gets to whether the hour was worth it.
These jobs also have clear triggers. The customer call starts in three hours. The metrics review happens every Tuesday. The Zoom transcript just arrived. Triggers are what make a workflow suited to an agent: an AI system that runs several steps on its own instead of waiting for a person to prompt it at every step. (View Highlight)
Here are three examples. 1. Prepare for a customer meeting
At Sundial, every customer call used to begin with the same scavenger hunt. Who is this person? What is happening at the company? What happened the last time we spoke? Someone searched LinkedIn, asked ChatGPT or Claude about the company, dug through the customer relationship management system (CRM), and reconciled it all before the call. Nobody enjoyed updating the CRM and Slack afterward either, so the context was missing again next time.
One of our teammates built an agent named Dwight, after the character from The Office. Yes, it had Dwight’s personality. Why not get a few chuckles out of it?
A few hours before each meeting, Dwight read the person’s profile, researched the company, pulled our old CRM notes, and delivered a sharp brief. The reaction was immediate: “Wow, this is great. It saved us so much time!”
Nobody needed a speech about AI transformation. People started imagining what Dwight could do next. Prepare a sales deck? Run prospecting? One useful agent created both relief and appetite. 2. Prepare for a weekly metrics review
At many companies, one or two people lose a day or two every week getting ready for a metrics meeting. They pull data from several tables, reconcile numbers that do not match, investigate what changed. An analysis agent can do most of that first pass. If activation drops, it checks whether the decline came from one product, one geography, or one channel.
People still verify the analysis and decide what to do. But the meeting opens with “What should we do?” instead of half an hour arguing about whose spreadsheet is current. 3. Give immediate meeting feedback
I want to give feedback after meetings, and I would love to receive it. Alas, I remember far less often than I intend to. So we built an agent that reads each Zoom transcript and privately tells the organizer how much everyone spoke, where the conversation meandered, and what might have made it better.
The feedback is about 85% useful. I sometimes disagree with it. But an outside view while the meeting is still fresh has helped me calibrate.
Do not start with a board meeting. Do not automate your one-on-one unless both people hate it. Do not renovate the kitchen while hosting Thanksgiving. Start with the junk drawer: a status report, a metrics review, some other low-drama weekly grind where stumbling is cheap and a win will be celebrated.
Let the most curious, AI-positive person lead. If five people attend a meeting, only one needs to reinvent the workflow, and it may be a junior IC or someone adjacent to the process. The manager supplies time, access, and safety. The IC brings the copy-paste, the edge cases, the workarounds, and the unreliable sources of truth that decide whether the thing actually works. (View Highlight)
Step 3: Goal on the outcome, not the process
A win is a result, not a good performance of transformation.
You want to say, “We reclaimed eight hours a week,” or “We caught twice as many issues before release,” or “Customers get an answer in ten minutes instead of two days.”
The win is not “we used AI,” “we adopted a new workflow,” or, lord help us, “we maximized tokens.” Those are inputs dressing up for Halloween as outcomes. People care that the work got faster, cheaper, or better. The AI is incidental.
Saving time is a wonderful early outcome. Easy to feel, easy to measure, and it gives people back the one thing they never have enough of. (View Highlight)
Step 4: Make the win loud
When a win happens, say so in public. Share the useful facts: here is the process nobody liked, here is what we tried, here is what failed, here is the result.
At Sundial, we made every question people asked Dwight public, so anyone could see what teammates were trying and steal the good ideas.
Each example gives the next person permission, inspiration, and a shortcut. Bank two or three wins and the mood shifts from “Is this even a thing?” to “What could we do next?” What to do If you manage a team
• Pick one low-stakes weekly ritual: a status update, a metrics review.
• Give one curious person time, access, and permission to rebuild it. Goal them on hours, dollars, or quality.
• Publish the result, failures included. If you are an individual contributor
• Volunteer to rebuild one repetitive team task.
• Write down the old baseline first, so you can prove what changed.
• Advertise the outcome. Publish the recipe so the next person can steal it. (View Highlight)
Part 3: Turn wins into systems
A few wins prove a better way of working is possible. What you do not want at the end of the rainbow is a pile of one-offs: a bot that runs only on one person’s laptop, a team prompt approaching the length of a Harry Potter novel, three agents synthesizing three different sources of truth.
A company that uses AI has a few good workflows. An AI-native company can find, build, test, and scale a new one on purpose, again and again. OpenAI’s enterprise research reaches a similar conclusion: “access alone may not be enough to scale AI.” The companies furthest ahead connect AI to their own context and tools, then turn one person’s working workflow into everybody’s. (View Highlight)
Step 5: Map the work, not the job titles
Because AI sounds like a human, people assume it can take a human job. But asking AI to “do sales” is nonsense. What is sales, exactly? Preparing for calls, building trust, negotiating a contract, updating the CRM, talking the product team into fixing a customer’s complaint. A job is too broad to build or evaluate.
Workflows are the useful unit. “One day before a customer meeting, assemble our past interactions and relevant company news into a brief” is something you can understand, test, and improve.
Most workflows contain some combination of four kinds of work:
• Research: Gather context, watch for changes, investigate a question. Competitive analyses, meeting briefs, metric investigations.
• Create: Turn an idea into an artifact. Software, prototypes, designs, test suites, campaigns, lesson plans, policies.
• Align: Move decisions and context through people and systems. Refreshing the CRM, routing tickets, collecting approvals, replenishing inventory.
• Improve: Judge the work so the next attempt is better. Quality checks, coaching, meeting feedback, reflection, critique. (View Highlight)
Every department has all four. To map one workflow, answer eight questions:
What triggers the work, and how often does it happen?
What information must be gathered, and can the system get at it?
Which steps are the same every time?
Where does the work require judgment, taste, or context?
What gets produced, and who relies on it?
How can someone tell whether the result is good?
If it goes wrong, how visible is the error and how reversible is the damage?
What outcome matters, and who owns it?
The best early candidates are frequent or time-consuming, guided by a repeatable playbook, easy to verify. Start there. Save the rare, judgment-heavy, high-stakes ones for later.
Managers and ICs have to find these together. I sometimes see a manager arrive with a checklist of “things to automate” and hand it out like homework. But the work a manager can see, the meetings and reports and deliverables, is a fraction of what keeps the place running.
ICs see the machine underneath. They know where people copy and paste between systems, which edge cases break the happy path, which workaround everyone depends on, and which source of truth is not truthful at all. Managers can compare opportunities across the team, move resources, set the risk tolerance. Neither view is enough alone. (View Highlight)
Step 6: Earn trust with evals
You earn trust in an agent through evaluations, usually shortened to evals. An eval is a repeatable test of whether the AI did the job right.
Think of it as the agent’s driving test. Before you hand over the keys, define what good driving looks like and test it on roads you already know.
Some evals have right-or-wrong answers. A data agent might face a set of “golden queries,” questions whose correct data and calculations have already been verified. If it cannot reliably answer those, it is not ready to answer them for the chief financial officer.
Sundial’s writeup on what it actually takes to trust AI with your data puts numbers on this. Fifty real user questions, given to a state-of-the-art model sitting on clean warehouse tables with a semantic layer, scored slightly better than 80%. The same questions, once the missing institutional context was supplied, jumped to 98%. What changed was not the model.
Other work requires a rubric. A recruiter might score candidates on required experience, evidence of impact, and role-specific skills. Past candidate packets with known scores become the test set. You are not testing whether the agent writes like a recruiter. You are testing whether it finds the right evidence and applies the same standard every time.
Build the eval alongside the workflow:
Collect real past examples: routine cases, edge cases, known failures.
Define the pass criteria before you look at the agent’s answer.
Run every change against the same set: new model, new instructions, new tools, new context.
Pilot with human review and log the important misses.
Add each new mistake to the eval set so it cannot come back unnoticed.
Give the test set an owner who keeps it current and watches for regression, meaning a new version got worse at something the old one handled.
The eval is also how an agent earns a wider circle of agency. I think of it in three levels:
• Level 1: Advisor. You consult the AI, but you do the work. It suggests the points for a report you then write.
• Level 2: Deputy. The AI does the work under your authority, but a person reviews every action before it commits. It drafts the report, or preps a CRM update for approval.
• Level 3: Regent. The AI acts within a defined domain and reports what it did. Its authority is borrowed, bounded, monitored, and reversible. (View Highlight)
Promotion requires evidence. An Advisor becomes a Deputy once it passes an offline eval set and people know its common failure modes. A Deputy becomes a Regent after a supervised live pilot, with monitoring and a fallback when something goes wrong.
The evidence required rises with the risk. An internal meeting brief is easy to inspect and cheap to correct. Sending a customer message, approving a discount, moving money, evaluating an employee: those reach farther and do more damage.
Trust stays local. An agent that has earned Regent status for sales briefs has earned it for sales briefs, and nothing else. (View Highlight)
Step 7: Refactor what repeats
During the experiments in Parts 1 and 2, inconsistency is fine. Different people, different models. Two teams can solve the same problem two ways. Proof matters more than tidiness.
Eventually successful experiments start depending on the same foundations. Sales, finance, and support may all need the number of new customers each week. If each agent defines “new customer” differently, headaches ensue. Three workflows may all need calendar access, CRM history, and the current company strategy. If every team builds its own plumbing, the systems drift.
Think of it like refactoring a growing codebase. The first workflow is an experiment. The second reveals a pattern. By the third, the repeated piece deserves shared infrastructure.
At a bigger company, a small central AI team should hunt for those repetitions. The job is plumbing:
• Provide approved models, consistent data connections, logging, and eval tools.
• Notice when teams have rebuilt the same component, and replace the copies with one reliable version.
• Own the foundation. Let local teams own what they build on it.
Local teams pick the problem, design the workflow, own the outcome. A central group automating other people’s work from a distance will miss the details and get resented for it. The center’s job is to make the local team’s good idea faster to build and harder to break.
You do not need a perfect architecture up front. Let teams experiment, evaluate what works, refactor what repeats. Run that loop reliably and you are ready for Part 4. (View Highlight)
Part 4: Recompose for abundance
By now you have believers. You have wins that scaled. You have workflows growing in number and in ambition.
Now the hard part. Everything so far has been additive. From here you question the old shape of the work and decide what the organization should become.
Step 8: Tell an abundant story
Change is scary, and no amount of magical technology changes that. When people cannot see the future, they imagine the worst. The default AI story inside most companies is subtraction: “We will do exactly what we do now, with fewer people.”
That is the absolute lamest future imaginable.
You need a bigger story: given what we value and what we are good at, what is suddenly within reach that was unthinkable before?
One of my favorite recent examples is Midjourney, an early leader in AI image generation. When it described its next chapter, it did not promise a better, cheaper image model. It described a push into medical imaging: affordable body-scanning machines, custom wellness spaces. Whether or not that bet lands, I love the shape of the ambition. The falling cost of one capability became permission to chase a much bigger problem.
When a resource gets dramatically cheaper, people find far more uses for it. Economists call this the rebound effect, or, in its strongest form, Jevons paradox. An abundant story asks what your company will make, serve, or discover once intelligence and execution get cheap.
This matters emotionally, too. When a team grows, people feel a loss as they hand pieces of their job to new colleagues. Molly Graham calls this “giving away your Legos.” The loss feels worthwhile when you believe those people will build something great with them, and a bigger castle is waiting for you.
Handing work to AI is the same. It becomes growth only when there are larger problems worth turning your eye and hand toward.
I have heard some of the best people in their craft describe the moment the game changed underneath them. If you were one of the rare few who could do something almost nobody else could, watching AI hand that skill to everyone is jarring. There is grief in it, and a real “What now?”
Then many of those same people say, in the next breath, “What is amazing is that I can go horizontal now.” The best designer in the room can make a damn good marketing video or build the app they always imagined. Deep skill gains range. Taste and judgment travel from one domain to another, even as AI closes the gap on taste itself and agency becomes the part that stays ours.
Telling that story is the leader’s job. The story is the strategy, not a communications wrapper around it. Everyone can add to the plot, but leaders make the opportunity concrete enough that people see where they fit. “You will do more strategic work” is not a story. Name the customers you can finally serve, the product you can finally build, the stubborn problem the team can finally take on. (View Highlight)
Part 4: Recompose for abundance
By now you have believers. You have wins that scaled. You have workflows growing in number and in ambition.
Now the hard part. Everything so far has been additive. From here you question the old shape of the work and decide what the organization should become.
Step 8: Tell an abundant story
Change is scary, and no amount of magical technology changes that. When people cannot see the future, they imagine the worst. The default AI story inside most companies is subtraction: “We will do exactly what we do now, with fewer people.”
That is the absolute lamest future imaginable.
You need a bigger story: given what we value and what we are good at, what is suddenly within reach that was unthinkable before?
One of my favorite recent examples is Midjourney, an early leader in AI image generation. When it described its next chapter, it did not promise a better, cheaper image model. It described a push into medical imaging: affordable body-scanning machines, custom wellness spaces. Whether or not that bet lands, I love the shape of the ambition. The falling cost of one capability became permission to chase a much bigger problem.
When a resource gets dramatically cheaper, people find far more uses for it. Economists call this the rebound effect, or, in its strongest form, Jevons paradox. An abundant story asks what your company will make, serve, or discover once intelligence and execution get cheap.
This matters emotionally, too. When a team grows, people feel a loss as they hand pieces of their job to new colleagues. Molly Graham calls this “giving away your Legos.” The loss feels worthwhile when you believe those people will build something great with them, and a bigger castle is waiting for you.
Handing work to AI is the same. It becomes growth only when there are larger problems worth turning your eye and hand toward.
I have heard some of the best people in their craft describe the moment the game changed underneath them. If you were one of the rare few who could do something almost nobody else could, watching AI hand that skill to everyone is jarring. There is grief in it, and a real “What now?”
Then many of those same people say, in the next breath, “What is amazing is that I can go horizontal now.” The best designer in the room can make a damn good marketing video or build the app they always imagined. Deep skill gains range. Taste and judgment travel from one domain to another, even as AI closes the gap on taste itself and agency becomes the part that stays ours.
Telling that story is the leader’s job. The story is the strategy, not a communications wrapper around it. Everyone can add to the plot, but leaders make the opportunity concrete enough that people see where they fit. “You will do more strategic work” is not a story. Name the customers you can finally serve, the product you can finally build, the stubborn problem the team can finally take on. (View Highlight)
Step 9: Find the new bottleneck and recompose
As AI handles more work, a funny pattern emerges. You celebrate the win, pat yourself on the back, and a few weeks later everyone is grumbling about a new bottleneck. How quickly we humans adapt.
That is a good sign. Relieve a constraint and it moves. Your job is to keep finding where the work piles up next, and redesign around that.
I recently asked a senior leader at one of the biggest companies in the world where the bottleneck had moved after an aggressive push into AI workflows. He suspected human decision-making. The company had a handful of senior decision-makers, and too much work was flowing through them like cars into an understaffed tollbooth.
His next questions were different from the ones he began with. How do we help these people make better decisions faster? Which decisions no longer need to reach them at all?
Sometimes an agent can gather the context, compare options, and recommend. Sometimes you codify the principles so people closer to the work can decide. Sometimes the approval does not need to exist at all.
The person is rarely the whole bottleneck. More often it is the structure around that person. A leader at a Fortune 500 company described the problem this way: “We are very consensus-driven. You need people across different organizations to say yes, which means you need to know whom to talk to and what they will care about.” I have seen routine decisions take five weeks because they cross four approval layers and, during the summer, one approver is always on vacation. (View Highlight)
Start by asking the people closest to the work where it waits. When their answers cluster, audit the workflow with real data:
Track the full life of the work, from request to result.
Separate doing time from waiting time.
Mark every handoff, approval, and repeated explanation.
Name the constraint: missing context, scarce judgment, unclear ownership, too many approvers, or not enough capacity.
Then redesign around it:
• If people wait for context, have an agent assemble and maintain it.
• If judgment is scarce, codify the common cases and save human attention for exceptions.
• If ownership is unclear, name who decides and what principles govern it.
• If there are too many approvers, remove the gates and move authority closer to the work.
• If capacity is still the constraint, decide whether AI expands it or whether roles and staffing have to change. (View Highlight)
A founder told me that every new feature at her company had to pass through a central design team, which decided where in the app it belonged. She thought about hiring more designers. Instead the company carved out a self-serve experimental area where product teams could launch on their own, with the expectation that losers get retired.
Now only the clear winners graduate into the core product. Teams move independently, the designers stopped being treated as blockers, and the company runs far more experiments.
That is recomposition. She did not add capacity to the old machine. She built a different machine. (View Highlight)
Step 10: Change what the organization rewards
Once you have evidence of how the work and the teams should change, change the rules in public.
People have heard “we are doing AI” a hundred times. Talk is cheap. The truest expression of what an organization values is whom it hires, rewards, and promotes. Change the hiring bar. Look at the people behind your strongest experiments. What made them effective? Maybe they thrived under uncertainty, took initiative, spotted patterns across systems, went horizontal without losing the depth of their craft. Turn those observed behaviors into the new bar. Do not invent a list of fashionable AI traits off a conference slide.
It also forces your interviewers to define what good AI-native work looks like. Nothing sharpens your thinking like having to spot the behavior in a stranger. Change the feedback and promotion bar. This one carries further, because everyone already inside is watching. Describe how an excellent team member now approaches a problem. How do they use AI without outsourcing judgment? What do they do when handed a tool instead of headcount? How do they make their colleagues better? What outcomes can they now own?
Performance and promotion still follow outcomes. “Used AI” is not an achievement. Make the new expectations clear enough that people can choose how to grow into them. (View Highlight)
The ten steps tell you what to do. Underneath them are a handful of principles that decide whether the whole thing feels human, or feels like something done to people.
This guide originally ended with them. I cut them, because arriving after ten steps they read as a fifth act nobody asked for. They are also the ones I would tattoo on the wall, so they live down here instead: honor human agency, be honest, apply flexible rigor, and believe in the endless ingenuity of the human spirit.
Managing the human side of change is the hardest part of any transformation, and hardest of all when the change upends old expectations fast. Four principles keep me grounded. (View Highlight)
Honor human agency
With so much talk of “AI agents” and “agentic systems,” it is easy to forget that agency begins with the humans involved.
Transformation often feels like being pummeled by forces we do not control: top-down pressure, “do this or else” mandates, layoffs. Layoffs feel especially awful because they remove choice. This does not mean layoffs are never necessary. It means leaders should preserve people’s ability to make informed choices wherever they can.
Ask for volunteers before assigning experiments. Invite bottom-up solutions before issuing automation checklists. Give people clear success criteria and room to decide how to reach them. When roles must change, make the options and the consequences explicit.
We cannot stuff large language models back into Pandora’s box. We can decide how we respond to them, and how much agency we preserve for one another. (View Highlight)
Be honest
People cannot exercise agency without accurate context. We spend enormous energy giving AI systems the right information. Humans deserve the same.
What is truly at stake? Why does the company need to change? What does success look like? What is no longer valuable? What remains unknown? What might this mean for me?
Leaders often believe they must project certainty when the future is impossible to predict. Teams can tell the difference between clarity and posturing. Trust grows when a leader separates what they know from what they believe, and both from what they are still trying to learn. (View Highlight)
Apply flexible rigor
The biggest gatekeepers to change are usually the familiar ones: the comfortable workflow, the risk-avoiding inner voice, the vigilant colleague who sees every reason the new thing might fail, the leader who protects the company by loading every experiment with overhead.
Evolution requires flexibility. People need room to make mistakes and to try things that do not work. The skeptics will be right about plenty of experiments. Many AI endeavors will fail.
That freedom cannot come at the expense of the outcome. AI does not excuse degraded customer service, factual sloppiness, or a five-page brief nobody wants to read. “AI wrote it” is not a hall pass. If anything, cheaper production should let us demand better results.
My favorite metaphor is a katana, a Japanese sword with a hard, sharp edge and a softer, shock-absorbing spine. If the whole blade were hard, it would shatter. If it were all soft, it would not cut. Flexibility and rigor make each other possible. (View Highlight)
Believe in the endless ingenuity of the human spirit
I am an optimist at heart. I love AI and its promise. But I hold no confusion about what ultimately matters: the health and flourishing of humans everywhere.
We are not merely the labor of our muscles, and we are not merely the output of our cognitive minds. We create meaning, form relationships, develop taste, dream up futures, and choose what is worth doing. AI can expand our range. It cannot settle those questions for us.
There is so much learning and discovery ahead. Let us use these tools to make ourselves a crazy excellent future. (View Highlight)
