Why AI hasn't made you faster yet — and the eight work habits to break
Nate B Jones of AI News & Strategy Daily argues that AI has shifted the bottleneck in knowledge work from execution to clarity, ambition, distribution, and relationships — and that most people's work habits are still optimizing for the old constraint.
Summary
Nate B Jones opens by contrasting two scenes: Anthropic shipping a full product feature (Claude's co-work) in ten days with four people, versus a typical corporate conference room where leaders are still requesting 30-day implementation roadmaps with phases, milestones, and resource allocation plans. His central argument is that execution capacity is no longer scarce — AI has removed that constraint — but our work habits were built entirely around protecting it. The bottleneck hasn't disappeared; it has moved to clarity, ambition, distribution, and relationships. Jones then identifies eight specific habits — from permission loops and polish-as-procrastination to consensus-seeking and hoarding work until it's ready — that made sense when execution was expensive but are now actively slowing people down. He closes by arguing that the chaos people feel in the AI era is not random; it is the gap between where the bottleneck has moved and the habits they still carry.
Key Takeaways
FULL TRANSCRIPT
The chaos of AI and why it feels so hard to navigate
Nate B Jones: The one constant right now is chaos. I hear it over and over again from people. The rate of change, the sheer unpredictable chaos of AI — it's very difficult to tell what's up and what's down. So in this video, I want to simplify it. I want to zero in on some of the underlying drivers that are shifting what truly AI-native working looks like, why most of our work habits are now optimizing for the incorrect thing, and I want to give you some specific habits — eight of them — that you can break to help you start working in a more AI-native way.
Two scenes that define the gap
But first, I want to start with two scenes from the last month.
Scene one: Anthropic shipping co-work, a full product feature. It was built in ten days with just four people. It was written entirely in Claude Code. And Claude Code, mind you, is an entire product that is less than a year old. So these folks have not had years of working AI-natively to do this. The Anthropic team is evolving as they go.
Meanwhile, scene two: at your company, and at many companies I've worked at in the past, there's a conference room where I guarantee you a leader is asking for a 30-day implementation roadmap or a three-month implementation roadmap for their AI strategy — with phases and milestones and resource allocation and a plan to protect capacity.
Execution capacity isn't scarce anymore. Ten days, four people, and they're shipping 60 to 100 releases daily. Execution capacity is not the problem. When we build our AI strategies, we're frequently asking for help or guidance on the thing that is no longer scarce and no longer requires efficiency.
How our careers trained us to protect the wrong thing
We have spent so much of our business lives assuming that execution capacity is scarce. Anthropic and many other organizations are now showing that it's not. And yet every organization, all of our individual work habits, anyone who's had a career of more than three or four years in the industry — it's all built around the implicit answer to one question: what's expensive here?
And the answer has been execution.
For most of our careers, building things required scarce hours from scarce people with scarce skills. Finding really good engineers was really hard. Training them took a long time — maybe years. Every hour of their time was precious. And so we evolved elaborate rituals to protect that capacity: planning processes, approval gates, specs, PRDs, meetings to align before anybody built. All of it was designed to protect the precious engineering execution time so it wouldn't be wasted on the wrong problems.
And that made sense. When the meeting to discuss a feature takes much less time than the time to build it, you definitely want to hold the meeting so you get it right. When gathering the requirements costs much less time than writing the code, you want to gather the requirements first. When rework is really expensive, you want to plan really meticulously so you avoid rework.
Now, I know — and you know — that there have been movements in software to make this easier. Agile comes to mind. But even then, engineering work remained expensive, and Agile was a response to the idea that you had to optimize engineering work over time to deliver value.
AI has inverted the entire cost ratio
AI has inverted the entire cost ratio and changed the way we think. We're no longer in a world where we argue about waterfall versus Agile. We're in a world where we're talking about a different kind of work entirely. Agile never imagined a world where everybody commits code in the organization.
Cursor, the AI code editor, went from a million dollars to $500 million in annual revenue faster than any SaaS company in history. And they're not done yet. They are launching Cursor for designers — and they're not launching it as a separate product. They're just relentlessly shipping features inside Cursor that make it easier and easier for other job families to use that tool. What used to be an impossible product expansion is now another day at Cursor.
What used to be a new feature like Claude co-work that took months to plan and ship is now ten days for four people. Coinbase engineers report that individual people are now refactoring, upgrading, or building new codebases in days. Same story.
So in that world, the meeting to discuss a feature now takes longer than building the feature. The PRD can take longer than the prototype. The planning process can take longer than shipping three versions and seeing which ones work.
The bottleneck has moved, but our work habits are still stuck in the way we've worked most of our careers.
The manufacturing principle that explains everything
There's a manufacturing principle that explains a lot of what's happening here. When you eliminate a bottleneck in a manufacturing system, the bottleneck doesn't actually disappear — it moves somewhere else downstream in the system. You see the new crunch point. Resistance is never destroyed; it's relocated. When you gain efficiency in one place, you see new constraints.
That's what's happening now. For decades, the constraint in knowledge work was execution. Now that AI has largely removed that constraint, the bottleneck has shifted. It hasn't disappeared — a lot of people will tell you it's disappeared, but it hasn't. It has shifted to clarity, to ambition, to distribution, and to relationships.
And we're still running around using work habits that are designed to protect execution capacity. We are optimizing for the old work constraint while the new one is compounding.
Where the bottleneck went: clarity, ambition, distribution, relationships
So let's explore where that bottleneck went.
Clarity. Do you actually know what's worth building? That is now a billion-dollar question. You can now build faster than you can think. Every day I see new startups come out of stealth claiming they can build a business with a prompt. In 2026 you are going to see people try that, and some of them will make money. The ones that make money are the ones that will have clarity — the ones that know what's worth building. Because it turns out the bottleneck was never putting the product on the website. It's knowing what product the customer wants.
PRDs were always a substitute for clarity. They were a big hedge against expensive rework — a way to disambiguate and get to some clarity when you were facing a potentially risky investment, a six-figure or seven-figure investment in engineering time to get to a prototype. But now writing a PRD can cost more than shipping the whole thing. And I'm not kidding. I have seen PRD cycles in my career at big companies take longer than Claude took to ship all of co-work.
Ambition. Are you swinging hard enough? When shipping requires a quarter of engineering time, small bets can make sense — that's Agile-style thinking. You might have three or four shots per year, and if you can increase that a little, great. But what if that's no longer a constraint? What if every ten days you can ship, every week you can ship — that's 50 swings a year. Suddenly your risk is timidity. Your risk is lack of courage. The danger isn't necessarily building the wrong thing, because you've got 50 shots to build the right thing. The danger is not building enough things toward a larger vision that is truly transformative for the customer.
We're going to see a lot of cases where people are using AI to build horseless carriages — which is the old name for cars. We called them that because we didn't have a mental model for a car when we first got them. We will see a lot of products that are horseless carriages. What we need is the ambition and the eyes to see what the 10x better product looks like in our particular domains, and to shoot for that with multiple releases as quickly as we can.
Distribution. When everybody can build, product is not really the moat that it was. Getting it into people's hands is the moat. My favorite example right now is Cognition, makers of the AI coding agent Devin. They chose not to pursue distribution themselves, even though they have a product that is an agentic coder very much on trend for 2026. Instead, they partnered with Infosys and are deploying Devin across Infosys's 300,000-person team and hitting all of their global client base. Why? Because Infosys has distribution. Infosys has decades of enterprise relationships. The technology is the easy part. Reaching customers is hard. And Cognition realized that an established brand was the way to do that.
Relationships. When capabilities are compounding quickly and platforms and channels keep shifting — what worked last quarter might not be the right way to do it next quarter — the thing that is durable is relationships. You can't vibe-code a relationship. And this is going to be a fractal truth, by which I mean it's true for individuals and it's true for companies. Companies in business relationships are going to have durable advantages by investing in those relationships. And you, individually in your career, will have a durable advantage by investing in your professional relationships — so that you are known as a trusted person to do work with. Because if technical skills are rapidly becoming a commodity, you're going to turn to someone you can trust to deliver, and that's a relationship thing.
Eight habits to break — and why they're costing you now
So we've taken a tour through how some of these constraints are shifting, and how AI-native ways of working are upending our assumptions about execution. How do our habits need to change?
Right now, most of the work habits we've embodied are risk-management rituals designed for a world where execution was expensive. And they've calcified — they've clotted into defaults that persist even though all of the unit economics in our world have flipped.
You've probably felt this. You've probably felt a sense that you're spending more time prepping for the work than you probably should. That there should be an easier way to do this. That maybe you're protecting something that doesn't need as much protection anymore. Maybe you're preparing for a meeting that doesn't need the kind of ritual it used to need.
Let me get specific. Let me suggest habits that made sense in the old model and are probably now actively costing you — so that you can flip them and they can become the seeds of a more AI-native way of working.
Habit one: the permission loop
The old logic is that doing something was typically expensive in time, so check before you do. Get buy-in. Make sure you're building the right thing before you spend precious resources. We had management books for a long time that said we need more autonomy, we need to push delegation down — but those still didn't change the fundamental understanding that execution was costly. You could take a bias for action and write up a proposal, but if you're going to spend a quarter of engineering time, someone's going to need to sign off on that.
That logic is now broken. Asking — even for relatively large things — still takes longer than doing. The email thread to get approval can take more time than building the prototype. The Slack conversation to confirm direction can take longer than trying both directions and just seeing what works.
We are in a world where Manus just launched a feature that literally builds the presentation you're talking about in the meeting as you're having the meeting. Approval processes were designed to reduce risk, but they don't anymore. They just add slowness.
This means our organizations need to shift. People need to be free to default to doing — to building the rough version, to showing it, to asking forgiveness when needed, and to committing to a broader vision that they can reliably ship against autonomously. We need leaders who can cast that wider vision so teams can be more independent and ship relentlessly against it. That's how you break the permission loop.
Habit two: polish as procrastination
The old logic was you get one shot, so make it count. If execution is expensive, don't waste it on something half-baked. I saw this show up when I was writing product requirement docs and PR FAQs at Amazon. You had one shot with a particular person you wanted to review with. You had to make it count. You had to polish it. You had to go through different reviews. Quality really mattered.
That's now broken. People are spending 80% of their time on the last 20% of quality when the marginal value of that polish is dropping quickly. And I want to be careful here — I am not saying that good thinking is going out of style. I am saying that polish is becoming a way to avoid getting your ideas rapidly in touch with reality.
The rough version that exists is going to beat the polished version that doesn't. And you can always improve on it. I'm not saying consumers won't value polish — I think there's a huge amount of money to be made in polished UI for AI products. We have a lot of rough edges on a lot of our AI products, and we're leaving billions of dollars in market value on the table by not fully polishing them. Take that as a goal to iterate toward. But if you're trying to get there quickly, you just have to ship and then relentlessly optimize from there.
Notebook LM is a great example. They shipped, they got into market, they saw the reaction, and they've been polishing that UI ever since. That path to polish looks really good when you're shipping fast.
Habit three: meetings as a default
Meetings are a function of people, and one of the consequences of AI-native teams is they tend to be smaller, so you get fewer meetings. But regardless of team size, the old logic was that you get alignment before you get action. You get everybody in the room so you don't waste expensive execution time building the wrong thing. An hour of six people's time is six hours of work — and that's often enough to just build the thing.
Meetings still feel responsible, don't they? Meetings still distribute accountability. If the meeting decided something and it was wrong, it's not our fault — the meeting decided. And the worst part is this: meetings about what to build often don't resolve what to build. They don't even answer the question. They surface opinions, create action items, and create delays.
AI is relentless about this. AI will show the truth: just build it. You have the time. Build it and show it. It might be bad, it might be good, but just build it. You can replace the meeting with a product demo. In fact, maybe the next time you schedule a meeting, just ask yourself: what if I built the rough version of this and showed people instead?
That's what a lot of these AI-native companies are doing. It's one of the foundational build principles animating the culture at Cursor right now. Cursor thinks a lot about how code is a way of getting your ideas into contact with reality. One of the things they've talked about in interviews over and over again is taking some of the craft, some of the extra, some of the planning that protected execution out of work — so that designers can commit, product can commit, engineering can commit, and you can see your ideas meet the code. That saves meetings. Meetings are no longer a default.
Habit four: structured waiting
The old logic was that coordination was important. Waiting for feedback was important because you wanted to protect execution time and keep it aligned. Everyone's time is precious, so you must respect the process. Wait for feedback. Wait for the sync. Wait for someone to unblock you.
So much of work in older, larger corporations is waiting. Waiting for approval. Waiting for feedback. Waiting for the next meeting. Waiting for someone else to do their part. But most of what you're waiting for doesn't need to be waited for, and you end up outsourcing your momentum to other people's calendars.
Stop waiting. Do the next thing while you wait for feedback on the first thing. Assume the answer is yes. This is another area where leaders need to set the tone — they need to cast a vision large enough that their teams always have something to work against, don't have to get stuck waiting, and know there's space to get forgiveness later when they're working in the correct direction.
The cost here is much larger than it used to be. Waiting an hour in the 2010s was waiting an hour. Waiting an hour now is waiting a prototype. If you're blocked on a decision, make a provisional decision now. Let people know what you picked and keep moving. But get rid of structured waiting.
Habit five: inverting planning and doing
The old logic was very much measure twice, cut once. Planning is cheap. Execution is expensive. I had that drilled into me when I was coming up in product. I've watched people spend a week writing a plan. I've watched them spend two weeks. I've watched people spend eight weeks writing a plan at times. The plan ends up being a hedge, and it ends up being much more expensive than the product will ever be. And ironically, the plan is almost always incorrect. It almost never survives contact with reality.
Look at the planning you're doing and see if you can cut it down. Set a goal: can you cut your planning down by 90%? You probably can. See if you can replace that with learning through prototyping. See if you can replace it with a bold rough direction and aggressively shipping and optimizing for what works.
If you haven't built something in the last couple of weeks at work — or at home if you're doing a home project — then you're probably overplanning. Let reality inform the plan instead of trying to predict reality with the plan. Because prediction has now become expensive and luxurious, while execution and doing is cheap, more accurate, and more reliable. Learning from customers directly by launching is more reliable. Think about how much you're planning and see if you can cut it by 90%. Spend that energy on doing instead.
Habit six: the deck instead of the demo
That's an old habit. We build consensus, we create a deck, we make sure stakeholders can weigh in. We call them walking-around decks. The deck shows what we want to envision. It helps build alignment with various stakeholders. Sometimes we workshop that deck. I remember having to pick the fonts.
Forget all of that. Build the demo. Show it. Build a working prototype and show that instead of the deck. In some senses, work is getting much simpler now. All of the rituals we prepared as a hedge against execution are in question. We should be asking if we need them. Why not just do the work? Why not just ship to customers?
Habit seven: consensus before action
Get everybody aligned. This is so deadly. I know organizations tried to push on this by giving individual team leaders autonomy — Amazon had their two-pizza teams, there were other versions of that. The difference now is that the cost of consensus has 10x'd or 100x'd. If consensus was expensive before in the 2010s, it is priceless now. And ironically, consensus often wasn't real anyway — people would agree in meetings and then undermine the decisions later.
Regardless, it is too expensive now to practice the habit of consensus before action. Let consensus come from the results that create alignment. Just try things. And this is going to be a leadership change as well as an individual contributor change. Nobody is exempt. Leaders can't just say "do this" and leave it up to people. But if you're starting out on your own and trying to set a new way of working, you're going to need some support from your manager and you're going to need to be explicit about that.
Lead by doing. Lead by showing that if you set an ambitious goal, a larger vision for the business, you don't need to get consensus. You can just act in that direction and let results create alignment over time. "I tried X and here's what happened" is much more persuasive than "let's agree to try X." Run the experiment first and then everyone will align when you get the data, because people can see what actually works. And it's never been faster to try that out.
Habit eight: hoarding until ready
People who like to not show work until it's complete. Who think half-finished work wastes other people's time. I was told that when I was a junior coming up. I was told: "Don't submit this half-finished piece of work. This isn't good enough to show your manager yet. You need to finish it all the way."
That is reversed. Now you're probably sitting on ideas and drafts and prototypes until they feel ready, which means you're getting feedback late — after you've invested in a direction that might be incorrect. The cost of getting to a rapid version is so cheap now. We just have to be willing to show it. And that takes a little bit of ego death. We have to be willing to show work that's a little raw and unfinished. What if people think it's bad? What if people think it doesn't work? Those are exactly the reactions you need. What if you ship that prototype to the customer and they don't like it? Well, this is why we ship a lot — and now we have that information. Finding out you're wrong a week from now is better than finding out a month from now.
This does put a primacy on thinking clearly. Half-finished work that is pure AI slop with no thought behind it is going to show — it creates downstream work for your colleagues. But half-finished work where you put some thought in, you have a direction you want to go in, and you just want some feedback and want to show a quick prototype — that's different. Now you're putting thought into what's good and what's not.
The through line and what it means in practice
The through line here is pretty simple. All eight of these habits are risk-management rituals that made sense when doing was expensive. Those unit economics have all flipped. The risk now isn't wasting anybody's execution time — engineering or otherwise. It's wasting time on anything that isn't doing, anything that isn't building. The permission loop is costing you more than the thing you're asking permission for. The polish costs more than shipping.
Let me give you a couple of examples of how this looks in practice.
The old way: you have an idea for improving a process internally. You write up a proposal. You schedule a meeting. The meeting surfaces questions. You update the proposal. Maybe weeks later, you get approval to try a pilot.
The new way: you get an idea. You spend an afternoon building a rough version. You show three people. Two of them have concerns that kill the idea. Good — you found out in a day rather than a month. Or they like it and you iterate, pilot, and launch from there. The whole thing took a fraction of the time.
Or the old way: you're working on a presentation for leadership. You spend a week on the deck. You refine the transitions, wordsmith the messages, anticipate the objections. Leadership asks questions anyway.
The new way: you spend maybe two hours — maybe 20 minutes — on a rough deck. You just want to get the alignment. You send it to your boss: "This is rough. Does the direction make sense?" She flags two problems you didn't consider. You fix them in ten minutes. The final deck took maybe an hour and a half and it's actually better because you got the feedback early.
Stop treating process as a prerequisite and start treating iteration — getting your ideas into contact with reality — as the process. The rough version is going to be the gold standard because we can get to it so much more quickly.
Addressing the counterargument: what about compliance and quality?
Now there's a counterargument I want to call out. In some places, you're going to hear: quality matters in my domain. My boss expects a plan. I'm going to get in trouble if I just do things without asking. That's fair. The habits I'm describing have different impacts in fields with high legal and compliance risks — say, medicine, with very high compliance requirements.
But here's what I would ask: how much of the process you're following is actually required? And how much is it just the way things have always been done in that environment? Most of us, when we're honest, realize we probably have more latitude than we're using. The habits that feel mandatory are often just the defaults that nobody has questioned before. And if we have our eye on quality in those fields — in medicine, in law, in finance — then we are going to be in line with compliance even if we get there more quickly.
If you want a place to start, pick the one of the eight habits that feels lowest stakes to you and just break that habit. Give it a try. Try a simpler way to do it. Maybe it's shipping something without the usual polish. Maybe it's skipping a meeting and just building the thing instead of brainstorming. Maybe it's stopping waiting — you're blocked on a decision, so instead of waiting, you make the decision, say "this is what I'm deciding," and do the work.
My goal is not to get you to break rules. My goal is to get you to discover that the principle of AI-native work is recognizing where value is really coming from in your work. It's not coming from protecting execution anymore. It's coming from doing the execution quickly, within a framework where you have the ambition and boldness to build something really meaningful, to solve meaningful customer problems, and to deliver value.
Closing: closing the gap between where the bottleneck moved and where your habits still are
The people who figure this out first are going to be operating at a velocity that feels a lot more like Anthropic, a lot more like Cursor, and a lot less like a traditional big company. And it won't be because they have better tools — almost everybody is getting the fancy tools now. It's because they will have stopped doing the things that are no longer worth doing in a world where execution is cheap. They'll be shipping while other people plan. They'll be iterating while other people align. They'll be learning while other people polish.
If we circle back to the beginning of this video, I talked about living in a world of chaos and needing a simplifier. The chaos you're feeling is not random. It's the gap between where the bottleneck has moved and the habits you still have today. When you close that gap — when you start to align your work habits to how AI actually changes scarcity in the business, how AI enables execution, and you recognize where there's other scarcity that needs your business judgment — suddenly you're going to know where to spend your time. You're going to know why you need to move faster, and the chaos is going to start making sense.
You're going to understand why meetings can't be the default anymore. You're going to understand that the next time someone ships a vibe-coded this or that, it's not just one more piece of AI news — it's someone who recognized they needed to get their idea into contact with reality quickly. And you're going to recognize that the truly precious resources — the ones that are good for your career and that help businesses thrive — are still true in the age of AI, and are even more true now when execution is cheap. Things like clarity, like ambition, like distribution. The bottlenecks that are appearing and growing more scarce as AI-driven execution becomes more pervasive.
In a world where we're going to get another major AI release tomorrow — I guarantee it — worry less about what execution is enabling a company to do, and worry more about your ability to shift your work habits, your ability to practice getting your ideas into contact with reality, solving real customer problems, and shifting your attention to the things that are actually difficult to do — the things that require good human judgment.
It's hard to get clear on things. It's hard to be ambitious. We tend to think smaller than we should. Distribution is a hard thing to tackle. Most entrepreneurs already overindex on product and underindex on go-to-market — that's not new. But distribution is exponentially more valuable now.
So that's the challenge. In a world that is chaotic, recognize that so much of the chaos comes from being out of sync with where AI is pushing scarcity in your business. It feels out of sync because AI is making execution so cheap and everything is changing because of that. Let's get that figured out, and things are going to start to get smoother.