The Real Lesson from a Flying Tesla: Derivative Doesn’t Mean Unoriginal

Tesla shows off a Roadster that can lift itself into the air, and somewhere an aspiring entrepreneur thinks “I need to invent something even more spectacular to be successful”. I admire the ambition and showmanship. I worry about the lesson people take from it.

The ingredients have histories of their own. The Chaparral 2J used fans to generate suction and grip decades before this Roadster demo. SpaceX developed reusable rockets, tackling the engineering challenges of bringing boosters back to land. These are different applications, but they put the spectacle in context: suction, thrust, and controlled landings each have substantial work behind them.

The same pattern extends to autonomous driving. In 2004, DARPA’s Grand Challenge pushed teams to build vehicles that could navigate a desert course themselves. None finished. Government-backed experimentation helped advance technology that would later find commercial applications. Turning those advances into reliable products required substantial additional engineering and development.

Watch a Tesla demo and you see one company’s vision. Look behind it and you find a much longer history of inventions, experiments, and accumulated knowledge.

Spectacular demos compress that history into a single moment. It looks like someone invented the future from scratch. The aspiring founder walks away thinking that’s the assignment.

What gets lost is the distinction between inventing a technology and combining existing technologies into something extraordinary. Both create value. But they require different amounts of time, money, and patience.

Building on existing work doesn’t diminish what a founder accomplishes. The opportunity is knowing which pieces are ready, which solve problems people care about, and how bringing them together can create something more useful.

When your money, time, and attention are limited, every additional breakthrough your business depends on is another uncertainty you have to fund and resolve. You can spread yourself across so many invention problems that you never get to solve the customer’s problem.

I talk a lot about focus. This is one of its most consequential applications. My advice: choose where you actually need to invent, and limit it to one thing at most.

Which part of your product requires something new? Which parts can use something that already works? Where will the difference matter enough for someone to pay?

Bread and knives existed long before Otto Rohwedder’s machine made commercially sliced loaves possible in 1928. That machine took real invention and persistence. He focused that effort on a specific problem without having to reinvent everything around it.

You don’t have to invent bread and the knife to create the next sliced bread.

Indecision Does Not Preserve Options

Product bloat is often blamed on too many ideas or not enough resources to get speed. More often, it comes from too few decisions catching up with you.

A team delays deciding if a feature is required, who it is for, is the value it brings worth it to the overall product and company stage. The team hopes that time is saved by moving on to the next feature or issue will speed them up or “fix it”. The unanswered question feels like a temporary issue that doesn’t deserve time because there is not enough time for it. The work can continue. But nobody has removed the question. They have simply created compounding overhead.

So, “for now”, the feature will; to serve anyone.

It gains settings for one interpretation, exceptions for another, and workarounds for the cases nobody wanted to rule out. What began as an effort to stay flexible becomes a product that asks its users to carry the uncertainty the team did not resolve. The cost is not limited to one vague feature.

Imagine five possible directions: A, B, C, D, and E. None has been decided. It can feel as if the team has preserved five options. In reality, it has created five active futures.

Each future produces its own downstream work. Each has dependencies, edge cases, and failures. If something breaks deep inside E, the question is not only whether to fix it. The team also has to decide whether E is still important, whether it will matter later, and whether that work should take priority over something developing in A or D.

The original decision has been deferred, but the decisions it created have multiplied. This is why indecision becomes so expensive. It divides attention before it divides the product.

A person can be actively working on a branch that should never have existed. A customer can arrive with a real need for D, but the team may not notice it clearly enough because everyone is already carrying the mental load of A, B, C, D, and E. Attention is scattered across possibilities. Problems demand responses. Opportunities arrive without enough clarity to be recognized.

Eventually, the cost becomes visible in the product itself.

Flowchart illustrating the consequences of indecision with five options (A, B, C, D, E) leading to divided attention, increased work, and ultimately product bloat.

The interface gets crowded. Navigation expands. Settings multiply. A straightforward path becomes a collection of tabs, filters, alerts, roles, exceptions, and competing actions. Each piece may have a history. Each may have made sense at the moment it was added. Together, they create a product that feels fat, difficult, and strangely hard to simplify.

By then, teams often describe the problem as technical debt, feature creep, or complexity. Those descriptions are true, but they are incomplete. The product is carrying the consequences of decisions that were never made. This is also why rebuilding can be so valuable.

Starting over is usually treated as a dangerous impulse. There are real things to lose: working behavior, unusual customer needs, knowledge embedded in old systems. The fear is understandable. The first version contains too much history to dismiss casually.

But rebuilding is not about pretending that history did not happen. It is about forcing history to justify itself.

When a product is rebuilt, every feature has to earn its place again. Every workflow has to explain why it exists. The old assumptions no longer receive automatic authority simply because they have been in the product for a long time.

Some things should carry forward. Many will.

But some complexity only survives because nobody has paused long enough to ask what it was protecting. The things that melt away during a rebuild are often the most revealing. They are not necessarily mistakes. They are artifacts of uncertainty that became permanent by accident.

The first version teaches the team what it did not know. The next version has the chance to decide.

This does not mean every unanswered question needs an immediate answer. Uncertainty is real, and sometimes waiting is the right decision. But waiting should be a decision too. It should have an owner, a reason, and a point at which the team comes back to resolve it.

Otherwise, the product will resolve it for you. And it will do so by getting bigger, slowler, and unruley.

Convenience Is the Product

Look in your pantry.

A surprising amount of what we buy is something we could have made ourselves.

Cocktail sauce is a good example. The basic idea is remarkably simple: tomato-based sauce, horseradish, vinegar, seasoning. (Heinz) Worcestershire sauce is a much more elaborate fermented condiment, but even it is ultimately a collection of ingredients and processes that could theoretically be reproduced at home.

Brownie mix makes the point even more clearly. A typical boxed mix is largely sugar, flour, cocoa, and a handful of other ingredients. Betty Crocker’s current mixes make the composition almost comically obvious on the label. (Betty Crocker)

Yet we buy these things and we fill our pantries with them.

The interesting part is that we are not really buying the ingredients. We are buying the time someone else spent assembling, measuring, packaging, testing, preserving, distributing, and explaining them.

That is convenience. And convenience is a much more fundamental concept in business than we usually give it credit for.

A business exists, in most cases, because there is something someone needs to do that they would rather not spend their own time doing. Even if you are buying a skill you don’t have, you are actually buying the time to have to learn that skill yourself.

Sometimes the thing takes skill. Sometimes it takes equipment. Sometimes it takes capital. Sometimes it takes experience. Sometimes it is simply annoying. And sometimes it takes five minutes away from what you would rather do. So you pay.

It doesn’t matter if it is software, AI, consulting, manufacturing, or physical labor. The implementation changes, but the underlying transaction is consistent.

This may sound like a shameless plug but it is truly the basis for why I published this in a kids book called Erec Makes a Fire almost 10 years ago. The story is intentionally simple because the underlying idea is simple and it is better to ingrain it in a child than repeat it to all the adults. Erec needs to accomplish something, but accomplishing it requires time and effort. The people around him each have different capabilities, and things they spend time doing.

Someone has a problem they could solve it themselves. You make it take less time and they pay you.

This is one of the reasons I think we sometimes misunderstand innovation.

We tend to imagine a business as creating something that did not exist before. That is certainly one kind of business. In reality 99.9% of the time, it’s a derivative. And for good reason – but not this post’s point.

A business takes something that already exists and make it easier, faster, cheaper, closer, reliable and/or understandable.

More convenient.

The product may look like software, a bottle of sauce, a box of brownie mix, or a person showing up at your house with a toolbox. Underneath, the value proposition is the same.

I will spend my time on this so you can spend yours on something else.

And that is actually a beautiful feature of an economy. We do not each have to become good at everything. We don’t need to astound the world with something never seen before. We don’t need to “innovate” beyond what customers have or need.

We are all simply and constantly buying back pieces of our own time. And that has scaled in layers for thousands of years.

The strange thing is that we often forget this when we talk about technology. We ask what AI can create, what software can automate, or what a new company has invented.

A more useful question is often much simpler:

What does this allow someone to stop spending their time doing?

That is where most of the value lives. The product is not always the thing in the box. Sometimes the product is the five minutes you just got back.

When Determinism Is Better Than AI

AI’s superpower is creating deterministic products.

We have become fascinated with AI’s “human-like” level of non-determinism. “I’ll just let AI handle it” is quickly becoming a default answer to problems we haven’t fully thought through, because if AI will figure it out, why should I?

One can ask a chatbot almost anything and marvel at what comes back. Give it a problem and it can find a path you did not anticipate. That unpredictability is part of what makes AI amazingly intelligent, and in many situations it is exactly what makes it the revolution in technology we dreamed of for decades.

But in some cases, we are using a sledgehammer to tap in a nail, and it is costing us a chance to provide the right solutions.

When delivering software as a product, the majority of products are not trying to surprise their users to provide value. Creativity sounds universally desirable, but humans require a surprising amount of consistency to be productive. We don’t generally ask our employees, engineers, doctors, lawyers, or PhDs to be randomly creative. We give them a mission, constraints, and some expectation of what a good result looks like. We want coffee on Tuesday morning at 9 AM, even though all the data points otherwise. Random human + informed AI + creative decisions = unhappy people.

AI doesn’t escape our expectations simply because it is intelligent.

A system that generates a different answer every time it is asked and is told to respond in certain ways with generalized prompts can aspire to be remarkably capable, but it can also be remarkably difficult to control, test, explain, package, interpret, and trust.

I don’t say this philosophically. I say it after years of wrangling AI to do things I hoped would create uniquely powerful products that didn’t exist. Why are the products we see so bad at converting this powerful tech into a powerful value prop? “SaaS is dead” encapsulates that problem. Copying and pasting your work into ChatGPT is often better than using a packaged product that tries to leverage the same AI to complete expected tasks efficiently or effectively. Chat is a bit messy, but it leans into that via the chat experience. That messiness is at odds with a packaged product.

I hear it every week from some aspiring entrepreneur: “Give the AI all your data and let it discover and manage [insert problem here].” It is a magical proposition that is within reach now.

Often it can turn into amazing demos and show unbelievable potential. But getting from a controlled demo to a reliable set of actions and outputs, unmonitored and unaudited by a human, is exceptionally hard. Even big companies with big wallets struggle with the transition. Small amounts of drift compound. Mistakes require review. Sometimes understanding what the AI produced takes nearly as much human effort as doing the work yourself, even if the human would have done the work somewhat worse. And don’t get me started on the cost of answering questions with non-deterministic expectations.

Perhaps we solve this. I hope we do. But in building these systems, I came to appreciate another application of AI that I think we’re underestimating.

Allowing the average person to let AI run their business may be a few years away. Building a business or product with AI tooling is already extraordinarily powerful, and not merely because AI lets us build software faster. It lets us afford complexity we previously couldn’t.

Historically, software has been deliberately simplified.

If a problem had a thousand paths, we rarely implemented a thousand paths. Humans had to anticipate the main cases, write the rules, implement them, test the edge cases, and maintain everything afterward. Eventually the economics would win. We scoped down to three possibilities and ignored the rest so that the software would work well for those cases, slowly building up one case at a time as needed.

AI changes the economics of that equation.

We can now use non-deterministic development tools to explore an enormous possibility space, then turn what we learn into deterministic software. A function that would take an immense level of brainpower to create efficiently, effectively, and robustly was expensive to build, let alone maintain as features were added. Now AI not only helps discover the paths, but can create elegant, hardened functions that deterministically handle all the possibilities you give it. Changing that functionality is no harder.

That distinction is more powerful than it initially sounds.

The power is in the fact that humans don’t typically expect to give or get thousands of outcomes from certain problems. Time and time again, we imagine a waterfall of possibilities when reality only requires an eyedropper’s worth of water.

Because of this tendency to overestimate the useful possibility space, 100 possibilities handled deterministically can feel remarkably intelligent. And with static, scalable, deterministic code, you are paying an infinitely smaller cost to solve these problems. It really is the best of both worlds: scalable software, intelligent-seeming computations, and extraordinarily low complexity to build and manage.

We get much of the richness we associate with AI without requiring an AI to improvise every interaction.

This isn’t an argument against live AI. Some problems genuinely benefit from open-ended answers. Chat obviously does. Search, research, documentation, creative work, and assistance are natural places for a system that can respond to questions nobody anticipated when the product was built. I have five bots running all the time to manage my email, calendar, video creation, socials, and more. They need slapping around now and again and require a lot of guidance, but they are far cheaper than hired help and always running 24 hours a day.

The thing I think nine out of ten people miss when the silver-bullet dream of AI is presented is this: infinite possibility isn’t a requirement for every problem.

Often the customer knows what they are trying to accomplish. Most consumers don’t need an infinite number of possible answers for the mountain of information that surrounds them. They need a sufficiently large number of great ones that consistently provide value.

That is the subtle opportunity I missed when I first started building heavily with AI. I was fascinated by AI’s ability to make software non-deterministic. I now think its ability to make deterministic software vastly more expressive may be just as important.

AI has dramatically lowered the cost of deliberate complexity.

Instead of asking AI to decide what our product should do every time it runs, we can use AI to help us consider far more of what it could do while we build it. We can then deliberately decide which of those possibilities it should handle and save ourselves and the customer more money than they thought possible.

The intelligence moves from improvising the product to constructing it.

And sometimes that is exactly where you want it.

Think in Sprites When You Generate Images with GPT

Here is a useful trick if you are generating a lot of small graphics with GPT.

Not every image you need actually needs to be generated as a standalone image.

A small icon, avatar, button state, game asset, UI decoration, or other graphic may only occupy a tiny amount of your final product. But image generation systems often work at resolutions much larger than the thing you actually need. ChatGPT Images now supports different aspect ratios and resolutions, but you are still generating a relatively large image for a relatively small object. Larger image outputs generally consume more image tokens and can take longer to generate. (OpenAI Help Center)

So think like a game developer from the 1990s. Think in sprites.

A sprite sheet is simply one image containing many smaller graphics. Instead of loading 100 individual images, a game could load one image and tell the graphics system which rectangle of that image contained the sprite it wanted. The browser or game engine would then display only that portion.

The same idea works surprisingly well with AI image generation.

1. Generate a grid instead of individual images

Suppose you need 20 tiny cartoon avatars for an application.

You could ask GPT to generate 20 images. That means 20 generation jobs, 20 waits, and potentially 20 times the generation overhead.

Instead, ask it for a grid:

Create a 10×10 grid containing 100 unique cartoon avatars. Each avatar should occupy exactly one cell. Keep the visual style consistent, but make every character distinct. [what you want the images of – in the case below clubhouse dressed, 2d, comic style, animals]

You now have one generated image.

Then split the image into its cells yourself. This is trivial with an image editor, a tiny script, or even a second AI-assisted step in your agentic environment.

The important part is that the generated image is now the container, not the final asset.

You can do the same thing for icons, illustrations, characters, graphics, UI elements, or almost anything where each individual asset is smaller than the generation system’s useful output size.

Here is an example of one I iterated on for a hobby project at http://www.skillbase.club where we made the theme country club based on the domain we could get.

A collage of illustrated animals wearing sports attire, including hats and vests, each with a unique expression. The animals include a variety of species such as birds, mammals, reptiles, and aquatic creatures.

That’s 100 avatars for the same time and token cost as a single oversized image.

2. Use grids for drafts, too

This is arguably the more useful trick.

Maybe you don’t need 20 images. Maybe you are trying to decide which version of one image you actually want. Instead of generating four separate versions, ask GPT for a 2×2 grid:

Create four variations of the same clubhouse in a 2×2 grid. Keep the subject identical, but make each cell explore a different composition. [or name each composition like Lichtenstein-esque or paper cut out]

Now you can compare four directions in a single generation.

Once you pick the winner, generate that one properly.

A large, elegant house with a spacious front porch and manicured lawn, set against a clear blue sky.

This changes the economics of experimentation. You are using the expensive, slow part of image generation to explore a space of possibilities, rather than paying the full cost to explore each possibility independently.

And there is a broader lesson here.

We spent decades learning to optimize graphics by separating the asset from the container used to transport it. AI image generation makes it easy to forget that distinction.

If you need something small, generate a sheet. If you need alternatives, generate a grid. Then cut out what you actually need.

What You Won’t Do Defines You

We often define ourselves by what we strive to build, the markets we want to enter, the customers we want to serve, and the visions we want to pursue. These are of course necessary, but they are not defining. The deeper source of identity is not what an organization chooses to do. It is what it chooses not to do.

This is uncomfortable because possibility feels like power. A capable team can always imagine another feature, another market, another customer segment, another partnership, another product line. The temptation is especially strong for talented founders because talent creates options, and options create the illusion of progress. The more a team believes it can do, the more vulnerable it becomes to doing too much.

But strategy is not the accumulation of possibility. Strategy is the disciplined rejection of most possibilities.

A company does not become distinct merely by having ambition. Ambition is abundant. What makes a company distinct is the courage to refuse attractive alternatives. The refusal to venture off into yet another direction is what gives a company its shape. It tells the team what matters, tells customers what to expect, and tells the market what the company is willing to sacrifice in order to become itself.

Artist are seen as those that are free to chase their every imagination, but making a 2 hour movie work is painstakingly an act of constraint and commitment. Imagine the push back that comes from wanting to represent a seen so badly that you push the a $10M shot that lasts 10 seconds. Could you commit to it when everything inside and outside your world is saying it is falling apart? What makes art such an inspiring form of achievement is the commitment to see a singular vision though to the end for – well – what some would call “just a little piece of expression”.

Apple is known for going big on marketing and charging a lot for their products. What most don’t see is Apple is a hallmark of restraint. Apple is not simply Apple because it builds computers, phones, operating systems, or services. Anyone can have a vision for a great computer or experience. Apple is Apple because it has historically chosen control over ubiquity. Upgrades over legacy. It has preferred integrated experience over maximum openness. That choice requires saying no to many obvious opportunities: software for every device, infinite customization, broad compatibility at any cost, and the short-term revenue that comes from being everywhere. (In fact, their lack of constraint is what almost put them under early in their growth – but that is a story for others to tell. )

Those refusals are not incidental. They are central to the product. That isn’t to say their decisions are required for success, but making those decisions and sticking to their identity is.

Few would say Microsoft is not successful and they are a very different looking company. For most of its history they made an opposite choice. Its power came from not being tied primarily to its own hardware. Windows and Office became dominant because Microsoft chose scale, backwards compatibility, options via licensing and distribution across a vast ecosystem of machines it did not manufacture. That strategic refusal gave Microsoft its own identity. It was not Apple, and that was the point. Both companies are shaped more by what they refused as by what they pursued.

The same pattern appears in many industries.

A company making a fast car is not merely choosing speed. It is often giving up affordability, mass accessibility, or low-maintenance practicality. A luxury brand is not merely choosing premium materials or elevated design. It is refusing accommodate everyone’s requests – in fact – they hope they are not. A great restaurant is not merely choosing a menu. It is refusing to serve every taste, every trend, and every customer expectation. The strength of the experience comes from the boundaries around it.

Most people know it when they see it, but chaos, disorganization, and what can feel like a lack of brand identity is merely the manifestation of a lack of restraint. Picking your lane is not giving up on a grand vision, it is dedicating your time to a specific outcome; it is an example of discipline. It is the difference between a light and a laser.

Even Costco, which appears on the surface to sell almost everything to everyone, is built on disciplined refusal. Its model depends on giving up selection of a myriad of options for the same product. It carries a limited number of SKUs, uses warehouse-like stores, bulks up on few variation of a product, and optimizes ruthlessly for efficiency and value. It will not take profits that don’t meet within these restraints.

This distinction matters because every “yes” has a cost beyond the immediate work required. Every yes changes the product. Every yes changes the company’s internal logic and dilutes identity.

Yes you can do anything, but no we can’t do everything.

How do we decide that line?

Proving ambition with breadth is how companies lose coherence. Rarely all at once. More often, they lose it through a series of individually reasonable expansions. One more feature. One more customer type. One more exception. One more “strategic” partnership. One more “why not?” One more compromise that seems harmless in isolation but slowly erodes the original center of gravity.

The problem is not that expansion is bad. Growth often requires expansion. The problem is having expansion without a theory of refusal. An investor without a thesis is throwing money around. Even if they make wins it is not a story they can sell, and without that sale there is no fund.

Without a clear understanding of what the company will not do, growth becomes drift. The company may get bigger, but it does not necessarily get stronger. It gains surface area while losing definition.

This is why the question “What are we building?” must be paired with a harder question: “What will we never build?”

What customer will we not serve?

What feature will we not add?

What revenue will we walk away from?

What behavior will we not reward?

What pressure will we not yield to, even when yielding would make the quarter easier?

What do we cut?

As the great quote says: I don’t sculpt stone into what it should look like, I chip away all the stone that isn’t.

For founders, this discipline is especially important because early companies are fragile systems. A young company has limited time, limited attention, and limited resources. You are ALWAYS stealing from Peter to pay Paul – you can’t pay both.

With AI the lack of constraints required is the very dirt founder can now bury themselves in ten-times over. A product cannot afford to become many things at once – even if it is able to. The founder’s job is not merely to inspire motion. It is to protect the company from compounding motion it can not manage. By being weighed down by its options.

In this sense, refusal is not negativity. It is not small thinking. It is not fear. It is not the lack of vision. It is the architecture of commitment. Vision may be the destination, but focus is not blowing all your travel money on the first stop. It is not getting married on the first date.

To choose anything seriously you must give up many other things. The stronger the choice, the stronger the refusals required to preserve it. This is true for products, brands, companies, and creative lives. You do not become distinct by remaining available to every possibility. You become distinct by rejecting most of them with enough conviction that the few remaining possibilities can be pursued deeply.

The world does not suffer from a shortage of people willing to add more. It suffers from a shortage of people willing to define what must be left out. That is where real strategy begins.

Keep It Right-But-Light

A clearer standard for building well

Most builders have inherited a bad vocabulary for early product work.

We tell people to build an MVP, but the phrase has become so overused that it often clarifies less than it confuses. Some people hear “minimum” and interpret it as permission to ship something brittle, awkward, or barely usable. Others hear “viable” and inflate the work into a miniature production system, complete with abstractions, infrastructure, tests, edge-case handling, and design polish that may never matter.

To balance that out we encourage people to “just hack it together,” which pushes the pendulum into a different problem. Hacky can be useful when it means fast, practical, and unconcerned with unnecessary ceremony. But hacky too often becomes an excuse for bad work. It works in the narrowest technical sense, but some see it as license to create terrible unpleasant use, error laden, difficult to understand, and fragile the moment reality touches it.

This is the gap “right-but-light” is meant to name.

Right-but-light means building something correctly enough that it earns trust, while keeping it light enough that it remains easy to change. It is not about doing less work. It is about refusing to carry more weight than the moment requires. It isn’t minimal or maximal. It is that harmonious balance so many folks have trouble finding. We don’t hack down the scope to get away with a bad implementation, we do it so the one thing we choose to do is done well – while still not evergreen or perfect.

The “right” part matters because users do not experience your product through your intentions. They experience it through the surface area you give them. A button that is twice as large as it should be may not break the system, but it may still signal that your offering is dangerously unkempt. A workflow that technically completes the task but makes the user think too hard won’t get used – at all. A prototype that loses data, hides errors, or makes the user feel unsure doesn’t give you the real signals you are looking for. These things may be excusable in a throwaway 24-hour hack-a-thon demo, but they are not balanced for an initial product seedling. They train the builder, the user, and the team to tolerate roughness where clarity was needed.

The “light” part matters because early certainty is usually fake. At the beginning of a product, most of the important learning still has to happen. The shape of the problem will change. The user may care about a different part of the workflow than expected. The thing you thought was a feature may turn out to be a demo path. The thing you thought was temporary may become the center of the product. In that environment, heavy architecture is often a liability disguised as discipline and thoughfulness.

A right-but-light implementation avoids both forms of waste. It does not ignore usability in the name of speed, and it does not overbuild permanence into something that has not yet earned permanence.

This distinction has become more important with agents. Human teams already struggled with the nuance between fast and sloppy, or thoughtful and overbuilt. Agents amplify that ambiguity. Ask an agent to “build an MVP” and it may produce a sprawling approximation of a real application. Ask it to “make it hacky” and it may skip the very details that make the product usable. The instruction was vague, so the output becomes a coin flip between underbuilt and overbuilt.

“Keep it right-but-light” is a better constraint because it tells both the human and the agent what kind of tradeoff is being made. The work should be correct in the parts that matter. The interface should be understandable. The happy path should feel intentional. The data should not vanish. The user should not have to forgive the product in order to use it.

But the implementation should remain light. Maybe the data lives in a JSON file before it earns a database. Maybe the workflow is hardcoded before it earns configurability. Maybe the UI uses an existing design pattern rather than inventing a new system. Maybe the feature does not need role-based permissions, event streams, audit trails, background jobs, and a full settings page on day one. Maybe the right version is simply the smallest version that behaves with taste.

Right-but-light also gives teams a cleaner stopping rule. The question is not “is this complete?” because early products are almost never complete. The better question is: “Is this right enough to learn from, and light enough to change after we learn?”

That question changes the conversation. It moves the team away from false binaries. You are no longer choosing between a disposable hack and a production-grade system. You are choosing the minimum level of correctness required for meaningful use, and the minimum level of structure required for responsible iteration. Yes, that is what MVP and lean startup. It is just that I have tried for YEARS to explain that to people and it either doesn’t land or get me in to trouble with “you said to do the minimum!”

For example, a right-but-light onboarding flow may not need analytics dashboards, branching personalization, enterprise configuration, and a polished admin panel. But it probably does need clear copy, coherent visual hierarchy, a reset path for testing, and enough state handling that the user does not get trapped. A right-but-light internal tool may not need a formal database schema or complex permissions. But it probably does need predictable inputs, readable outputs, and error states that do not require the builder to stand nearby and explain what went wrong.

There are many great product folks out there building things you love, and I am sure you have used a product you loved and realized there was no delete button and wonders “WTF? Why can’t I delete?!”. If you get a req for your unaware peers in different departments they would have died on the sword to put the obviousl “delete” in on v1, but no feature is truly “simple” and without a time cost. So, that product manager says “they need to love what they create before they need to delete.” One less unit of time for one side of the product, one extra unit of time for another. No one has infinite time or infinite money. No one.

So how do you chose? The point is not to lower standards. It is to place the standard in the right part of the work and be okay with walking away from the other.

Long-lasting applications deserve robustness, scalability, observability, automated tests, security reviews, edge-case handling, and design precision. But not every early feature has earned that full burden yet.

Premature permanence slows learning. It turns every change into a negotiation with yesterday’s assumptions.

Speed without standards is not iteration. It is motion. Wheels spinning fast in the mud.

It asks for seriousness without heaviness. It asks for speed without sloppiness. It asks for taste without vanity. It asks the builder to cut everything that is not required, while doing the remaining things with care.

That is the deeper meaning of the phrase.

Right-but-light is not another way to say MVP. It is a correction to what MVP has become. It restores the part people forgot when “agile” and “MVP” became a household term.

Can You, Did You, and Taste

Three stages separate ideas from products, and products from things people love.

The first stage is capability.

Can it be done? Can the software be written? Can the company be started? Can the product be built? Can the problem be solved?

These questions matter because capability is the foundation upon which everything else rests. Nothing can be executed, adopted, or admired until it is first plausible.

This is where most people rest comfortably.

The surprising thing about capability is that proving something can be done often provides many of the same emotional rewards as actually doing it. Once someone becomes convinced they could build the company, write the book, get in shape, learn the skill, or launch the product, the pressure largely disappears. Potential becomes a source of comfort.

The entrepreneur enjoys imagining the startup. The author enjoys discussing the book. The engineer enjoys architecting the system. The athlete enjoys knowing they could get serious whenever they decide the time is right. People tend to love their own brains and marvel at what it can achieve. Potential is attractive because it is free from accountability. It cannot fail because it does not yet exist.

Did you actually do it?

The second stage is execution. It only lives in the past tense. This is where the conversation changes. Ideas become products. Plans become companies. Discussions become outcomes. Concepts become features available for use and critique to the public domain. The world stops evaluating intentions and starts evaluating evidence.

A customer cannot buy potential. A user cannot interact with ambition. An investor cannot generate returns from possibility alone (though they push this rule as much as possible). The market, unlike our friends and colleagues, is remarkably indifferent to what could have happened. It only responds to what did happen.

People who reach this stage deserve significant credit. The distance between an idea and reality is far larger than most people appreciate. Building something that survives contact with the real world is difficult. Something complete, end-to-end. It accomplishes its intend (big or small) and no hand holding or excuses are needed for it to be used properly. Launching is difficult. Selling is difficult. Maintaining momentum is difficult.

Most people never get there. And I am being generous with “most”.

Yet many who do arrive at execution mistakenly believe they have reached the finish line. They assume that because something works, people will care. They assume that because a solution is objectively better, adoption will naturally follow. They assume that utility alone is enough. They believe a logical use case exists and therefore usage will follow. A good plan and execution is all that is needed.

History suggests otherwise.

The graveyard of technology is filled with products that worked. Many were faster than their competitors. Many were technically superior. Some were years ahead of their time. Their failure was not one of engineering. Their failure was assuming that human beings make decisions primarily through logic.

Taste.

Taste is one of the most misunderstood concepts in business because it is often reduced to aesthetics. People hear the word and think about typography, color palettes, industrial design, architecture, or fashion. Those things matter, but they are only symptoms of something deeper.

Taste is the ability to understand how another human being will experience what you have created.

It is the recognition that people do not merely consume functionality. They consume stories, emotions, identity, aspiration, status, trust, culture, and delight. A chair is not simply somewhere to sit. A restaurant is not merely a place to eat. A home is not simply shelter. A product is not just a collection of features. Even a purposefully poort taste for those that are tasteless is, in fact, a taste. The trickle down story line of decisions and focused intent lead to those that have the same test to become interested. Taste is not being fashionable, it is knowing people need clothes and certain groups of people like cheap clothes, some like expensive clothes, and some like expensive clothes that are on sale – but they know which group of tasters they want to have.

Every meaningful creation eventually becomes an experience.

This is why two products with nearly identical functionality can produce radically different outcomes. One becomes beloved while the other is forgotten. One creates a movement while the other creates a user base. One becomes part of a person’s identity while the other remains a tool.

The difference is often explained by taste.

The creators who understand taste recognize that presentation is part of the product. Storytelling is part of the product. Culture is part of the product. The emotional experience surrounding something is not separate from what is being built. It is one of the things being built.

Most people spend their lives asking whether something can be done. A much smaller group proves that it can. The rarest creators understand that neither capability nor execution guarantees significance.

The first stage asks whether something is possible.

The second proves that it is.

The third determines whether anyone cares.

Socratic AI: The debate-based Writing Method to create better content

When asking AI to write articles, I think most people prompt apps to “Write about this…”. They provide some details about what to write, more or less, and then use AI to help with the editing. It’s a kin to having an editor or ghost writer.

I started in the same way, but always felt like I was battling the AI instead of working with it. I’ve come to use it very differently. Not do I love this new method but I learn a lot from the experience each time.

Instead of asking AI to write for me, I use it to think through concepts with me. To have it debate or question my thoughts. To specifically “not write an article” for quite some time until I think we are on the same page. This can sometimes take weeks strewn with small chats with long breaks in between until a new thought spark up again.

This whole approach started by accident when I discovered more personality with GPT 4. One day I got riled up from reading some shallow post. It sparked a mental argument with myself to try and see how “the other side” could come to such a different conclusion. On a whim I gave ChatGPT a chance to give me the other side and it surprised me. It not only delicately agreed with my POV, but it gave another potential position followed by “if you could change the circumstance how would you do it?”

It didn’t just echo my points. It pushed back. It made counterarguments. It sharpened the conversation. I ended up having a long conversation with the AI. By the end of it, I understood my own idea better. I felt like I had a smart, patient thought partner who genuinely got what I was trying to work through. It was mind blowing.

That’s when it hit me. If GPT can do this with abstract ideas, why not use the same kind of back-and-forth to help me write?

That’s how this process was born. I’m not starting with a goal to create a draft. I’m starting with a goal to think through a conversation and see where it leads.

What I’ve found feels like a modern revival of the Socratic dialectic. It gives me a space where I can toss out half-formed thoughts, question assumptions, test ideas, and refine them through dialogue. Some go nowhere, but all end with a better grasp of my original thought or counter thoughts.

I keep all my writing in a single project so GPT has context from everything I’ve written or said before. When I want to explore something new, I open a fresh thread and say:

“I don’t want anything created yet. I want to jot thoughts down and then I’ll let you know if I’m ready to create something or if I want to dig deeper.”

Then I just post whatever comes to mind. No outline. No goal. Just the original vapor of a concept. Sometimes I ramble. Sometimes I loop back or take side paths. Sometimes I ask:

“What do you think?” or “Is there a counterpoint I’m missing?”

And it responds. Not with a final draft, but with friction. With momentum. With more angles to explore.

I think best in conversation. I rarely find clarity in a vacuum. Often I will argue a point with someone and walk away with a whole new version or perspective on my belief. Often, I push on ideas, debate myself, and churn.

So when GPT became more conversational, it clicked. It felt like I finally had a thinking partner who didn’t judge, remembered everything, and has no distinct side. The result isn’t just better writing. It’s better thinking.

Once the idea has been explored enough, I ask GPT to turn the thread into an article. Since it has been there for the full conversation and already knows my tone from past articles, the first draft usually comes back pretty close to what I want.

It is never final, but far more inline and final than anything I have ever tried to create with AI before.

Once I am done I end the thread with my final post in my project:

“Here’s the one I actually used. Save this to memory. No more feedback or follow up needed.”

Over time, it learns me. My tone. My rhythm. The kinds of lines I keep, the ones I cut, and the ones I repeat for emphasis. It becomes both a mirror and a co-writer.

So no, I don’t start by asking GPT to write something. I start by asking it to listen. To push back. To help me think through things better. This isn’t AI-assisted writing, it is AI-assisted dialectic.

Updated Review of LLM Based Development

I tried developing using GPT mid-2022. While I was amazed by the potential, I was not impressed enough to add it to my daily development workflow. It fell off my radar as a development tool, outpaced by a far more impactful use of text generation and image creation. A toolset that has significantly changed my day-to-day productivity.

Recently, a group of peers convinced me to give coding with LLM another shot. They loved using A.I. to develop code in languages they were not comfortable with. Or, as a manager, a way to better explain what they wanted to see in a project from their team. What convinced me to try it again was their highlighting of how well the results were formatted, syntactically correct, and well documented from the get-go. While they admitted the development of code may not be faster, the prospect of all those benefits culminating into a cleaner, well formatted final product convinced me to develop with GPT again in earnest.

I began my reexamination of the tooling via demos, as we often do. I was very impressed. I converted code into PowerShell (which I don’t know well) and re-created functionality I came across in weeks prior. I was so impressed, I showed my team examples of how the work they completed in the past could’ve been done with the help of GPT instead.

After those successes, I committed to using GPT to develop. Over the next few weeks I made sure to use it on projects I was working on.

While the technology showed incredible advancements since I tried it last year, it still hasn’t become my go-to in the same way using ChatGPT has for writing content.

Here are some areas I was both impressed with but left wanting:

Code completion
- Pro: Impressive. The look-ahead came in handy similarly to code-completion functionality of the past, with the added benefit of more contextual relevance that was not just a “cookie cutter” snippet.
- Con: It gave me a useless hint quite a bit and I found myself typing almost as much as before with the incumbent “dumb completion”. I think it is because my mind is moving ahead to what I want the code to do, not necessarily what it is doing on the console at the moment. In the end, it is using patterns to make predictions. So, any new code that is a result of changes to my approach, or my on-the-fly reworking to fix a bug (that was not due to syntax issues) took as much time to develop as non-GPT-based code completion.
Testing
- Pro: When it comes to testing an existing application, the A.I. hits it out of the park. Ask it to “write a test for myFunction() using Jest” it creates an awesome base test case that I would have hated to write for each function.
- Con: Some of the same issues outlined in the “Code Completion” and Functional Development” can be problematic here. It doesn’t always create a great test for code I haven’t written yet. (i.e. TDD) However, if the code is already there, it uses that context I’ve provided and its LLM to unpack what it the function is suppose to do and generate all the mocks and assertions needed to create a well written unit test.
Functional Development
- Pro: Much like helping me get past the dreaded blank page in text generation, I found it more useful than Google searches and StackOverflow reviews to develop a series of functions I wanted, without developing entirely from scratch. Better than code snippets, the snippets A.I. gave were pre-filled based on my prompts, variables, and existing object definitions. That was appreciated. I didn’t have to review the documentation to tweeze out the result I wanted. The A.I. pulled it all together for me.
  Additionally, the fact that it almost always has an answer goes under appreciated in other reviews I’ve read. The part that makes it so advanced, is it fills in a lot of grey area even if I (as a stupid human) carelessly leave out an instruction that is critical in generating a solution. If I were to get the response, “could not understand your request” due to my laziness, I would never use it. The assumptions it makes are close enough to my intent that I am either using the solution, learn a new path, or see what my explanation is missing so I can improve how I communicate with it.
- Con: The end result did not work out of the gate most of the time. Sometimes it never got it correct and I had to Google the documentation to figure the issue. This was due to what I think was more than one documentation existing for various versions of the library I was using. I’m not sure. While the syntax was correct, the parameters it assumed I needed, or the way the calls were made to interface with a library/API led to errors.
Debugging
- Pro: Per the “functional development” points above, I was impressed at how I could respond to a prompt result with “I got this error when using the code above: [error]”. It recognized where it went wrong, and attempted to rewrite the code based on that feedback.
- Con: Each response had a different result than the original. So, instead of fixing what I found was wrong (like a param missing) it also added or removed other features from the code that were correct. This made the generated result difficult to utilize. In some cases, it could never understand the issue well enough to generate working code.

One limitation I am not too surprised about, and am hopeful to see evolve in the future, is the AI’s understanding of a project in its entirety. Done so in a way that context is used in its function creation, making the solutions it provides “full stack”. Imagine a Serverless.com config, for an AWS deployment, that generates files and code that creates and deploys workflows using Lambda, DynamoDB, S3 and so on, all being developed based on prompts. With the yearly (and more recently) weekly leaps, I don’t think we are to far away.

As of today, I find myself going to GPT when filling in starter templates for a new project. I find it’s a much better starting point than starting from cookie cutter function as I set up my core, early, “re-inventing the wheel”-type, skeleton.

For example, I will use a Gitlab template for my infrastructure (be it GL Pages, Serverless, React, nodejs or Python and on and on), then fill in the starter code an tests via a few GPT prompts, and copying them over. Beyond that copy, I find myself detaching from GPT for the most part, and returning to occasionally “rubber duck” new framework functions.

Examples referenced above