Stop Calling Everything Made With AI Slop
AI slop means low-quality content, not AI-assisted work. The three markers of slop, and the review habits that separate skilled AI-assisted work from filler.
Scroll any comment section under a blog post, a video, or a song in 2026 and you will find the same two-word review. “AI slop.” Sometimes the label fits. The post invents a statistic, the video melts into nonsense halfway through, or the article repeats the same empty point six times in slightly different words.
Other times the label lands on work that is careful, accurate, and genuinely useful. Someone spent hours on the research, wrote pages of their own notes, argued with the draft, fixed what the model got wrong, and shipped something worth reading. A commenter spots a familiar phrase or a too-clean thumbnail and dismisses the whole thing in two words.
I want to make a case that sounds simple but gets lost in the noise. Bad AI-assisted content is bad because it is bad, not because AI touched it. Good AI-assisted content exists, it takes real skill to make, and the only way anyone builds that skill is by using the tools often enough to get past the clumsy early stage.
I have not been shy about how I work. I use AI heavily, mostly in areas where I already have deep knowledge, and I use it to move faster. I also make my mistakes in public, because public mistakes bring feedback, and feedback is how I get better. This article explains what slop actually is, why every creative tool goes through this same backlash, what I learned by doing it over and over, and how I think we should judge creative work from here on out.
What Slop Actually Is
The word has a real definition now. Merriam-Webster named “slop” its 2025 Word of the Year and defined it as digital content of low quality that is produced usually in quantity by means of artificial intelligence. Macquarie Dictionary picked “AI slop” as its word of the year too. The term earned its spot. The internet filled up with junk fast.
Read the definition closely, though. “Low quality” sits right in the middle of it. Quality is the defining trait. AI is the usual production method, but the definition does not say every AI-produced thing is slop. It says slop is low-quality content, usually mass-produced with AI.
That distinction matters, because a lot of people use the word to mean “anything I suspect involved a model.” Those are two very different claims. One is a judgment about the work. The other is a guess about the workflow.
So what puts something in the slop bin? I see three clear markers.
- It tries to misinform. The content spreads false claims, fabricated quotes, fake sources, or invented events. Sometimes this is deliberate. Sometimes the creator just never checked. The result for the reader is the same.
- It adds no value. The piece says nothing a reader did not already know. It restates the obvious, pads the length, and leaves you with no new idea, no new skill, and no new perspective.
- It clearly was not reviewed. The draft still contains the model’s mistakes. You see broken references, contradictions between sections, hallucinated version numbers, and leftover phrases like “as an AI model” or “here is your blog post.”
Every one of these markers describes a failure of the creator, not the tool. A human writer with no AI at all can misinform, add nothing, and skip editing. We just had other names for that content before 2023. We called it content farm filler, clickbait, or spam.
The volume is the new part. A content farm used to need a room full of underpaid writers. Now one person with a script can publish thousands of pages a day. Streaming service Deezer has reported that a large share of the music uploaded to its platform every day is fully AI-generated, at one point estimating about 28 percent of new uploads. That flood is real, and people have every right to be tired of it.
But fatigue makes people sloppy judges. When the default reaction to any hint of AI is “slop,” the label stops meaning “low quality.” It starts meaning “made with a tool I disapprove of.” That shift hurts the people who use these tools well, and it teaches newcomers that the right move is to hide their process instead of improving it.
Every Creative Tool Got This Reception
The backlash against AI-assisted work feels new, but the pattern is old. Almost every tool that lowered the barrier to creative work got called cheating, fake, or the death of real art.
Music has the cleanest example. In 1982, the UK Musicians’ Union passed a motion to ban synthesizers, drum machines, and any electronic devices capable of recreating the sounds of conventional instruments. The union feared these machines threatened the jobs of session players. The motion came after Barry Manilow toured the UK using synths to simulate the sound of a big band orchestra.
The ban went nowhere. Drum machines went on to shape hip-hop, house, and techno. Those genres did not replace older music. They added new music that did not exist before those instruments arrived. The union was right that the work was about to change. It was wrong that the change meant the end of real musicianship.
Photo editing went through the same cycle. Adobe released Photoshop 1.0 in 1990. For years after, “photoshopped” worked as an insult. It meant fake, manipulated, dishonest. Today nearly every professional photo passes through editing software, and nobody accuses a wedding photographer of fraud for adjusting exposure. People still criticize manipulated images, and they should when the edit deceives. The criticism shifted from “you used the tool” to “you used the tool to lie.”
Digital audio workstations (DAWs) followed the same arc. A DAW is software for recording, editing, and arranging music on a computer. Tools like FL Studio, which started life as FruityLoops in the late 1990s, let a teenager with a laptop build tracks that once needed a studio, an engineer, and a band. Early bedroom producers got mocked for not playing “real” instruments. Some of those bedroom producers now headline festivals.
I have picked up a lot of these tools over the years. Photoshop, FL Studio, Videoleap, and plenty more. I learned each one for the same reason: it let me make the ideas in my head real. AI tools are the latest entry on that list.
Not every critic in these cycles was wrong. Some synth-heavy records from the 1980s really are thin. Plenty of early Photoshop work really was garish. Tools do get misused, especially early, when nobody has figured out the craft yet.
Where the critics went wrong was the target. They judged the tool when they should have judged the output. Over time, the culture corrected. The tool stopped being the story, and the work became the story again. I expect the same correction for AI-assisted work. The only open question is how many capable creators we discourage before it happens.
Using AI Well Is a Skill, and Skills Come From Reps
Here is the part that gets lost in most arguments about AI content. Using these tools well is a skill, and it behaves like every other craft skill. You start out bad, you get feedback, you adjust, and you slowly get good.
Nobody opens a DAW for the first time and mixes a radio-ready track. Nobody opens Photoshop and produces a clean composite on day one. The first hundred attempts teach you where the controls are. The next hundred teach you what good looks like. After that, you start to build taste and speed at the same time.
AI tools work the same way. Your first AI-assisted blog post will probably read flat. Your first AI-generated image will have strange hands or warped text. Your first AI-assisted song will sound generic. That tells you that you are a beginner, which is exactly what everyone is at the start of any skill. It says very little about the ceiling of the tool.
The people quickest to shame AI users often point to those beginner outputs as proof. “Look how bad this is.” They are judging a first draft from a first-week user and treating it as the ceiling of the medium. Nobody judges guitar by listening to someone on their third lesson.
My own practice looks like this. I mostly use AI in areas where I already know the material well. I have co-written books on Apache Iceberg and Apache Polaris and written another on architecting an Iceberg lakehouse. When a model drafts something in that territory, my expertise does the checking. The model does the typing, the first-pass research, and the structural grunt work.
That pairing is where AI pays off the most. Expertise plus AI acceleration beats either one alone. An expert without AI moves slower. AI without an expert produces confident nonsense. The skill lives in the combination.
The clearest example from my own work is my personal websites. I manage them myself with AI assistance. That work used to need a team of specialists: a web developer, a copyeditor, a graphic designer. Now I handle the full workflow on my own, end to end. At a larger scale, my plan is not a row of narrow specialists. I want a small team of generalists, each one owning complete workflows end to end with AI support, all pointed at a shared goal.
None of that came free. I got there by doing the work over and over, and by doing it in public. I make mistakes in public without shame, because that is how I get the feedback and the lessons that let me improve quickly.
This is why I push back when people say “just don’t use AI until it’s good.” The tools keep getting better, but the person using them only gets better through practice. If you wait on the sidelines until the technology is perfect, you will show up with zero experience while everyone who practiced has years of instinct on you.
What the Early Reps Taught Me About the “AI Meh” Feel
Every heavy AI user eventually learns to recognize a certain texture. I call it the “AI meh” feel. The content is not wrong, exactly. It is grammatical, organized, and on topic. It is also forgettable. Readers sense it within a paragraph, even when they cannot name what bothers them.
Learning what gives content that feel, and how to get rid of it, was one of the first lessons that came from doing this regularly.
The meh feel tends to come from a handful of habits that language models fall into when nobody steers them.
Generic openings. Models love to open with a broad statement about how fast the world changes or how important a topic has become. Readers skip these lines automatically. A strong opening starts with a concrete problem the reader recognizes.
Filler transitions. Stiff, formal connector words pile up at the start of sentences in AI drafts. They make prose sound formal while carrying no meaning. The same goes for phrases that announce importance instead of demonstrating it.
Rhythm lists. Models reach for groups of three adjectives because the rhythm sounds polished. After a few paragraphs, the pattern becomes obvious, and the reader starts to tune out.
Hedging. Unsteered drafts qualify everything. Every claim gets a softener in front of it. The result reads as if nobody stands behind any of it.
Missing opinion. This one matters most. A model with no direction defaults to a balanced summary of every side. Balanced summaries have their place, but they have no voice. Readers come back to creators because those creators see things a certain way.
I keep a list of style rules and banned words for anything written in my voice. No em dashes. No stacked formal transitions. No hedge words in the body of the piece. Active voice. Short paragraphs. Concrete numbers over vague claims of size. Those rules go to the model as part of the instructions whenever it drafts in my voice.
Rules only fix the surface, though. The deeper fix for the meh feel is input. A model drafts flat content when it has nothing specific to work with. Give it a generic prompt and you get the average of everything it has read. Give it your specific angle, your notes, your opinions, and the examples you actually care about, and the draft starts to sound like a person with a point of view.
This article is a good example. It started with a long set of notes in my own words. Every argument in it, every example, every opinion came from those notes. The model helped organize the material, expand it, and tighten it against my style rules. The thinking is mine. The acceleration came from the tool.
That is the real difference between slop and skilled AI-assisted work. Slop starts with a topic. Skilled work starts with a point of view.
Verifying Efficiently: Know Where the Model Breaks
The second big lesson from the early reps was about review. Beginners tend to make one of two mistakes. Some skip review entirely and publish whatever the model gives them. That is how slop gets made. Others review every sentence with equal suspicion, which takes so long that the AI saves them no time at all.
Efficient review means knowing where the model is strong and where it is weak, then spending your attention where the weaknesses live. A good editor already works this way with human writers. You learn which writer mangles dates and which one overstates conclusions, and you read their drafts with that in mind.
Language models have fairly predictable weak spots. Here is how I think about the split.
| Area | How models tend to perform | Where review time should go |
|---|---|---|
| Structure and outlining | Strong. Models organize material into a sensible order quickly. | Light check that the order matches the argument. |
| Grammar and clarity | Strong. Sentences come out clean. | Almost none, beyond style rules. |
| Explaining well-established concepts | Mostly strong for widely documented topics. | Spot check for subtle oversimplification. |
| Version numbers, release dates, current facts | Weak. Training data goes stale and models fill gaps with plausible guesses. | Every single one gets verified against a current source. |
| Quotes and attributions | Weak. Models blend sources or invent wording. | Every quote gets traced to its origin or cut. |
| Statistics and benchmarks | Weak. Numbers look precise and come from nowhere. | Every number gets a source or gets cut. |
| Niche or fast-moving technical detail | Mixed. Accuracy drops as the topic gets more specialized. | Heavy review, which is where the creator’s expertise earns its keep. |
| Personal stories and experiences | Dangerous. Models invent anecdotes to make prose feel human. | Anything first-person the creator did not supply gets deleted. |
That last row deserves extra attention. An unsteered model writing in your voice will happily invent a conference you never attended or a customer you never met. Those fake stories read well, which makes them dangerous. I have a standing rule that no anecdote appears in my content unless it actually happened to me. The model does not get to make up my life.
Review also gets faster when you automate the mechanical parts. My style rules are specific enough to check with a script. Here is a version of the verification pass for my long-form drafts.
F=article.md
# Word count: long-form pieces need to clear 6,000 words
wc -w "$F"
# Em dashes and en dashes: must return nothing
grep -nE '—|–' "$F"
# Semicolons: must return nothing outside code blocks
grep -n ';' "$F"
# Banned vocabulary sweep: the words that make prose read like a template
grep -niE '\b(delve|leverag|seamless|paradigm|synergy|holistic|robust|unlock|realm|tapestry|testament|embark|myriad|plethora|pivotal|transformative|bespoke|curated|meticulous|elevate|empower|streamline|foster)\w*' "$F"
# Hedging modals: claims get stated plainly or not at all
grep -niE '\b(might|could|would|may) ' "$F"
# Call to action present
grep -n 'books.alexmerced.com' "$F"Here is what each part does and why it exists.
The first line sets a variable named F to the file path, so every later command checks the same file without retyping the name.
The wc -w command counts words. Long-form pieces have a target length, and models often land short when asked for a long draft in one pass. A word count catches that right away, before anyone spends time reading a draft that needs more material anyway.
The dash check uses grep -nE with a pattern that matches either an em dash or an en dash. The -n flag prints line numbers, so each hit is easy to find. Models lean on em dashes heavily, and a page full of them is one of the fastest tells of unedited AI prose.
The semicolon check works the same way. My style rules keep semicolons out of prose, so the only acceptable hits are inside code blocks.
The vocabulary sweep uses -i for case-insensitive matching and \b for word boundaries, so one stem catches every form of a word, capitalized or not. Every word on that list is technically fine English. The problem is frequency. Models reach for these words far more often than people do, and a reader who sees three of them in a paragraph stops trusting the writing.
The hedging check looks for soft modal verbs followed by a space. The goal is plain claims. If a claim is true, state it. If it holds only under certain conditions, name the conditions.
The last line confirms the call to action made it into the final draft. That sounds trivial, but when a model rewrites a closing section, the link sometimes disappears.
None of this replaces reading the piece. The script handles the mechanical checks in about a second, which frees my attention for the parts only a human expert can judge. Is the argument right? Is the technical claim accurate? Does this say something worth saying?
That division of labor is the whole point. The machine checks the machine-checkable. I check the meaning.
The Flow Changes With the Model, the Tool, and the Medium
Once you have a process that works, it is tempting to treat it as finished. It never is. My workflow shifts every time one of three things changes.
The model. Different models have different habits. One model leans on certain phrases, while another pads its output with summaries at the end of every section. One is more likely to invent citations, another is more cautious but more generic. When I switch models, my review checklist shifts with it. The weak spots in the table above hold up broadly, but the details move.
The tool around the model. The same model behaves differently depending on the app or agent setup wrapped around it. A chat window, a coding agent with file access, and a writing tool with a custom style guide all produce different results. Some setups let the model search the web, which helps with current facts but introduces new risks around sources. Others let the model run code, which helps with verification. Learning what each setup is good at is its own skill.
The medium. Writing, video, and music each have their own failure modes. In writing, the risk is factual errors and the meh feel. In video, it is visual consistency from shot to shot, characters whose faces drift, and motion that breaks physics in distracting ways. In music, it is generic arrangements, muddy mixes, and lyrics that rhyme without saying anything.
Each medium needs its own review habits. For an article, review targets facts and voice. For a video, it targets continuity from shot to shot, and anything that breaks the illusion gets cut. For a song, it targets the moments where the arrangement goes on autopilot.
The underlying pattern stays the same across all three. You learn where the tool is reliable. You learn where it fails. You put your effort where the failures are. And the only way to build that map is to make a lot of things and pay attention to what goes wrong.
This is also why one attempt, years ago, is a weak basis for deciding AI is useless. The tools they tried are gone. The models changed, the setups changed, and the outputs changed. More to the point, they never built the map. One try tells you almost nothing about what a practiced user can do.
”If AI Wrote It, Why Should I Read It?”
This is a common objection, and it deserves a real answer. The argument goes like this. If a machine wrote the piece, the writer put in no effort, so the reader owes it no attention. Why spend my time on something nobody bothered to write?
The objection rests on an assumption that the person contributed nothing. For slop, that assumption is usually correct. Someone typed a topic into a box, copied the output, and hit publish. There is no person in that content, so there is nothing of a person to read.
Skilled AI-assisted work is different. The creator puts themselves into the piece at every stage that matters.
The angle. Before any drafting happens, someone decides what the piece argues. A model left alone writes the average take. A creator with a point of view picks the specific claim, the counterintuitive framing, or the uncomfortable truth that makes the piece worth reading.
The research. Someone decides which sources to trust, which facts to verify, and which examples best support the point. Those choices shape the piece as much as any sentence does.
The notes. Strong AI-assisted pieces start with the creator’s own notes. Those notes hold their opinions, their reflections, the wisdom they have built up, and the arguments they want to make. Those notes are the raw material. The model shapes them. It does not invent them.
The judgment. Someone reads the draft, rejects what is wrong, pushes back on what is weak, and decides when the piece is done. That editorial judgment is a creative act. Film directors do not operate every camera, and nobody says their films lack a creator.
When all four are present, the reader gets the creator’s thinking, delivered faster than typing it alone. That is a good deal for the reader.
There is a second half to this objection worth answering. People say, “If AI wrote it, why should I read it? I can just ask AI.” Fair enough. Ask AI to help you read it, then. Ask a model to summarize a long piece, pull out the key claims, or explain a section you found confusing.
Reading can be AI-assisted the same way writing can. There is no shame in that. A dense technical article is a lot more approachable when you can ask follow-up questions about it. Using AI to read does not mean the piece should not have been written. It means the reader found a faster way into it.
The piece still had to exist. Somebody still had to choose the argument, gather the evidence, and stand behind the conclusions. A model summarizing an article you never wrote has nothing to summarize.
And there is real pleasure in reading something slowly, on your own, with no assistance at all. Some books deserve your full, unassisted attention. Neither approach is the correct one. Both are tools a reader can pick up depending on what they want from the experience.
What Good Looks Like
Arguments are easier to evaluate with examples, so here are two. One comes from a creator whose work shows what this technology can do. The other comes from my own music.
Gossip Goblin
The filmmaker Zack London, known online as Gossip Goblin, makes dark, strange science fiction short films with AI video tools. His channel has passed 1.4 million subscribers and more than a billion total views. His first feature-length film, an anthology called Gods Don’t Give Gifts, was scheduled to open in several hundred U.S. theaters on October 30, 2026, produced with the studio Fable.
What makes his work relevant to this argument is how much human effort goes into it. London has said his feature still needed a human-written script, a dozen voice actors, and a post-production crew. Reporting on the feature describes a team of 10 artists working on it over two months. His process starts with a script, then builds every shot from a still image generated in Midjourney, refined in other tools, and only then animated into video. That is a slower, more hands-on pipeline than the one-click generation most people picture.
London has also said plainly that AI is a tool for him and not the point. It gave him access to filmmaking without replacing crafts he already valued, like illustration. His shorts do not hide the strangeness of the medium. They lean into it and build a recognizable visual style from it.
You do not have to like his films. Taste is personal. But nobody watching his work in good faith calls it low effort. It has a vision, a consistent world, and a recognizable voice. Those are the things that separate art from filler in any medium. AI did not supply them. London did.
My old acoustic recordings
The second example is smaller and more personal. I have old acoustic recordings from my younger days. They hold my effort and my vision from that time.
Suno is an AI music platform, and one of its features lets you upload your own audio and generate a new version in a different style while keeping the original melody. My old acoustic recordings have become the seeds for full productions.
The songwriting, the melodies, and the emotional core come from my younger self. The production comes from a tool that did not exist back then. Neither approach makes these songs alone, and the result is a fun bridge between two versions of me.
Is that slop? Run it through the three markers. It does not misinform anyone. It adds value by finishing work that started years ago. And the review test comes down to keeping the versions that serve the song and tossing the ones that miss. By the definition that matters, it is not slop. It is a creative project that uses a new tool.
There Is Room for Both Markets
I want to be clear about one thing. I am not arguing that all content should be AI-assisted. I do not think that, and I do not want that.
There will be a strong market for creators who make everything without AI, and there should be. Some audiences care deeply about knowing that a human did every step. That preference is valid, and the market already serves it. Bandcamp, the music platform popular with independent artists, announced in January 2026 that it no longer allows music generated wholly or in substantial part by AI. The company framed the policy as a way to keep its community human. That is a reasonable choice for a platform, and it gives listeners who want fully human music a clear place to find it.
Handmade work has always held value next to manufactured work. Hand-thrown pottery sells alongside factory dishware. Film photography survives next to digital cameras. Live acoustic sets still draw crowds in a world full of programmed beats. Those markets did not vanish when the faster tool arrived. They found their audience, and the handmade quality became a feature people choose on purpose.
The same thing will happen with writing, video, and music. Some creators will advertise that they use no AI, and some audiences will seek them out. Other creators will use AI heavily and openly, and their audiences will care about the output, not the process.
What I push back on is the idea that one camp gets to define the other as illegitimate. The human-only creator is not a dinosaur. The AI-assisted creator is not a fraud. They are making different choices about tools, and both can produce excellent work or terrible work.
Every major creative technology eventually settles into some balance between efficiency and quality. Photoshop did not make every photo fake. DAWs did not make every song soulless. The balance point took years of experimentation, curiosity, and a lot of bad early work to find. AI-assisted creation is in that experimentation phase right now. The people who will find the balance point are the ones willing to practice in the open.
How to Practice Without Publishing Slop
If you are early in using AI for creative work, the advice to “just do it a lot” needs some guardrails. Practice should not mean flooding the internet with weak drafts. Here is how I think about building the skill responsibly.
Start where you are already an expert. Use AI first in a subject you know cold. Your knowledge acts as a built-in fact checker. You will catch the model’s errors quickly, and you will learn its failure patterns faster because you can see them. Using AI in a field you do not understand is how people publish confident mistakes.
Verify against primary sources. When a draft cites a release date, a feature, a quote, or a number, check it against the original source. That means the official documentation, the project’s release notes, the dictionary entry, or the article that first reported the claim. Secondhand summaries repeat each other’s errors, and models blend them together. A habit of going to the original catches most factual mistakes before they reach a reader, and it gets faster as you learn which sources hold up.
Write your notes before you prompt. Spend 15 minutes writing down what you actually think about the topic. Your opinions, your examples, the arguments you want to make, the mistakes you see other people making. Feed those notes to the model. The difference in output quality between “write about X” and “here are my notes on X, shape them into an article” is enormous.
Keep a running list of what goes wrong. Every time a model makes a mistake you catch, write it down. Wrong dates, invented quotes, repeated phrases, weak openings. After a few dozen pieces, that list becomes your review checklist. It tells you exactly where to look, which is how review gets faster over time.
Turn your style into rules. If you notice yourself fixing the same pattern over and over, write a rule for it and give the rule to the model up front. Then check the rule with a script where you can. My own banned-word list and verification script are examples of this.
Publish less than you generate. The ability to produce a lot does not mean you should publish a lot. Generate ten drafts, publish the one that says something. Volume is the defining trait of slop. Selectivity is the defining trait of craft.
Be open about your process. Hiding AI use creates a trust problem when people find out. Being open about it lets you get honest feedback on the work itself. I talk about how I use AI because the feedback makes me better, and because I think the conversation about these tools needs more practitioners sharing real workflows.
Take criticism of the work seriously and criticism of the tool lightly. When someone says a claim is wrong or a section is weak, that is gold. Fix it. When someone says “AI slop” with no further detail, there is usually nothing to act on. Check whether your piece hits any of the three slop markers. If it does not, move on.
Judge the Work, Not the Workflow
So how should we judge creative work now that AI is part of so many workflows? The same way we judged it before. Look at the output and ask the questions that always mattered.
Is it accurate? Does it state true things? Are its sources real? Do its numbers hold up? A piece full of errors fails this test whether a human or a model introduced the errors.
Does it add value? Did you learn something, feel something, or see something in a new way? Content that leaves you exactly where you started has failed, no matter how it was made.
Does it have a point of view? Can you tell what the creator thinks? Is there a specific argument, a specific style, or a specific vision behind it? Work with a clear voice behind it is almost never slop.
Was it cared for? Do the details hold together? Are the rough edges sanded down? Does it feel finished? Care shows up in the work, and its absence shows up too.
Notice that none of these questions asks what tools the creator used. That is deliberate. We do not grade a novel by whether the author used a typewriter or a laptop. We do not grade a song by whether the drums came from a kit or a drum machine. We do not grade a photo by whether it passed through editing software. We grade the result.
These questions also work in both directions. They catch slop made with AI, and they catch lazy work made without it. A handwritten article full of errors fails the accuracy test. A fully human song with nothing to say fails the value test. The standard stays the same no matter which tools sit in the workflow.
AI-assisted work deserves the same standard. Hold it to a high bar. Call out errors. Call out empty content. Call out work that clearly never got a human review. Those criticisms help everyone.
But when a piece is accurate, valuable, distinctive, and carefully made, the presence of AI in the workflow counts as a production detail, the same way the choice of camera or DAW is a production detail. Something that adds real value, AI or not, still required skill, vision, and determination to make.
Conclusion
Slop is real, and I share the frustration with it. Content that misinforms, adds nothing, and skips review makes the internet worse, and people are right to call it out. The dictionary even gave us a word for it, and the definition puts low quality at the center.
But the word has drifted. Too many people now use “slop” to mean “made with AI,” and that shift punishes the wrong people. It discourages beginners from practicing a skill that only improves with practice. It pressures experienced creators to hide their process instead of sharing what they learned. And it confuses a judgment about a workflow with a judgment about the work.
My own path has been simple. I learn whatever tool lets me make the things I picture in my head. Photoshop, Videoleap, FL Studio, and now a range of AI tools. Each one made me more versatile. I learn in public and make my mistakes in public on purpose, because public mistakes bring the fastest lessons, and I am proud of what that approach has let me build.
If you want to criticize AI-assisted work, criticize it on the merits. Point to the wrong fact, the empty argument, the missing review. If you want to make AI-assisted work, put yourself into it. Bring your angle, your notes, your judgment, and your willingness to throw away drafts that do not meet the bar.
Judge creators by the quality and value of what they make, the way we judge creators in every other medium. That standard worked for photography, for synthesizers, and for the bedroom producer with a laptop and a DAW. It will work for this too.
Keep Going
If this piece was useful, I have written a lot more on how AI is changing the way people work. My book on AI and labor economics covers the economic side of this shift, and you can find it at a.co/d/06SeOKw8. You can find every book I have written, across lakehouse architecture, Apache Iceberg, Apache Polaris, and AI, at books.alexmerced.com.
