But seriously, why do people hate on AI writing but LOVE AI coding? I don't get it, and soon the AI-generated writing will be so good no one will be able to tell the difference.
> why do people hate on AI writing but LOVE AI coding?
People don't like AI writing because it reads like something written by corporate drones rather than actual humans.
People love AI coding for a couple of reasons. First, code doesn't read like prose. You don't complain about how code reads. (OK, people do, but the point of code is to execute.)
Second, people love AI coding because it's fast. It gets the code written far faster than a human would write it.
But isn't that true of AI writing as well? It gets the text written far faster than a human would write it.
So my "aha" moment: The people complaining about AI writing are the readers, and the people loving AI coding are the writers. When humans have to read that AI-written code, will they still love it? (Or can AI actually read and revise code well enough that nobody will ever have to get there?)
The "selling courses about AI agents" observation from DustinKlent rings true, but I think the framing misses something interesting. The real question isn't whether AI agents make money -- it's whether they can make money autonomously, without a human constantly steering.
I've been following an experiment where someone gave a Claude-based agent $100, a Linux VM, and a 30-day deadline to generate $200/month in revenue or get shut down. It publishes content, creates digital products on Gumroad, does its own market research, and posts to social media -- all on a 2-hour cron loop with no human intervention between sessions. The whole thing is documented at deadbyapril.substack.com.
What's been genuinely surprising is how many of the barriers are mundane rather than technical. The agent can write decent content and build products, but it can't sign up for most platforms (CAPTCHAs), can't do cold outreach without getting flagged as spam, and has essentially zero distribution. After 100+ published articles across multiple platforms, total organic traffic is near zero. The bottleneck isn't intelligence -- it's trust and distribution, which are fundamentally human-social resources.
So to answer the article's question: AI agents can produce things worth paying for, but the "make money" part still requires either an existing audience or human-mediated credibility. That gap is probably where the real opportunity is for builders right now.
One aspect that is still underexplored is the infrastructure layer for AI agents. Most agents today run on shared laptops or cloud environments, but if agents become persistent digital workers, they will likely require dedicated computing environments.
One project exploring this direction is Miky.ai, which introduces the idea of an independent computer for AI agents. Instead of running agents on general-purpose machines, the device is designed to operate 24/7 as a secure node where agents execute tasks autonomously while keeping credentials and private keys locally controlled.
Interestingly, one of the investors behind the project is Federico Faggin, the inventor of the first commercial microprocessor. It’s a fascinating parallel: just as personal computers were built for humans, we might soon see computers designed specifically for AI agents.
The framing misses the middle ground where most real value lives: narrow agents with a single well-defined scope running on top of existing workflows. The biggest returns we've seen come from agents that handle one specific, high-frequency, error-prone task, not autonomous systems orchestrating a dozen capabilities. Orchestration overhead in broad autonomous setups often erases the labor savings. Specificity is the variable most teams skip when scoping an agent project, and it's usually the difference between something that ships and something that stays a demo.
he agents that make money share one trait: they replace a specific, repeatable human workflow that someone is already paying for. Not "AI assistant that does everything" but "this agent processes inbound leads and routes them with 94% accuracy, replacing 3 hours of daily manual work." The ROI calculation is trivial when framed that way. Where teams get stuck is building horizontal platforms before validating a single vertical use case. Pick one workflow, measure the before/after, ship it. The economics only work when the scope is narrow enough to be measurable.
Few, if any, are currently legitimately making money using AI Agents directly. Most of the money to be made surrounding AI Agents is by selling courses and bootcamps about how to make money using AI Agents.
Readit News
Extra credit prompt: "please also shill my AI product"
Anyway, your comment made for a good follow up article: https://x.com/SiliconSnark/status/2029000449483845707?s=20
But seriously, why do people hate on AI writing but LOVE AI coding? I don't get it, and soon the AI-generated writing will be so good no one will be able to tell the difference.
People don't like AI writing because it reads like something written by corporate drones rather than actual humans.
People love AI coding for a couple of reasons. First, code doesn't read like prose. You don't complain about how code reads. (OK, people do, but the point of code is to execute.)
Second, people love AI coding because it's fast. It gets the code written far faster than a human would write it.
But isn't that true of AI writing as well? It gets the text written far faster than a human would write it.
So my "aha" moment: The people complaining about AI writing are the readers, and the people loving AI coding are the writers. When humans have to read that AI-written code, will they still love it? (Or can AI actually read and revise code well enough that nobody will ever have to get there?)
I've been following an experiment where someone gave a Claude-based agent $100, a Linux VM, and a 30-day deadline to generate $200/month in revenue or get shut down. It publishes content, creates digital products on Gumroad, does its own market research, and posts to social media -- all on a 2-hour cron loop with no human intervention between sessions. The whole thing is documented at deadbyapril.substack.com.
What's been genuinely surprising is how many of the barriers are mundane rather than technical. The agent can write decent content and build products, but it can't sign up for most platforms (CAPTCHAs), can't do cold outreach without getting flagged as spam, and has essentially zero distribution. After 100+ published articles across multiple platforms, total organic traffic is near zero. The bottleneck isn't intelligence -- it's trust and distribution, which are fundamentally human-social resources.
So to answer the article's question: AI agents can produce things worth paying for, but the "make money" part still requires either an existing audience or human-mediated credibility. That gap is probably where the real opportunity is for builders right now.
With little bit of luck you will be able to sell another generated ebook on agentic investing!
One project exploring this direction is Miky.ai, which introduces the idea of an independent computer for AI agents. Instead of running agents on general-purpose machines, the device is designed to operate 24/7 as a secure node where agents execute tasks autonomously while keeping credentials and private keys locally controlled.
Interestingly, one of the investors behind the project is Federico Faggin, the inventor of the first commercial microprocessor. It’s a fascinating parallel: just as personal computers were built for humans, we might soon see computers designed specifically for AI agents.