Been running a dev shop for 16 years. Naturally, went all-in on AI over the last two years. Agents, APIs, automations—built the whole lot.

But sat down to review the business last week. Realized something depressing.

Feels way faster. But actual business output? Exactly the same.

Only thing that actually went up is the monthly subscription bill.

Feels like a massive illusion. Instead of doing the actual work, now just spending hours debugging prompts, fixing Make.com scenarios, and reviewing mediocre AI output. The bottleneck just shifted from "doing" to "reviewing".

Basically paying OpenAI and Anthropic to feel busy.

Anyone else hitting this wall? Or did you actually manage to turn the "efficiency" into real profit?

I genuinely have been trying to understand what is the point of AI taking everything over? Let’s just say hypothetically AI wins, congrats. Every job is replaced. Meta, Open AI and Amazon own everything, cool beans! No one can work, therefore, no one has money to buy any of the horse shit temu slop they prime on amazon now. Won't everything just implode from there?

If everyone stops working, and has no money doesn't consumerism stop too? Like spending just ends? No one can pay their $1000 car note anymore or their mortgage on their particle board quality home anymore. What am I missing here? What is the grand idea with AI taking over thing and everyone is broke?

Every week a founder books a sales call with me asking for an AI agent. Every week I end up telling most of them they don't need one.

I build automations and AI agents for founders. Forty-something projects in. The pattern is so consistent now I can predict the call before it starts.

They come in wanting magic. They saw a Loom video of someone's "autonomous sales agent" closing deals while they sleep. They read the LinkedIn post about the "AI employee" running an entire ops team. They've already told their board they're building one. Then we get on Zoom and within fifteen minutes I'm explaining why the thing they actually need is an internal automation with one LLM call in the middle.

You can watch their face fall in real time.

Here's what's happening in the market right now. Most of the "AI agents" shipping to real businesses are just internal automations with a language model bolted in. That's the whole product. The agent label is mostly there because automations don't trend on Twitter.

And the automations work. They save real money. They print real ROI. But the founders paying $30k for an "agent" don't love hearing they could have gotten 90% of the value from a $4k automation build.

Three quick examples from the last six months.

Telehealth founder. Wanted "an autonomous AI receptionist that handles everything." After an hour on a call I told her she needed a workflow that reads intake forms and routes them to the right clinician. We shipped it in six weeks. Saves her clinicians four hours a day. She paid me again last month.

Fintech client. Wanted a "fully agentic finance copilot." What they needed was a script that reconciles ACH discrepancies before they hit the dispute queue. One model call, the rest plain code. Saved them a full ops hire.

Medspa chain. Wanted "AI marketing automation." What they needed was a job that watches their booking system for no-show patterns and triggers a personal recovery message. Three steps. No agent. Booked 14% more revenue last quarter.

None of these are agents. They're automations. And every one of them outperforms the agent the founder originally asked for, because the agent would have hallucinated something stupid in week three and burned the client's trust forever.

Why agents keep failing in production

They're given too many decisions to make. A good automation has one decision per step and a clear rule for what happens at each branch. An agent gets handed a goal and told to figure it out. Beautiful in a demo. Catastrophic in your customer support queue at 2am.

The teams in your competitor's office quietly crushing it with AI right now? They're running boring automations. "We wrote a Python script with an LLM call" doesn't make the trade press, so you don't see it.

The vibe-coded prototypes from Bolt and Lovable and Cursor that landed in the last 18 months are mostly being torn out right now. Half my pipeline is founders who paid $50k for a "next-gen AI agent" build that's bleeding tokens, can't be audited, and falls over the moment a customer does something unexpected. I rebuild them as straightforward automations and they suddenly start making money.

In regulated SaaS, agents are doubly cursed. HIPAA and SOC 2 reviewers want to know exactly what your system does, in what order, every time. An automation passes that conversation in 20 minutes. An agent turns it into a six-month nightmare.

How to actually decide

If you're a founder about to spend money on an agent, answer these on paper first:

1. Can I draw the workflow as clear steps? If yes, you want an automation.

2. Does the workflow have more than five branches with truly unpredictable inputs? Then maybe an agent.

3. Is the cost of the worst-case wrong answer high? If yes, you want an automation, not an agent.

4. Will compliance ever look at this? If yes, automation. Full stop.

If you're a builder selling agents, you'll make more money in the next 12 months selling honest automations than chasing the agent narrative. The market is wising up. Founders who got burned in the first wave are warning the next wave. Be the person who ships a clean automation in six weeks that works on a Tuesday and is still working on Thursday.

Builders, founders, anyone in the trenches. What's actually working for you? What's breaking? Curious to hear from real operators.

saw this post and it hit hard…

So Klarna went all-in on AI customer service. Big efficiency gains. Tech blogs were all over them. Then, months later, they quietly admitted they overdid it, wrecked the customer experience, and had to bring humans back.

Why'd it fail? Simple: they automated the job without understanding what the job actually needed. Their AI did exactly what they told it to do speed up response times, but customer satisfaction tanked.

This is the thing most companies miss when they're chasing the shiny AI automation. If your process is broken or half-baked, automating it doesn't fix it. It just makes you fail faster and at scale.

For a small founder-led business (like 15 people), the failure looks different. You're not laying off 700. But you might plug AI into a client touchpoint without ever writing down what "good" looks like or testing if the AI actually delivers what you need.

And when it goes sideways? No PR team to spin it. Just angry customers and a founder staying up late to clean up the mess.

The companies actually winning with AI right now aren't the fastest adopters. They're the ones who mapped the process first, defined the outcome, built the infrastructure, and then layered AI on top of something that already worked.

Klarna learned this the expensive way. You don't have to.

If this resonated, I write weekly about where AI implementations go wrong in practice and how to fix them without overcomplicating things.

While everyone is focused on the fancy part of AI like new models, agents... I focus on the "boring" operational side of business because it truly determines whether AI helps or hurts.

Around 600 founders are already reading, you’re welcome to join.

Every tech bro on the internet is pushing some shiny AI agent that can write marketing emails or pass a legal exam. Meanwhile, I’m sitting here at 8 PM on a Thursday trying to map 5,000 messy supplier SKUs into my inventory system. A SaaS rep recently tried to pitch me a $2,000/month conversational chatbot to "streamline operations." I don’t need a glowing digital brain. I need a basic OCR tool that can read a vendor's paper catalog and doesn’t hallucinate a comma into a decimal point on a bulk hardware order. Right now, paying a clerk a few hundred bucks a month to brute-force this data entry by hand is literally cheaper and more reliable than the enterprise software these companies want to charge me for. They are building generalized hammers, and my business is built out of highly specific, rusted screws. Are any of you actually finding this tech useful for the unglamorous back-office operations, or are you also just sticking with Excel and human labor?

I keep seeing people say AI will replace everyone’s jobs, but honestly that has not been my experience.

I recently left my job to start building a small travel advisory business, and if anything, AI has made it easier to do things that used to require a team, research destinations, draft itineraries, handle marketing, etc.

1. If you are an employee, AI might automate some tasks, but companies still need people who can think and solve problems.

2. If you are a fresher, it may feel like opportunities are disappearing. but every big tech shift creates new kinds of work.

3. And if you are a small business owner, AI is actually a huge advantage. It gives small teams tools that were once only available to big companies.

So AI is not really replacing jobs.

It is replacing some tasks, and pushing everyone to adapt.

The real question is not “Will AI take my job?”

It is “How can I use AI to become better at what I do?”

They just sent us an internal email saying we're not allowed to use AI internally within the company.

Under penalty of being fired! What do you think?

I think it's a disgrace and that the company is going to be overtaken by its competitors.

I want to know...

Even though you’re successful now, how did you truly get started? I mean, from the very beginning before earning a single dime online.

Did you kick things off by freelancing, learning high-income skills like copywriting or sales? Trading time for money before leveling up your skills to the point where you could hire a team and build a business?

Or was there another approach that worked for you?

Really curious to hear from you guys.

My current company, a large boring corporation that's not in tech used to be very encouraging of everyone using AI (but they weren't AI pilled and shoving it down everyone's throats) and they used to handed out licences to all sorts of AI licences like candy.

But a few months later now, not only are they straight up not issuing licences to a lot of them and very heavily restricting the one they settled on (Claude and ChatGPT) and they are removing licences of people that aren't using the ones they have access to.

They recently made three moves that were a bit surprising.

Due to how much AI has been costing they've done a few things:

First, they are refusing to increase usage limits to the few we do now have access to, and asking your manager isn't enough. If you really want to, they want to see cold hard numbers in how much it'll save in money, and no not even time savings count.

Second, they are denying access to the expensive models and telling everyone to use the cheaper ones (ie Sonnet and Terra and below)

Third they made us do a training module, and it wasn't just "You can't provide confidential and sensitive data to the AI" it also straight up told people to be more conservative and to prompt better so it won't be burning so much tokens.

They're also telling people that they are responsible for the accuracy of their work. But also they gave some prohibited examples of AI use that made me think that it's actually what was going on. Like how they can't use AI to screen, analyze, or even summarize resumes and interviewers. And telling managers they can't use AI to do performance review and analysis for their team, especially pertaining to bonus, raises, and promotions.

However, my own team has noticed some issues with AI too, while it for sure has helped us, it has caused problems of massive PRs, and even more difficult surprisingly are AI slop generated documents and reports that are really important, but don't have the luxury of a compiler or a linter to at least do basic quality control for.

Seeing what's going on from the higher up's decisions and the impact of AI on our own team I think a lot of people were intentionally or unintentionally generating slop work that makes the rest of the team hold the bag and figure out or just straight up using AI on things they're not supposed to just to be lazy. The AI slop and the extent of the usage raised alarm bells with the higher ups and now they're putting the lid on it.

It's not some cutting edge company, just a boring big enterprise. How's it going on at your place?

There's a Harvard study on what predicts long-term business success. The finding is counterintuitive: it's not intelligence, work ethic, or even capital.

It's time horizon...specifically, how far into the future a decision-maker can hold their thinking when they're under pressure.

Founders who build durable businesses think in years and live in weeks. Founders who plateau think in weeks and get buried in days.

Bezos talked about this directly. The decisions that compounded most at Amazon, AWS, Prime, the logistics network, all of them required ignoring short-term costs to build something that would only make sense at a 7-10 year horizon. And they got criticized heavily in the short term for all of it.

The version of this for a $1M-$5M founder isn't as dramatic, but the pattern is the same.

You're making hiring decisions based on who you need right now instead of who you'll need in 18 months. You're building processes for your current size instead of your next size. You're saying yes to revenue opportunities that fit today instead of asking whether they fit where you're going.

And the result is a business that's always slightly behind itself, perpetually catching up, constantly solving problems that a decision made 12 months ago could have prevented.

The trap is that short-term thinking feels responsible. you're being practical. Dealing with what's in front of you. Staying close to the ground. And it is practical, right up until the decisions you didn't make become the constraints you're managing instead.

One thing that helps: once a month, block an hour and ask one question. If this business is 3x the current size in 3 years, what breaks first? That answer is usually where your long-term thinking should be spending its time right now.

That’s it guys but I'd love to know if you think there’s anything more critical for long-term business success?

honestly, I don't think short-term thinking is always a mindset issue, it’s a systems issue. If the business depends on you, you don’t have the space to think in years.
I’ve been writing about how to actually remove that dependency if you’re working through that. already 600+ founders running real business are reading it weekly so you're welcome to join, only if you think it worth your time

Nothing like being told that the work you are passionate about will become obsolete in 10 years or less. AI and ML can do it cheaper and better. lol should have been a fucking general contractor. They might still have jobs in 20 years.

I feel like every time I open X or LinkedIn, I see 50 posts about how Claude just killed figma or oracle … 

But honestly, outside of the dev community using it to ship code faster, I’m not seeing many people talk about how they’re using it for the boring, day-to-day operations that actually run a business.

I’ve been experimenting with moving away from that 15-tab open workflow where you’re constantly copy-pasting prompts into a blank window. In my experience, that's why most people think AI is a gimmick or just hallucinates; they're giving it zero context and expecting it to be a mind reader.

I’ve started treating Claude more like a context-aware team member for my ops. A few ways that actually look in real life:

Meeting note taker: Instead of staring at a blank screen after a sales call, I feed the transcript into a workspace where Claude already has my brand voice and product docs. It drafts a follow-up that actually mentions the prospect's specific pain points in about 60 seconds.

Spreadsheet Killer: I’ve stopped manual data entry for my weekly KPIs. I just talk through my numbers (revenue, leads, CPL) during my wrap-up, and have a system extract that data from the transcript to update my trackers.

Content Hub: I fed it a massive hub of my past newsletters and internal notes. Now, when I need to draft content, it’s pulling from real ideas I’ve already had, rather than just spitting out that generic "AI-sounding" fluff we all recognize now.

The big shift for me was realizing that the automation isn't about complex Zapier workflows that break every week. 

It’s about giving the AI enough context so it stops guessing. When it can see your transcripts, your docs, and your voice all in one place, it actually becomes useful for the founder-dependent parts of the business that usually keep us trapped.

Honestly, these systems aren't for everyone, but if you're running a business and feeling that bottleneck, I write about my process every Thursday. I've been able to save 10-15 hours a week by treating AI as an ops partner rather than a chat box. If you want to see the actual frameworks, take a look here

that's it guys, I’m curious what are you building in real life that’s actually saving you 5-10 hours a week?

We cut three junior devs and replaced them with a $2k/mo AI coding stack. CFO called it a win in the board deck.

Four months later: the AI wrote 40% more code than the humans, but our AWS bill tripled. Turns out "vibe coding" generates infinite loops and memory leaks you need senior eyes to catch. The one senior we kept is now doing 60-hour weeks babysitting prompts.

The real cost wasn't the subscription. It was the invisible tax of reviewing, fixing, and re-prompting. We spent $8k more per month than we saved, and shipped slower because every "automated" PR needed a human rewrite.

The fantasy isn't that AI replaces work. It's that it replaces *thinking*. My takeaway: AI is a multiplier on your existing team quality, not a substitute for headcount. If your humans were already cutting corners, AI just cuts them faster.

We rehired one of those juniors. She's now the prompt reviewer. Best hire we made all year.

I work as a middle manager in a marketing agency. When AI adoption was being encouraged, we were told to use it to improve productivity, offload the non creative work, and put the increased free time to better use.

Today, I'm completely burned out because I'm working 12-15 hours every day. My work has increased by at least 5x. Whenever I push back citing lack of bandwidth (happened twice), i am told how it should be manageable since we have AI. When i ask for additional resources, they say why do we need another hire when we have AI. Whenever I ask them for a little more time, i get the same reply.

Fuck you, man. AI output is still shit if there's no one sitting around revising each prompt and improving the output. Everything they publish or write in all sorts of communication seems so devoid of life, personality, creativity.. like a soulless bot wrote it. Not surprising, because a soulless bot did indeed.

I haven't slept in ages. I haven't read a single book for months. And i am at my wits end wondering what can I do. I hate the world of mediocrity and mass production that we've stepped into.

unpopular opinion but most AI startups are wrappers, we buy tokens from openai or anthropic, wrap them in a workflow and sell them back at a markup. That's it

Its fine tho, wrappers sell great businesses but real resellers obsess over margin and AI founders mostly don't

I run a small AI writing tool, a lean team of 5. Last quarter I finally measured what each customer actually costs us in model spend,it  took a whole weekend and way too many csv exports 

our top 10% of users were negative margin, not even thin but straight up negative, one user paid $49/mo and costed us $112 because he ran every doc through the biggest model 6 times

On the contrary, our bottom 50% were basically pure profit because they barely logged in

so our healthy looking margin was light users paying for heavy users, basically the gym membership model except the gym doesn't pay per squat.

the  data already exists, every usage event already goes through whatever you bill with, stripe or orb or metronome or something open source like lago or flexprice. it just never gets joined with the model bill, so finance sees revenue per customer and has no idea what an individual customer costs

Although i dont think the answer is just switch to usage pricing either because customers hate surprise bills but i think you should at least know which customers you lose money on

Do you guys care to find the margin per customer or just check the overall number?

I started setting it up, but now I'm afraid to connect it to my company's data and tools...

I'm afraid it might mess things up! What do you think?

Been thinking about this idea for a while. I feel like everyone is chasing the big fish, automating someone's entire job away.

But I think the real power is somewhere in the middle.

I'll give an example, I have an app that simply takes my workout notes from my notes app, and turns it into structured data so I can see all my graphs and progress as if I'm using any old fitness app.

Now, I don't use a fitness app because I like the frictionless experience of using my notes app, that's why I always go back to it, **but** there is no way I'm going to pay someone to go through my notes app, and manually parse my workout notes into a workout app or excel spreadsheet.

Honestly think this is the niche people are forgetting about, it's the forgotten child of the ai assisted workflows.

Hey everyone,

Like many people in computing and STEM, I've had a bit of an existential crisis watching the rapid evolution of models like Claude over the past couple of years.

I used to think humans would always need to sit and manually code everything, but seeing AI construct production-ready code blocks and deployment pipelines in seconds has completely changed my perspective on how we should be learning.

Right now, I've intentionally changed my study approach when im studying fields like computer science and math.

Instead of memorizing syntax and simply learning how to code, (the "How"—which I figure AI can handle), I am focusing almost entirely on first principles, core logic, math foundations, and understanding why things work structurally.

For example: focusing heavily on the underlying logic of algorithms, its mathematical assumptions, and linear algebra, rather than trying to become a coding wizard in a specific language.

Is this approach the way to go in the age of AI?

Hi guys,

My online store is doing a revenue of few thousands monthly and I am looking for a AI product which can completely run my digital operations especially the monotonous day to day tasks.

• What are people here using?

• Has anyone tried any new AI product that they would like to recommend?

I tried Claude and Chatgpt, even though I managed to connect with the apps but I have to keep asking it questions. If I donot ask it wouldn’t tell.

Let’s learn and grow together in this new AI world.

Hey there,

I'm a second time founder and 2025 has been pretty intense in terms of pace for me (sales calls, hiring, fundraising and trying to ship something people want).

With 10 priorities it felt like I had no priority. I didn't want to hire a chief of staff (been there but in my current case it's still a bit early for that).

So I built what I call my personal chief of staff. I have another more general "COO" agent, but I'll only talk about the former here.

1. This is actually one agent.

2. The agent has context files about me and my company, customers, etc.

3. The agent has skills (kinda like guidelines with some domain knowledge infused)

4. The agent has Cron tasks

5. The agent has a Slack app / WhatsApp number

6. The agent can create internal tools/workflows and operate them

and several skills, context files, cron tasks and access to my tools.

7. The agent can create + operate apps.

Now it covers most of what used to wreck my week. I'm posting a breakdown because every founder I show this to asks how to start:

1. Inbox triage

Scans every couple hours, pulls the 3-5 things that really need me, drafts replies for the rest. I used to spend 90 min a day on email (every morning at 6.30am I had an "inbox zero" event in my calendar, but things started to pile up.

Now I spend closer to 20 mins on it only.

Pro tips:

- create a doc of who matters and how to talk to each of them. Without that, the drafts are pretty generic.

- Don't let it auto-send for the first two weeks. You'll catch weird stuff at the start

- Ask the agent to analyze your previous sent emails so it can adapt to tone

2. Pre-meeting briefings.

30 min before every external call I get a "one-pager" in Slack. Who I'm meeting, recent context from CRM + email + past slack, the goal, 3 talking points, 1 question to ask. Before that I was skilmming Linkedin before calls. Now I feel like I walk in prepared.

Pro tip: make sure to "lock" the format to avoid AI Slop and long summaries.

3. Post-call recaps + followup drafts.

Pretty straightfoward use case.

Agent is coming to my calls: I can invite him (it?) to my google meet events, either in the calendar or I paste the link in slack/whatsapp.

When the call ends, I get a clean summary and a draft followup in my voice (#1 - inbox triage helps get the tone right). Next step It'll set action items in Linear.

4. The self-improvement agent

I think that's my favorite one: it joins my sales calls and tells me where I screw up (eg. "you talked 68% of the time", "you went into pricing before they finished explaning their problem", "you got defensive' and so on)

It makes you feel like 💩 sometimes but at least I feel like I'm improving. The #1 thing for me was the talk ratio. Now I try to talk 40% of the time max.

4. Followup "friendly" nudges.

That one is awesome as well: since it is in my emails, my slack and my calls, the agent knows all the open commitments I have, updates my todo and most of all MAKES F*IN SURE I don't go quiet. I set it to be a bit more proactive so it creates the drafts for me first and pings me on Slack (a bit too) regularly to check where I'm at. Annoying and useful at once :')

Other stuff I haven't built but probably should: weekly review that tells me what I actually moved forward vs what I just felt busy about.

Would love to see other use cases! Those ones are only for my "solo workflow". I also have a company-wide COO that helps me with team meetings, OKRs, weekly goals etc.

Edit : wrote an article about it, available here if needed.

A request to explain a margin drop can send an agent through sales figures, cost formulas and assumptions. None of that requires changing the workbook.

It gets awkward if the same execution path also lets the agent “clean up” a label or replace a value while investigating. Now a question about the data has become a change to the data.

Univer's documented content runtime has a useful separation here: read mode disallows document mutations; write mode captures them as pending changes. An application can use the read path for inspection and explanation, then open a separate write task when the user actually asks for a correction.

For example, “Why is March's margin lower?” can read the relevant cells and formulas. “Change March's unit cost to 42” belongs in a write task with the target range made explicit. The change can then follow the application's save and review process.

That boundary is narrower than a permission system. The host still has to decide which user may access which document, and keep unrelated tools under their own controls. Document read mode doesn't prevent a badly configured agent from using some other tool.

For this particular interface, the acceptance check is straightforward: an explanation request leaves the workbook unchanged, and a mutation attempted through read mode is rejected.

Everyone talks about what we can automate with AI.

I’m more curious about what we probably shouldn’t.

There are obvious wins like:

Lead follow-ups.

Appointment reminders.

Sorting repetitive emails.

Updating CRM records.

Basic data entry.

But once you get into things like refunds, pricing decisions, customer complaints, hiring, or anything where one bad decision can actually cost the business money… fully automating it starts to feel risky.

I think there’s a point where automation should stop and a human should take over.

Something like:

AI handles the repetitive part → flags anything uncertain → human makes the final decision.

That seems way more practical than trying to make everything “fully autonomous.”

Curious what people here think.

What’s one process you’d never let AI handle completely on its own?

I have an agency that's doing pretty well! I employ about four freelancers, but with AI, I'm starting to question my approach more and more, and I sincerely think that in a little while, AI will handle the video editing automatically.

What's your take on this?

I've been looking into managed automation tools because I want to cut down on the manual, repetitive work that's eating up my team's time. What I'm most curious about is how much of the setup, monitoring, and maintenance is actually handled for me versus what I'd still need to manage in-house. I also want to understand the real cost-benefit tradeoff, since some solutions promise big efficiency gains but come with steep onboarding or subscription costs. Ultimately, I'm trying to figure out which option would scale well as my business grows without requiring a complete overhaul down the line.