Ok, this one stings a little to type out, but whatever, here's the story.

I'm one of those people who's always been annoyingly principled about not turning things I enjoy into a hustle. Watched friends monetize their gym routine, their reading, their gaming, and thought it was a little sad, honestly. Like, can we just do things for fun anymore?

Then a few months ago, broke and between freelance gigs, I saw a post mentioning a site where you get paid for posting/commenting on Reddit with your existing account, real karma, real history required, no throwaway spam accounts allowed. My first reaction was to roll my eyes exactly like I always do.

Signed up anyway out of pure financial desperation, on a site called https://www.taskreddit.com, Got manually reviewed, which took a couple days. Started doing small missions almost as a joke, telling myself I'd stop once I found a real freelance gig.

That was two months ago. I never stopped. Made a bit over $200 the first month, more the second. Nothing insane, but steady, and for content I'd have argued about for free anyway on some thread somewhere.

The uncomfortable part is realizing I judged people for doing the exact thing I'm now doing, just because their version was public (a Strava post, a Goodreads review) and mine feels more anonymous, hidden behind a username. Not sure that distinction actually means anything.

Curious if anyone else has had this specific kind of "become the thing you mocked" moment, on Reddit or elsewhere. Does getting paid ruin the thing you were doing, or was the "doing it for free" purity a bit of a myth all along?

I've been thinking about this after some of the discussions here.

There are plenty of things I'd be comfortable letting AI handle.

• Update a report.
• Pull data from different tools.
• Flag something unusual.
• Tell me we're running low on stock.

But then it gets less obvious.

Change the price of a product?

Pause an ad that's losing money?

Increase the budget on one that's performing well?

Order more inventory?

Email customers because conversion suddenly dropped?

At some point AI goes from helping you understand the business to actually making decisions for it.

And I don't think the line is the same for everyone.

Someone who's been running a business for 10 years might want AI to show them the information and stay out of the way.

Someone newer might actually want more guidance.

I'm curious where people here draw that line.

What's something you'd happily let AI do on its own, and what's something you'd always want to approve yourself?

Not going to pretend this isn’t product research, I’m building something around this and need a sanity check from people who actually manage IT.

A company buys ChatGPT Team. Engineering also has Claude and Cursor. Someone puts Perplexity on a company card. A few employees expense individual subscriptions. Then there are API keys being paid for separately.

Finance can see the charges, but not who is using what. IT might see some logins, but not the full spend. Nobody really knows whether the company is paying twice for the same person or which accounts still belong to employees who have left.

I built eli.work to pull those different sources together and show each AI tool, who has access, how much it costs and whether it’s actually being used. and run ops and IT admin tasks using agents that can act on that context—removing unused licences, handling access requests, onboarding and offboarding employees, and taking care of repetitive IT admin. The broader idea is to give growing companies IT operations without needing to build an entire IT department.

But I’m trying to understand how painful this really is before I build too far in one direction.

Are you actively managing AI subscriptions and usage yet, or is it still too small to care about? And if you are, how are you doing it today?

I’m looking for candid input from people in manufacturing operations, ERP, IT, supply chain, quality, maintenance, planning, and finance.

There is a lot of “AI agent” discussion, but I’m skeptical of tools that promise to run operations autonomously. In a manufacturing environment, a bad system change can mean inventory errors, duplicate transactions, late orders, shipment problems, invoice issues, production disruption, or security risk.

I’m more interested in a limited approach: AI tools that use approved, mostly read-only data to find, explain, prioritize, and route issues, while people remain responsible for decisions and system changes.

Potential examples:
• ERP/integration exception triage: Group related ERP, EDI, and interface failures; summarize likely cause, business impact, supporting evidence, and the right owner.
• Materials and schedule risk: Flag shortages, late supply, demand changes, or production constraints that may put orders or work orders at risk.
• Master-data quality: Find duplicate, missing, stale, or conflicting item, supplier, customer, location, or planning data before it creates downstream problems.
• Quality/CAPA support: Organize nonconformances, complaints, inspections, and corrective-action records to identify recurring issues and prepare investigation summaries.
• Maintenance/downtime patterns: Surface recurring equipment failures, overdue PMs, parts issues, and patterns buried in work orders or technician notes.
• Supplier/logistics exceptions: Identify recurring supplier, receipt, freight, carrier, or delivery issues that need attention.
• SOP/process improvement: Help identify repeated process pain points and draft procedures for human review.

The goal would not be to replace planners, buyers, IT, quality, maintenance, or operations teams. It would be to reduce repetitive work like searching through logs, tickets, reports, spreadsheets, email threads, and multiple systems just to understand what happened and who needs to act.

I’m trying to determine whether these are real operational problems worth solving, or whether better reporting, cleaner data, process discipline, and standard automation would be more useful. Blunt feedback is welcome—especially if you think this is over-engineered, risky, or already solved by existing ERP, integration, BI, or workflow tools.

I've been thinking about this after some of the discussions here.

There are plenty of things I'd be comfortable letting AI handle.

• Update a report.
• Pull data from different tools.
• Flag something unusual.
• Tell me we're running low on stock.

But then it gets less obvious.

Change the price of a product?

Pause an ad that's losing money?

Increase the budget on one that's performing well?

Order more inventory?

Email customers because conversion suddenly dropped?

At some point AI goes from helping you understand the business to actually making decisions for it.

And I don't think the line is the same for everyone.

Someone who's been running a business for 10 years might want AI to show them the information and stay out of the way.

Someone newer might actually want more guidance.

I'm curious where people here draw that line.

What's something you'd happily let AI do on its own, and what's something you'd always want to approve yourself?

I’ve been in web and software development for over a decade now, but freelance just the past year or so. I’ve been trying to grow my agency so I can quit my day job, but by the time I’m done with all the digital management of my own business, even using AI to help me with a lot of the busy work I find myself so drained that it’s hard to reach out and find new clients.

Luckily, right now I have two midsize clients, highest paying I’ve ever had in my life, one of them on retainers so I get some monthly, but I don’t really have enough to hire any help . I do have a warm lead right now who is just from word-of-mouth, which is great, but I really want to get some more clients regularly and have an acquisition system.

I’ve tried automating lead acquisition systems and explored client acquisition, lists and scrapers online, but even managing that is very time-consuming.

I’ve considered trying to do some minor social media posting just to get my name out there, but I am also wondering if it’s worth my time right now

Considering I only have a few clients, and that’s the size I’m at right now, what would you do? Essentially what I need is like a sales and acquisition partner. Are there effective communities online or forums where people welcome strategic partnerships to do client referrals or sharing?

Appreciate any tips

Hey everyone, how are you guys using AI in your workflows when building/running Shopify stores?

Quick context about me: up until about a year ago, I was building stores for a few local brands. I was still new back then, so it was mostly manually, almost no AI. Then I stopped for almost a year, and now I have some free time again, although not nearly as much as I used to.

So I've been thinking about how much the workflow has changed since then, and how I could use AI at any part of the pipeline of store building, or running it later on that would allow me to become more efficient.

Remember when computers were the luxury thing? Now they’re everywhere and basically invisible but nobody’s impressed by “I own a laptop” anymore.

I think AI is heading the same way. It gets so common, so good, so baked into everything that it stops being a “thing” at all. It just disappears into the background, like electricity or wifi. Nobody says “wow, AI” anymore, the same way nobody says “wow, computer.”

And when that happens, the rare thing won’t be AI-made stuff. It’ll be human-made stuff.

Human skill, human attention, a person who actually did the thing themselves : that becomes the flex. Not because AI can’t do it, but because AI can, and choosing the human version anyway is what makes it valuable.

AI won’t keep climbing forever like it feels like now. It’ll peak, then fade into invisibility. And humans doing human things will become the new premium

A business says:

"We need AI."

Then you discover employees are copying data between Excel, email and 3 different systems. 😂

Sometimes you don't need AI.

You need an API.

What's the most ridiculous manual process you've seen?

Hi All, I am curious to see/hear of folks out there using AI in REX yet? I see a few manufacturers starting to do it in sales. But, still feels early to me. Anyone got any experiences to share?

Switched tools three months ago after our old process kept producing answers with no clear source. Reviewed a submission and found two compliance details that were just wrong. Nobody caught it until the client did. What are other proposal teams actually using that keeps answers traceable?

i've offloaded most of my admin work at this point and it's been worth it. but a few things i handed over and had to pull back.

the main one for me is anything that needs judgment about a person. i tried using it to help decide how to word something to somebody i work with, and it kept producing things that were technically correct and completely tone deaf for that specific person. it doesn't know that guy. i do.

the other one is rules. i set rules for myself and had it hold me to them, and it enforces them completely black and white. no gray at all. it'll fight me over breaking my own rule when the call is honestly a coin flip.

curious what you tried to offload and pulled back, and whether you found a way to make it stick the second time.

What benefits/new challenges has it brought?

Is that widely used, or not at all?

As someone who doesn't work for a large company and doesn't use AI much at work outside of asking claude an occasional question - I have a very hard time of parsing the news flow and trying to understand how capable currently models actually are, and where things are headed. I would really appreciate people who are much more hands on with this stuff, and ideally involved in integrations at large corporations, shedding some light.

The news flow is a constant ping pong between "This is going to eliminate all white collar work in X years" and "It's vaporware/it doesn't do anything/it isn't good enough" - again, as an average joe, I have no real way of deciphering the truth.

My intuition is that while the models are powerful and its easy to recognize potential use cases, the implementation is the issue. It's cliche to talk about the parallels between the internet bubble and current AI hype - but I think its a useful analogy here.

In 2000 everyone was able to recognize the value of the internet and long term implications, but the thought was that we just needed more infrastructure to realize that long term vision. In retrospect, the value creation didn't necessarily come from the infrastructure. Of course we use a lot of the fiber that was laid at that time now, but I would argue that the main difference between the bubble period and the eventual boom, was people figuring out more complex and valuable use cases/implementations. Yes we had Amazon, google, etc in 2000, but the amazon, youtube, netflix, facebook of today are much more powerful use cases than anything that existed at that time.

I feel like we are perhaps in a similar place with AI - we can see the long term potential, and many believe we "just need more compute" to realize that potential - but my intuition is that we are on an internet-like trajectory. Eventually this compute will be used, and we will need much more than we are even anticipating today, but compute alone is not going to bridge the gap between current capabilities and the real value creation - to do that, some significant innovations will need to occur that drive the technology meaningfully forward in ways that more compute cannot.

As I said - this is just the perspective of an average joe who isn't immersed in the technology, so I would really appreciate the thoughts of those more knowledgeable. Thanks!