Ferenc @ferenc

Fake engagement is about to cost you a lot more than a subscription fee. AI search engines now cite LinkedIn as a primary source of professional expertise, which means the platform has a massive incentive to wipe out automated comment loops. When the algorithm flags your coordinated pod behavior as inauthentic, your distribution will suffer the consequences. Discover how platforms are tracking these patterns and what actually survives the upcoming purge. ➡️ bossfeed.io/blog/linkedin-… bossfeed.io/blog/linkedin-…

Developers complaining about AI-generated code usually blame the prompt. I blame the documentation. Before AI touches any project I work on, I write architecture decision records, auth patterns, data model specs. Months of work upfront. That's what teaches the AI how we build. Skip it and the output looks clean until someone opens the file. The prompt is the finishing move. The documentation is what makes it land.

I've reviewed landing pages where the value prop was technically accurate and nobody converted. And better copywriting didn’t fix it A calculator did. "How much do you want to spend? £100. You get £3 back." One interaction that answered what three paragraphs couldn't.

I heard something in a meeting last week that I've been thinking about since: A lawyer who uses ChatGPT to produce near-final briefs. Not because AI understands law. Because she does. She knows what makes a brief hold up. She prompts from that knowledge. She catches the gaps the model leaves. I've seen the same dynamic on the development side. The output is only as good as the expertise you bring to it. AI gives a bigger return on existing knowledge than it does on the absence of it.

A client I work with built a genuine cashback product with real differentiation and good unit economics. Then they spent two years not mentioning cashback anywhere on the site. The thinking: lead with gift cards, which people already understand. Don't complicate it with how the rewards model works. It backfired. They looked like every other gift card site on the internet. What they learned: Stop explaining the thing that makes you different and you become whatever the customer assumed you were.

The most important design decision in an AI agent isn't what it handles. It's what it refuses to handle. I met a founder this week building an AI concierge for hotels. It handles inbound questions, collects guest feedback, upsells extras. Works well for the routine stuff. The moment a guest raises a real complaint, it cold-transfers to a human. Immediately. He built the escalation path before he built anything else. Because one wrong call from an AI agent costs more trust than the feature was worth.

AI is making it easier to build an app. It's not making it easier to build a business. I sat in on a call this week with a founder who showed me a working product built over a weekend. Clean interface, real features, no users in the three weeks since it launched. The build was never the bottleneck. Finding someone who actually pays, figuring out the price, and checking whether the number is enough to run a company on - that's where most of this falls apart.

It took me a few years in SaaS consulting to learn this: "Build for outcomes, not tools" is the right destination. But: Even obsessing over business outcomes gets you nowhere if you can't name the one metric your product is uniquely positioned to move - that nobody else is measuring yet. There's something specific in that gap. It's where positioning becomes a moat. Simply because most founders never stop long enough to find it. Building for outcomes without that specificity is consulting with worse margins.

The longer I work with AI-powered SaaS founders, the more I realize: Your pricing model isn't fully yours. It's downstream of infrastructure decisions made by companies you've never sat across from. For example: OpenAI's "Jalapeño" chip - built with Broadcom, deploying before the end of 2026, reportedly 50% cheaper than GPU inference - just rewrote the cost assumptions underneath every AI product priced in the last 18 months. Most founders modeled their margins on today's inference costs. Those costs are now someone else's competitive lever.

Ideas are officially worthless. Anyone can ship an app in a weekend now. The market is flooding with AI-built apps that look like products but aren't businesses. Building was never the moat. It was just expensive enough to look like one. The real filter: Not "do you like it.

Survivorship bias threatens your SaaS strategy. Saw two viral Reddit threads - one celebrating vibe-coded SaaS to $10k MRR, another breaking down 7 months of the exact same approach with "what nobody tells you." Both experiences are real. Neither represents the full distribution - only the founders still posting. The winners kept going and kept sharing. Everyone else quietly stopped. Founders aren't benchmarking against everyone who tried. They're benchmarking against everyone who survived.

AI made writing free. Now nobody can afford to read. We built an internal knowledge base with AI. Standards, decisions, best practices. Impressive volume in weeks. Then it stalled. "Review 10 of these by next week" killed it. Nobody came prepared. The meeting collapsed. The content wasn't the problem. The process was. AI removed the old bottleneck: creation. It created a new one: human review. If you don't redesign your workflow around review capacity, AI output isn't a knowledge base. It's a landfill with a search bar.

The niche that sounds too specific to matter is where the best exits happen. A company just sold for nearly a billion dollars disrupting 911 call centers. Nobody was building for that in 2021. Horizontal AI tools are racing for the same $5B TAMs. Vertical builders are quietly owning $200M markets with no competition and enterprise contracts that never churn. The moat isn't technology. It's the fact that nobody else bothered.

"We're splitting the budget and moving half to next year." Every agency founder gets this message. Most misread it as good news. It's not. A split budget is a downgrade dressed as a plan. Budgets don't restructure themselves. Someone made a decision, and the decision was: this project matters less than it did. 3 things I do when this signal arrives: 1. Ask what changed in their business, not in the budget. The budget is the symptom. Priority moves happen first, budget moves follow. If you don't know the reason, you can't fix it. 2. Treat the confirmed half as the whole project. Scope something complete within it. A half-finished deliverable makes the second half easy to cancel. A finished one makes it easy to fund. 3. Update your own forecast immediately. Money that moves once moves twice. The vendors who get burned are the ones who book deferred budget as future revenue. Deferred budget is not revenue. It's an option the client holds. Your job is to make exercising it obvious.

Crazy how often founders tell me their AI agent is live and assume that means it's done. I find this costly. First, live and reliable are not the same milestone. LangChain's State of Agent Engineering 2026: 57% of organizations have agents in production. 1 in 3 say quality - not cost - is their biggest blocker. Second, it misses what enterprise clients actually audit: not whether the agent runs, but whether there are approval steps, an audit trail, and a rollback for when it acts on bad data. Shipping is the easy part. Trust is the product.

If a client's exit surprises you 2 weeks out, your contract is broken. Not your relationship. Our rule for retainers: 60 days out: you should sense if it continues. 30 days out: it must be in writing. Don't rely on people asking brave questions. Hope is a comfortable default. Put the question in the contract. Then nobody has to be brave.

The first enterprise client who sends a security questionnaire will ask about audit trails. Founders usually find this out in the meeting, not before it. An audit trail in a B2B SaaS is a specific answer to a specific question: When an action happened in this product, who triggered it, what data was touched, what changed, and can it be reversed? For products where AI takes actions (filing a document, issuing a refund, routing a lead, updating a record), this matters more. The agent acts faster than a human would. That speed is the feature. But speed without traceability is what makes procurement teams pause. What a functional audit trail covers, at minimum: → User or agent that triggered the action → Timestamp and session context → What data was read and what was written → Whether the action is reversible and by whom → What the end user sees vs. what the admin retains I've seen well-built products lose enterprise deals at the security review stage because the audit trail was an afterthought. The product worked. The record of what it did didn't exist. Build the trail before you need to show it.

Our first profitable month this year had nothing to do with sales. We cut fixed costs. Revenue fixes take quarters. Cost fixes take weeks. Most founders attack the slow lever first because it feels like growth. Survive on the fast lever. Grow on the slow one.

The standard SaaS idea advice: find an underserved market. The approach that helped one founder hit $9k/month faster than expected: find where money is already flowing toward a broken solution. There's a real difference between the two. Underserved markets are often underserved because the problem isn't acute enough or the buyer can't pay. Bad solutions are different. The customer is already spending. The problem is already validated. You're not convincing anyone the problem exists. The signal worth looking for: complaints that repeat across multiple platforms from people who are already paying for something that doesn't quite work. I've watched founders spend a year doing customer discovery trying to confirm a problem exists. The ones who moved faster started by looking for money already moving in the wrong direction. That's your replacement opportunity.

I keep hearing the same question from early-stage founders right now. "If someone can clone my product in a weekend, what's protecting me?" It's the right question. The answer just isn't about the code. A weekend clone can match your feature list. What it can't replicate: • six months of customer feedback that shaped why those features work the way they do • the specific edge cases you've already burned through and fixed The moat moved. Before AI coding tools, the cost of building was the barrier. That's gone. What replaced it: depth of understanding. How well do you know the workflow your customers actually use this product for, and can you prove it? In my experience, the founders who lose ground to fast clones were building broad with no real insight advantage. The ones who hold position have a specific vertical and real workflow knowledge no weekend project can replicate. The fast clone is a threat to the generic solution. It's not a threat to the founder who knows the customer better.