VCs keep pushing AI as the magic solution to automate expensive consulting work and deliver software-level margins. Reality check: AI creates more work than it saves, but admitting that would tank valuations.
Call it "workslop" - the weird mix of work that looks professionally done but doesn't actually function. AI output that passes visual inspection but fails when people try to use it.
AI hallucinations - confident-sounding output that's completely wrong - are everywhere now. Spent 4 hours debugging an AI-generated Kubernetes deployment that kept throwing Error from server (NotFound): namespaces "production-cluster" not found. The YAML looked clean, had proper error handling, detailed comments. Problem? It referenced clusters and namespaces that didn't exist. GPT-4 just made up an entire infrastructure stack that looked plausible.
Legal teams are using AI for contract drafts now. Problem is, AI hallucinates case law and makes up precedents that don't exist. Lawyers catch most of it, but not all. One bad citation in the wrong contract and you're looking at liability issues.
The real problem: AI outputs look professional and confident, even when they're completely wrong. Humans usually hedge when they're unsure. AI just makes shit up with perfect formatting and bullet points.
Everyone thinks AI saves time until they're fixing its mistakes. VCs fund companies claiming they can automate service work, but ignore the part where humans still check everything. Startups claim 30-40% automation rates, but they don't publish their error correction costs.
The hidden cost is time spent reviewing and fixing AI output. Multiply that across a team, and you're spending more time on cleanup than you save with automation. But VCs don't want to hear about that.
AI hallucinations create this weird situation where deliverables look complete but don't actually work. Teams use AI for documentation, proposals, code - output looks professional with proper formatting and examples. But when people try to use it, half the API endpoints return 404 Not Found or the code examples reference libraries that don't exist.
Last month our team shipped documentation with AI-generated curl examples. Within hours, developers were filing GitHub issues: curl: (6) Could not resolve host: api.example-service.com. The AI had invented an entire API that looked realistic but was completely fake. Spent 2 days fixing docs that should have taken 30 minutes to write correctly.
It's this weird productivity trap - companies invest in AI tools but spend more time fixing output than they save using it. Executives think AI boosts efficiency while engineers clean up the mess.
VCs promise AI will deliver 60-70% margins or whatever. They ignore the cost of human verification. If you're reviewing everything anyway, where's the efficiency gain?
Services can't ship broken deliverables and patch them later like software. Sales teams send AI-generated proposals with wrong pricing or non-existent features. Clients call asking about implementation timelines for stuff that doesn't exist. Deals die fast.
The irony: successful AI implementation needs more human expertise, not less. You need people who understand the business domain and how AI actually works. Companies try cutting costs by replacing experts with AI, but you need experts to make AI work right.
Smarter VCs are hiring actual AI engineers instead of funding ChatGPT wrappers. Turns out you can't just dump AI into business processes and expect magic.
Companies that figure out how to avoid this workslop trap will win. Right now, most are just creating expensive messes. We replaced human expertise with confident guessing machines and somehow expected better results.