Written by an anonymous guest.
Across the company, we’re all about cutting-edge technology that drives real impact and is result-driven. But after evaluating generative AI tools like ChatGPT, I’ve decided it’s not right for our company culture and needs – at least not yet. Here’s why:
1. I don’t want to waste months trying to make black box AI align with our values and processes. As a lean startup, we simply don’t have spare engineering cycles to devote to uncovering and resolving the inevitable inconsistencies between how a generative model operates and our established workflows.
2. Too many unknowns around intellectual property and liability. What happens if our AI-powered products or marketing include biases, misinformation or inadvertently plagiarized content? The legal liability could be a nightmare.
3. Generative models can’t be audited or certified for crucial compliance. In our industry, we need iron-clad documentation on model provenance, training data, and decision-making to meet regulatory requirements. Today’s large language models remain opaque black boxes.
4. I worry about hallucinations and factual errors in mission-critical workflows. We process sensitive personal and financial data – AI hallucinations that make stuff up could lead to costly mistakes.
5. My developers say AI coding assistants introduce more bugs than they fix. While increasing productivity in some cases, my engineering team found the AI-generated code lacked quality, was bloated, and required excessive “human polishing.”
6. We’d have to spend millions retraining our workforce around AI tools. As a startup, we simply can’t afford the operational overhead of training everyone and redesigning our processes around generative AI right now.
7. Customers don’t want AI-generated content representing our brand. In our market research, clients expressed discomfort over having bot-written emails, support documentation, or other content associated with our brand.
8. Lack of customization and composability for our industry use cases. Current generative AI models are too general-purpose and rigid to mesh seamlessly with our specialized proprietary systems, data models, and domain expertise.
9. Privacy and security vulnerabilities are a non-starter for us. With the volume of sensitive data we process, we can’t risk models extracting and leaking private training data.
10. I’ve seen the cringe-worthy attempts at AI marketing copy. Maybe I’m just old-fashioned, but the generic, cliched outputs I’ve seen from AI copywriters would make me cringe as a customer.
11. Relying on a third-party model creates too much vendor lock-in risk. I don’t want to bet my startup’s future on a large tech company’s black box model that could be altered, restricted, or cease being updated at any time.
12. We can’t validate the fairness, bias, and safety of these models. Regulating AI bias and quantifying safety risks is immature – I can’t responsibly deploy generative AI without solving that.
13. We’re worried about disclosure rules around using generative AI. Uncertainty around whether and how we’d need to disclose AI involvement creates liability and transparency issues.
14. AI makes our specialized knowledge workers redundant over time. While improving productivity, I’m concerned generative AI could eventually commoditize the very human expertise that makes our products and services differentiated.
15. The hype cycle is causing talent retention headaches for us. While exciting for engineers, all the hysteria around generative AI has sparked resignation threats as developers chase moonshot “AI startup” dreams.
16. You still need highly skilled humans to wrangle and tune the AI. Deploying generative models responsibly requires building out an entire practice around AI oversight, prompt engineering, and human review.
17. It’s too big a change to go all-in on bleeding-edge technology. As an established startup, we simply can’t afford to disrupt our entire operations overnight by pivoting fully to unproven generative models.
18. Generative AI output lacks true creativity and differentiation. Can AI language models ever replicate the spark of truly original, creative human ideas? I have my doubts.
19. We sell premium, tailored services – not commoditized AI outputs. Our value proposition is delivering boutique, white-glove solutions tuned for each client. No generic bot outputs for our customers.
20. I’m old school – a human wrote our mission-critical content. With the stakes so high for our product documentation, marketing, and project plans, I insist they get the human touch.
21. We operate in a highly regulated industry with strict AI guardrails. From audits to certifications, deploying generative AI models responsibly in our sector would be a bureaucratic and legal quagmire.
22. We’d have to rebuild our production pipelines around generative AI. Our entire data infrastructure, QA processes, and DevOps tooling would need to be refactored to accommodate large language model outputs.
23. Internal resistance to “being replaced by robots and AI overlords.” I’m not going to steamroll an apprehensive workforce into a workplace AI transformation – that’s a recipe for mutiny!
24. We prioritize symbolic, interpretable machine-learning approaches. Rather than opaque neural networks, we build expert systems and constraint-based models for mission-criticality.
25. It doesn’t move the needle enough for our core KPIs and use cases. Based on diligence, generative AI didn’t show an ROI uplift significant enough for us to overhaul our processes.
26. I fundamentally believe humans should be in control of key decisions. Abdicating core business judgments like roadmaps, comms, and strategy to language models feels reckless to me.
27. We only use AI systems that we fully own and can audit the code. I won’t relinquish technological sovereignty and accountability by ingesting opaque external AI systems.
28. We’ve already made huge investments in human expertise and capabilities. Reinventing that wheel for some modest potential AI productivity gains? Not worth the sunk costs.
29. Generative AI introduces incalculable existential risks if misused. Imagine nation-state disinformation, biased healthcare models, or subverted autonomous systems. Too dangerous.
30. Text generation isn’t our focus – we deal with physical products. While generative models may help in some workflows, it’s not core to our industrial manufacturing business.
31. I’m worried about the energy/environmental costs of large language models. The carbon footprint of these massively trained models seems at odds with our sustainability values.
32. Early adopter challenges like prompt hacking make me apprehensive. Reports of adversaries gaming language models with carefully crafted prompts to generate disinformation give me pause.
33. There’s too much hype and not enough proven ROI for our use cases. I’m a rational business leader – I need more than aspirational musings to go all-in without dispassionate value analysis.
34. Responsible, ethical AI oversight is a huge resource and governance burden. Holistic AI governance, XAI, bias bounties – the accountability stack is simply too heavy a lift for us today.
35. I fundamentally believe technology should augment and empower humans, not replace them. Rather than subsuming core business practices to AI automation, we want to enhance our people’s capabilities.
So while generative AI is undoubtedly a powerful technology, I believe the risks and uncertainties still outweigh the potential benefits for my startup at this stage. We’re going to keep focusing on what we do best – building differentiated software and services powered by human expertise, ingenuity, and creativity. Let me know when AI can replicate the spark of human brilliance and we’ll reconsider. Until then, AI is staying in research mode.
