Many founders repeat the same mistakes with AI safety.
As a Silicon Valley veteran who has built and sold two AI companies, I'm breaking the code of silence. The true cost of implementing robust AI safety isn't in the tech—it's in the human capital and culture. I'll reveal the numbers and strategies you need to know.
The Framework That Actually Works
Here is a simple framework for evaluating AI safety. It's not complicated, but it requires discipline.
Step 1: simplicity beats complexity every time
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: the market doesn't care about your roadmap
Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly
Nothing works perfectly the first time. The companies that get AI safety right treat it as an ongoing process, not a one-time project.
What I've Learned From 109 Companies
A few patterns in AI safety come up again and again.
The founders who handle AI safety well understand that you should focus on one thing and do it exceptionally well.
The Counterintuitive Truth
In AI safety, the best practitioners often do less, not more.
Trying to do everything at once is one of the most common ways to fail: even a team with strong technology and talent can spread itself too thin.
The lesson: your team matters more than your technology. It sounds simple. It's incredibly hard to execute.
Real Talk: What Actually Matters
Here is what actually matters in AI safety.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on AI safety. Many fail because they moved too slowly.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. It sounds obvious, but many founders make decisions about AI safety in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around responsible AI, AI safety, AI regulation 2026, ai-ethics|AI bias, AI alignment that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But you don't need to get AI safety right on day one. You just need to get started and keep improving.
Founders who do well at AI safety tend to share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
Frequently Asked Questions
Q:How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.
Q:Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
Q:What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.