I once spent $50,000 on the most beautiful, eloquent, and utterly useless AI voice you can imagine. It sounded like a dream. It just couldn't understand a single thing anyone said to it. That expensive failure taught me more than any successful pitch deck ever could.
I'm Sahin Boydas. You might know me from RemoteTeam, which was acquired by Gusto, or from my earlier startup, MovieLaLa. Nowadays, I spend most of my time as an angel investor, writing checks for companies that are building the future. I’m lucky enough to have backed over 200 startups, including some you might have heard of like Anthropic, OpenAI, and Scale AI. I’ve seen the AI revolution from the inside, and nowhere is it moving faster or breaking more things than in the world of voice. After years of building, investing, and advising in this space, I’ve collected a few scars and a few lessons. Here are ten of them.
1. It's All About the Data, But Not Just Any Data
Everyone says "data is the new oil." It's a tired phrase, but in AI, it's true. The problem is, most people are drilling in the wrong place. In the early days of MovieLaLa, we were playing with voice snippets from movie trailers to see if we could categorize them by genre. We had a massive dataset, terabytes of audio. But it was a mess. Clean, well-labeled, and diverse data is infinitely more valuable than a giant pile of audio garbage. We learned that the hard way. It’s not about having the most data; it’s about having the right data.
2. Latency is the Silent Killer
Imagine telling a joke, but the punchline arrives two seconds late. It’s not funny anymore; it’s just awkward. That’s what latency does to conversational AI. I saw a startup burn through millions building a real-time translation earpiece. The tech was brilliant, but the half-second delay made conversations feel stilted and unnatural. It failed. For a user to feel like they are talking to something intelligent, the response has to be immediate. Milliseconds matter. Anything else feels broken.
3. The 'Uncanny Valley' of Voice is Real
We’ve all heard it. That voice that is almost human, but something is just… off. It’s creepy. One of my portfolio companies spent months trying to perfect a human-like voice for their customer service bot. The closer they got to human, the more complaints they received. They eventually found that a pleasant, clear, and obviously synthetic voice performed better. The lesson? Don't aim for a perfect human imitation. Aim for a perfect user experience. Sometimes, that means embracing the robot.
4. Emotion is the Next Frontier
The difference between a good voice AI and a great one will be its ability to understand and convey emotion. Think about a GPS navigator. A flat, robotic voice telling you to "turn left" is functional. But imagine a voice that sounds slightly more urgent when you’re about to miss your exit, or more relaxed on a long, open road. The first company that can reliably detect user frustration or excitement in real-time and respond appropriately will unlock a whole new level of interaction.
5. Don't Boil the Ocean. Find a Niche.
I get pitched a "Jarvis-like" general-purpose AI assistant at least once a week. I pass every time. The most successful voice companies I’ve seen are hyper-focused. They aren’t trying to be everything to everyone. They are the best in the world at one thing, whether it’s transcribing medical notes for doctors, analyzing sentiment on sales calls, or providing voice commands for industrial machinery. Solve a real, painful problem for a specific group of people. You can always expand later.
6. The 'Mic is Always On' Problem is a Trust Nightmare
Let’s be honest, the idea of a device in your home always listening is terrifying. As someone who has invested in companies at the forefront of this technology, I can tell you that privacy is not a feature; it’s a prerequisite. Users are not stupid. They are becoming more and more aware of how their data is being used. The only way to win their trust is through radical transparency and giving them absolute control over their own data. Any company that cuts corners here is building on a foundation of sand.
7. Text-to-Speech (TTS) is Only Half the Battle
Having a great-sounding voice is the easy part. The real challenge is on the front end: Speech-to-Text (STT) and Natural Language Understanding (NLU). Your AI can have the voice of a god, but it’s useless if it can’t understand my accent, your slang, or the dog barking in the background. The complexity of accurately converting spoken words into structured data and then figuring out the intent behind those words is a monumental task. That’s where the real magic, and the real difficulty, lies.
8. Your Business Model is Your Product
How you make money from your voice AI is as important as the technology itself. A pay-per-API-call model encourages different behavior than a flat-rate subscription. When we were building RemoteTeam, we thought a lot about how our pricing would affect our users. A usage-based model might scare away smaller teams, while a subscription could feel wasteful for infrequent users. Your business model isn’t just a way to collect revenue; it’s a core part of the product experience that shapes how people interact with your creation.
9. The Best Interface is No Interface
The ultimate goal of voice AI is not to create a cool app you can talk to. It’s to make the technology disappear entirely. I want a world where the AI anticipates my needs before I even have to ask. It’s about ambient computing—intelligence woven into the fabric of our environment. The AI should be a silent partner, removing friction from my life, not adding another screen to stare at or another command to memorize.
10. The Moat isn't the Model, It's the Ecosystem
A foundational model, no matter how powerful, is not a defensible business on its own. The real competitive advantage—the moat—comes from the ecosystem you build around it. Look at the giants in the space. They have developer platforms, hardware integrations (like smart speakers), and most importantly, a constant flow of user interaction data that creates a powerful feedback loop. If you’re building a voice company, don’t just think about your core algorithm. Think about how you can enable others to build on top of your platform. That’s how you build something that lasts.
The Road Ahead
So there you have it. Ten lessons from the trenches. The world of AI voice is just getting started, and the biggest opportunities are still ahead of us. The next decade will be defined by the companies that can build trust, solve real problems, and create experiences that are not just functional, but truly seamless. What are the lessons you’ve learned? Share them in the comments below.
Frequently Asked Questions
Are these recommendations still relevant in 2026?
Absolutely. While specific tools and tactics change, the underlying principles remain consistent. I update my thinking regularly based on what I'm seeing in the market and across my portfolio companies.
How were these items selected?
Each item on this list comes from direct experience, either from building my own companies or from patterns I've observed across the 200+ startups I've invested in. I prioritize practical, actionable items over theoretical concepts.
How do I know which items apply to my situation?
Start by honestly assessing where your biggest bottleneck is right now. The items that address that specific constraint will give you the highest return on your time and energy.