What does it really take to build a software startup that doesn’t crash before it can fly? In this episode of Tech Talks Daily, I sit down with David Hirschfeld, founder of Tekyz Inc., a software veteran who’s spent over 35 years working across more than 90 startups.
From his early days studying physics at UCLA to becoming the top national salesperson at Computer Associates, and later founding and selling his own successful software company, David has seen the full arc of startup life—success, failure, and the lessons in between.
In our conversation, David unpacks the patterns he’s observed over decades in the field and the epiphany that led him to create the “Launch 1st Method.” Rather than falling into the trap of building first and validating later, David’s approach challenges founders to prove product-market fit and generate real revenue before a single line of production code is written.
He shares how this model not only reduces time, cost, and reliance on outside investment, but also offers a better chance of startup survival by confronting real-world customer demand from day one.
We also explore how generative AI is reshaping software development, both in terms of speed and in shifting user expectations. David explains which areas of software development are accelerating most, where AI still struggles, and why even the best tools can't substitute for deep customer insight and thoughtful architecture. He also breaks down the artifacts that distinguish exceptional development teams—from weekly status reports to estimation accuracy—and how these practices shape delivery quality and team alignment.
But perhaps most importantly, David offers a grounded look at what makes founders succeed. He emphasizes the value of falling in love with a problem, not a product idea, and staying relentlessly focused on understanding customers and their willingness to pay. Whether you're just starting out or rethinking your strategy, this episode delivers practical insights on how to derisk innovation, build smarter, and stay resilient in a fast-moving AI era.
Are you building what people need, or what you hope they want? Let’s explore that further.
