The AI Era: How Product Cycles Drive Growth and Transform Software Markets
The AI revolution represents the latest major product cycle in technology, following the PC, internet, cloud, and mobile eras. Unlike previous cycles that faced resistance from incumbents and customers, AI enjoys universal enthusiasm—everyone recognizes that artificial general intelligence in everyone’s pocket is valuable.
This widespread acceptance creates unique opportunities. The infrastructure already exists: 8 billion humans have smartphones, cloud computing powers everything, and AI adoption is accelerating faster than any previous technology. The result? Software companies are achieving unprecedented growth, with some reaching $100 million in revenue within one to two years.
Three Investment Themes Define the AI Market
Traditional Software Goes AI-Native
Existing software categories are being rebuilt from the ground up with AI as a core component. This mirrors the cloud-native transformation of the 2000s, when companies like Workday and Salesforce displaced on-premise incumbents.
The opportunity lies in “greenfield” markets—new companies or businesses hitting inflection points where they need new systems. A startup choosing its first ERP system will pick the AI-native option over legacy solutions. While incumbents are adding AI features, they face the innovator’s dilemma of cannibalizing existing revenue streams.
Every major software category presents this opportunity: payroll, customer support, ERP, and beyond. The key is building systems of record that become deeply embedded in business operations, making switching costs prohibitively high.
Software Replaces Labor
The largest opportunity involves creating software that performs jobs previously done by humans. This isn’t about the traditional software market—it’s about the vastly larger labor market.
Consider a front desk receptionist earning $47,000 annually. Software that handles five of their eight responsibilities might cost $20,000 per year—expensive for software, but a bargain compared to human labor. The business can operate 24/7, speak multiple languages, and never call in sick.
The key is building defensible moats. Companies must become systems of record, not just point solutions that competitors can easily undercut. Success requires owning the entire workflow and accumulating proprietary data that improves performance over time.
Proprietary Data Creates Walled Gardens
The most defensible AI companies control unique datasets that large language models cannot access. These “walled gardens” transform low-value data into high-value finished products.
FlightAware exemplifies this model. They collect free airplane transponder data using antennas purchased on Amazon. While anyone can access this information, FlightAware’s comprehensive database and AI-powered analysis create valuable insights unavailable elsewhere.
Medical AI company Open Evidence demonstrates the power of exclusive data. While ChatGPT provides general medical information, Open Evidence has exclusive licenses to medical journals, making it indispensable for evidence-based care. Two-thirds of American doctors use it weekly.
The pattern repeats across industries: legal databases, procurement contracts, historical subscriber data. Companies that control unique information can charge premium prices for AI-powered insights that competitors cannot replicate.
Why Incumbents Won’t Win Everything
Unlike previous technology cycles, AI benefits both startups and incumbents. Established companies will strengthen their positions by adding AI capabilities and charging for new services. However, three factors favor new entrants:
Model aggregation beats single models. Just as Kayak succeeds by searching all airlines rather than promoting one carrier, AI applications benefit from accessing multiple models with different specializations.
Greenfield opportunities abound. New companies and businesses at inflection points choose the best available solution, not the one they’re already using.
Data moats compound. Companies that start collecting proprietary data today will have insurmountable advantages as their datasets grow.
The Path Forward
The AI era is just beginning. Unlike previous cycles that took years to mature, AI applications are achieving massive scale in months. The combination of existing infrastructure, universal acceptance, and transformative capabilities creates unprecedented opportunities.
Success requires focusing on defensibility from day one. Whether rebuilding existing categories, replacing human labor, or creating data-driven walled gardens, companies must build sustainable competitive advantages that compound over time.
The next decade will see the creation of enormous value as AI transforms every aspect of business and consumer life. The companies that understand these patterns and execute effectively will define the next generation of technology leaders.