AI's Profound Impact: Solving Unknown Problems and Redefining SaaS Value

The current wave of AI euphoria, reminiscent of the dot-com bubble, masks a deeper, more complex transformation of the software industry. While the immediate focus is on cost reduction and efficiency gains, the truly profound implications lie in the creation of entirely new categories of problems that can be solved, the redefinition of value, and the emergence of competitive advantages for those who embrace the difficult, long-term shifts. This conversation reveals that the most significant impact of AI on SaaS won't be about replacing existing software, but about fundamentally altering what software is and what users can expect from it, creating opportunities for those willing to look beyond the obvious and the immediate.

The prevailing narrative around AI and SaaS often defaults to the immediate, tangible benefits: faster development, lower costs, and automation of existing tasks. This perspective, however, misses the systemic shifts that AI is triggering. The conversation highlights a critical distinction: the difference between automating what we already do and enabling what we could do. This is where the true, non-obvious implications emerge, creating a landscape where foresight and strategic patience become potent competitive advantages.

The Illusion of Obvious Solutions: Automating the Known vs. Discovering the Unknown

The initial impact of AI, as discussed, is the acceleration of existing workflows. Software that once took years to build can now be prototyped and deployed much faster. This leads to a surge in automation, tackling problems that were previously too expensive or complex to address with traditional software. However, this "obvious" step, while productive, often distracts from the more profound changes. The true disruption comes when AI moves beyond automating the known to enabling the discovery of the unknown.

Consider the example of a manager facing a natural disaster. The first-order response is to use AI to find relevant data within existing systems like Salesforce or SAP. This is automation. The second-order response is to use AI to build a dashboard presenting this data. This is an improvement. But the third-order, and most powerful, implication is when AI proactively asks the manager what questions they should be asking, what they should be worried about, and builds a dashboard around those predictive insights. This isn't just about accessing data; it's about leveraging AI to augment human judgment and foresight.

"The underlying point is, I think the hard part of making software is almost never writing the code. There are some things where you're solving some hard technical problem, but mostly what you're doing, the problem is working out, is realizing that the problem in the company even existed, and then working out what would be the right way of solving that, and then working out how you would get everybody across the industry to use it."

This quote underscores a fundamental truth: the value in software creation often lies not in the technical execution, but in problem identification, solution design, and market adoption. AI, by lowering the barrier to code generation, shifts the focus dramatically to these higher-order skills. Companies that merely use AI to churn out more of the same code will find themselves in a commoditized race. Those that leverage AI to uncover and solve previously unimagined problems, or to redefine existing markets, will build durable advantages.

The "Tar Pit" of Innovation: Where Difficulty Creates Moats

The conversation touches upon the "tar pit" phenomenon -- problems that have been persistently difficult to solve with software, despite repeated attempts. These are not necessarily technically complex in terms of coding, but rather involve aligning incentives, understanding nuanced human workflows, and overcoming inertia. AI's ability to analyze vast datasets and identify patterns can help unearth solutions in these tar pits, but the process of translating that insight into a usable, adopted product remains a significant undertaking.

This is where delayed payoffs and competitive advantage emerge. Building a solution for a "tar pit" problem requires significant upfront investment, often with no immediate, visible return. Most companies, driven by short-term metrics, will shy away from such endeavors. However, success in these areas creates deep, defensible moats. The Frame.io example illustrates this: their innovation wasn't just about video editing, but about creating a "Google Docs of video" for collaborative workflows, solving a complex, multi-stakeholder problem that others had struggled with.

"And now imagine you are managing the color grading of that video and you're thinking, 'Hey, it's a massive pain in the ass to me to share these videos.' Are you going to build that? Is that what you, is that your skill? Is that how you think? Is that, do you want that to be the next two years of your life? Is that what you do? That's that's making working, seeing the problem exists and thinking that that of how to solve it is not about the code."

This highlights the crucial distinction between building code and building solutions. The latter requires a deep understanding of user needs and workflows, often gained through experience and a willingness to tackle difficult, non-obvious problems. Companies that can leverage AI to accelerate this problem-solving process, and then endure the long development cycle, will create offerings that are far more valuable and harder to replicate than those simply automating existing tasks.

The Authenticity Premium: Beyond Mechanistic Output

The discussion around AI-generated art and music raises a vital point about authenticity and value. While AI can produce technically proficient outputs, the emotional and artistic value often lies in the human intent, journey, and vision behind the creation. This principle extends to software. As AI makes it easier to generate code and build functional applications, the differentiator will increasingly become the human element: the insight, the judgment, the imagination, and the deep understanding of a problem that transcends mere mechanistic output.

The example of McKinsey consultants being dismissed as simply creating slides, when their true value lies in strategic thinking and problem framing, is pertinent. Similarly, a SaaS product that is merely a wrapper around SQL queries and a slick UI is vulnerable to commoditization. The real value, and the source of competitive advantage, will come from software that embodies a unique perspective, solves a complex problem in a novel way, or offers a level of strategic insight that AI alone cannot replicate. This requires a commitment to understanding what customers are truly buying -- not just code, but solutions, expertise, and a vision for how their problems can be solved.

Key Action Items

• Shift focus from "automating the obvious" to "discovering the unknown." Actively seek out complex, persistent problems within your industry that have resisted traditional software solutions.
  • Over the next quarter: Dedicate time to identifying 2-3 "tar pit" problems in your domain.
• Invest in deep problem understanding over rapid code generation. Prioritize understanding user workflows and identifying the right problems to solve, rather than simply building faster.
  • This pays off in 12-18 months: Establish processes for in-depth customer research and problem validation before committing to development.
• Embrace delayed gratification. Recognize that the most valuable innovations often have long development cycles and require patience.
  • This pays off in 18-24 months: Develop internal metrics that value progress on long-term strategic bets, not just short-term feature delivery.
• Cultivate unique insights and domain expertise. Focus on building offerings that embody a distinct perspective or solve problems in ways that AI alone cannot replicate.
  • Over the next quarter: Encourage teams to develop specialized knowledge in niche areas, focusing on "what ChatGPT wouldn't say."
• Define value beyond the code. Understand what customers are truly paying for -- expertise, judgment, imagination, or a unique solution -- and build your offerings around that.
  • This pays off in 6-12 months: Re-evaluate your product positioning and sales messaging to emphasize the unique human-driven value proposition.
• Experiment with AI for predictive and prescriptive capabilities. Move beyond using AI for simple data retrieval or automation, and explore its potential for foresight and strategic guidance.
  • Over the next 6 months: Pilot AI tools that offer predictive insights or prescriptive recommendations within your existing workflows.
• Foster a culture of curiosity and continuous learning. Encourage teams to explore new possibilities and challenge conventional wisdom, especially in areas where AI is making rapid advancements.
  • Ongoing investment: Allocate resources for continuous learning and experimentation with emerging AI capabilities.