Bridging the Gap: Your Tools, United by AI for Smarter Product Development
Sep 19, 2024
I remember a few years ago, I was trying to automate a truly tedious task for work. It involved pulling data from one clunky system, tweaking it manually in a spreadsheet, and then painstakingly uploading it to another. I spent hours trying to write scripts and build macros, but every time a small detail changed, the whole thing fell apart. It was frustrating, and honestly, a huge time sink. Back then, AI felt like something out of a sci-fi movie, a distant promise of a smarter future.
Fast forward to today, and that future is not only here, but it's rapidly evolving beyond what I, or many others, could have imagined. Artificial intelligence has come a long way. We've gone from simple tools that needed explicit instructions to fully autonomous agents capable of making decisions. What started as a way to summarize text or generate SQL from plain English has transformed into a new paradigm: agentic AI.
I believe this shift is not just about better models, but about how we, as builders and users, adapt and push AI’s boundaries. Each phase has unlocked new possibilities, not just because of improvements in technology, but because ambitious founders experimented, iterated, and proved what was possible. Every major AI evolution has led to successful startups pioneering the next big thing.
AI as a Tool
The first wave of AI was all about direct inputs producing direct outputs. You had to prompt it precisely to get the result you wanted. This was particularly useful for structured tasks—summarization, translation, and generating SQL queries from plain English.
OpenAI’s early Codex, for example, turned human language into functional SQL, making databases far more accessible. Similarly, startups like Copy.ai built AI-powered content generation tools that automated human creativity. But at this stage, AI wasn’t truly "thinking"—it was responding. The intelligence still relied heavily on the user’s instructions.
AI as an Assistant
The next phase saw AI moving beyond one-off interactions into more integrated, interactive assistants. Instead of simply producing an output, AI became part of a workflow, continuously helping users with tasks.
For instance, Cursor embedded AI inside development environments, making coding more intuitive. But these assistants still required significant human direction. They reduced friction, but they didn’t independently make decisions.
AI as an Agent
Then came AI that could not only assist but act. AI agents execute tasks end-to-end, taking automation to another level.
A breakthrough example is Devin AI, the world’s first AI software engineer, capable of coding and debugging on its own. This was a major shift—AI wasn’t just enhancing human capabilities; it was beginning to replace manual effort entirely.
The Rise of Agentic AI
Now, AI is moving beyond executing predefined tasks to dynamically figuring out what needs to be done next. Instead of following a fixed script, agentic AI can adapt in real time.
Anthropic’s Claude is one of the leaders in this space, aiming to build AI that not only completes requests but can also determine what’s necessary to achieve a broader goal. This marks the beginning of AI managing complex processes with minimal human intervention.
The Race to Find New Use Cases
With the rise of agentic AI, we’re seeing an intense competition among founders to discover the next breakthrough use cases. Investors are placing massive bets, sometimes funding AI startups at sky-high valuations—even before they’ve proven product-market fit.
I think this is both an incredible opportunity and a dangerous trap. Some of these AI startups will fail. A lot of capital will be lost. But for the ones that succeed, the upside is enormous.
Take Lovable.dev as an example. This text-to-functioning-app editor skyrocketed to $17M in ARR in just six months. The appeal is clear: you simply type the app you want, and Lovable generates the necessary code and deploys it for you. This kind of AI-native experience would have been unimaginable just a few years ago.
What Enabled This Shift?
Several key factors have driven AI’s rapid evolution:
Better models → More capable AI like GPT-4, Claude, and multimodal systems that handle images, text, and actions.
Better infrastructure → Vector databases, AI memory, and improved orchestration frameworks have made complex AI applications more viable.
Successful experiments → Startups testing the limits of AI have demonstrated what’s possible, inspiring others to follow suit.
Why Does This Matter?
Each AI evolution doesn’t replace the last—it builds on it. AI assistants didn’t kill AI tools, and AI agents haven’t made assistants obsolete. Instead, every step forward has unlocked new, higher-value use cases.
I believe we’re just at the beginning of agentic AI. The most exciting part isn’t what AI can do today—it’s what people will build with it next. The startups that master this new paradigm will define the next era of AI-powered work. Soon, instead of asking AI to perform a task, we’ll just define a goal—and AI will figure out how to achieve it.
The race is on.