One of the most influential academics in the entrepreneurship world is Steve Blank. His steps to the epiphany or i-Corps have shaped generations of founders and how they approach customer discovery and development. A recent article by Steve Blank, “AI and Teaching – The Brave New World”, explores what's is happening on the ground in startup education right now.
It doesn’t celebrate or dismiss AI. Instead it focuses on what can go wrong when the speed of building outpaces our own human speed of learning.
From scarcity to abundance
For years, one of the key constraints in startups was the ability to build. MVPs took time, resources, and technical capability.
That constraint has largely disappeared with introduction of vibe coding and arrival of multiple open platforms, inlcuding ones that let you stich a prototype for free.
Students in Blank’s class were showing up with working products on day one. Ideas could be turned into something tangible really fast.
What an amazing acceleration of the startup building progress. Almost infinite iterations are possible to dream up and deploy to test users very quickly.
This changes the game. Literally.
More product, less insight
What Blank observed is something we are also becoming increasingly aware of:
- Teams can now build faster than they can think
- They generate more products than they can reliably validate with human users
- They can easily mistake volume of output for deep understanding
The core issue is what Steve calls an “impedance mismatch” between product development and learning.
The bottleneck sits in the judgement space:
- Choosing the right problem.
- Interpreting weak signals from customers.
- Deciding what to ignore.
AI is making customer discovery harder
It is sort of counterintuitive.
AI accelerates building, but it can degrade the quality of learning:
- Teams rely on AI to generate communication, which reduces clarity of thinking
- Customer interactions become more superficial
- Validation gets replaced by assumptions dressed up as outputs
Blank notes that teams started producing what he calls “AI slop” when they outsourced thinking to tools.
The market has changed as well
There is a second-order effect that is easy to miss.
Customers themselves are now being disrupted by AI.
Some of the companies students spoke to weren’t just evaluating new tools. Most were questioning whether and how their business would exist at all.
During the validation the students observed :
- Data is no longer freely shared
- Organisations are more defensive
- Proprietary data becomes one of the few remaining moats
What comes next: from product to outcome
One of the more interesting signals in the article is the move from:
- Product/market fit
to - Agent/customer outcome fit
Customers don’t want more dashboards.
They want outcomes delivered.
AI agents will increasingly take on the “doing”, not just the “informing”.
That changes what we build, how we measure success, and how we engage users.
What this means for Canberra
There are three implications worth calling out for our ecosystem.
1. The barrier to entry has dropped. The bar for quality has risen.
Anyone can build. Fewer can interpret reality correctly.
2. Programs need to shift focus.
Less emphasis on tools and prototyping. More emphasis on judgement, customer engagement, and signal detection.
3. Relationships matter more, not less.
As data becomes guarded and markets become uncertain, trust becomes a critical asset.
This aligns with what we consistently see across CBRIN programs and events.
The founders who make progress are not the ones who build the fastest.
They are the ones who learn the fastest.
Final thought
AI has compressed the time it takes to create something.
It has not compressed the time it takes to understand whether it should exist.
That gap is where most of the risk now sits.
And it is where the work of founders, researchers and ecosystems like ours becomes more important.
