Why deep tech products fail
It’s fascinating how the life cycle of a product looks. My unpopular opinion is that communication is the biggest killer of deep tech products.
In Crossing the Chasm, Geoffrey Moore identifies 5 steps every innovative company goes through. First, 2.5% of the market adopts the product. This first batch of users are innovators themselves — they understand the tech well. Then come the early adopters, that comprise roughly 12.5% of the market — they are somewhat curios, but don’t get everything.
The gap between early adopters and early majority, is called the chasm. How fast the early majority gets acquired(or not) as customers is crucial, and makes or breaks a product.
This is true for the blockchain, graph databases and A.I.
In Zero to One, Peter Thiel differentiates between horizontal(polynomial) and vertical(exponential) progress.
Taking things that work somewhere and making them work everywhere is an example of horizontal progress. China is one example; its 20-year plan is to become like the United States is today. If you know your food ordering apps: GrubHub is the US leader and first mover while FoodPanda dominates the markets in Asia. Compare the valuation of these 2 companies, and you’ll understand that merely copying others will bring you less profit.
The single word for vertical, 0 to 1 progress is technology . Properly understood, any new and better way of doing things is technology — and deep tech is in most cases, the fuel.
My unpopular opinion is that many deep tech products don’t cross the chasm, because communicating their strengths and return on investment is impossible. The product can be communicated clearly to other peers(innovators), and some of the early adopters. Then, game over. This is ultimately a big loss, because deep tech is what drives vertical progress.
The communication chasm
- Developers can communicate with other developers
- Business execs, marketers can communicate with their peers
- There is no common language for business execs and developers
Let’s have a look at my favorite deep tech product, graph databases and the company behind the most popular solution, Neo4j.
If you’ll have a look at the Neo4j blog, you’ll quickly understand. No one article addresses business executives properly. It’s deep tech people, talking to other deep tech people. Some articles try to be friendly to the business guy, but end up exploding with tech jargon and references.
My best stab at explaining graph databases in a sentence is this: instead of storing data in a tabular format(Excell), we store it in a easy-to-draw, whiteboard friendly manner that puts an emphasis on relationships in the data, instead of columns and rows. The implications to this are monumental — it’s what made the Google search empire possible. But the tech continues to be heavily overlooked(even inside Google, despite push from C-level and V-level people).
Here’s a bonus unpopular claim: i think *most* of the code(think Pareto distributions) that covers 80% of the needs out there has been already written.
You are a developer, and want to do deep tech — it’s probably the most rewarding intellectual challenge out there. The challenge(and bulk of the work) in the coming years will be to correctly communicate and identify needs based on the product, and some clever, business-oriented, integration work — not implementing it.
Companies, business execs — if you want your business to create genuinely *disruptive*(cringe!) products, pay attention to and be patient with deep tech consultants and developers. If you want to transform what you can do with your data, hire me or book a free 15-min blitz call.
Happy 0-to-1-ing!
