Ask HN: Solutions searching for problems, can they succeed?

[iiJDSii]

Interested in what may now be a somewhat contrarian idea. It's startup school 101 to avoid "SISP": Solutions in Search of a Problem. I think YC's startup school actually coined this acronym.

But I'm wondering, have great businesses and products started from this? There's plenty of downsides that have been discussed, but I'd like a different perspective too.

One could argue that anything that began organically as some techie's for-fun side project might fall under this category.

I'd even argue that something like ChatGPT, created by a (formerly) non-profit org, might qualify as a powerful example. I mean, a computer program that just talks back to you and answers generic questions? Sounds like a cool tech project, or 'solution' on the surface, without a specific problem in mind. Of course, now we see it has a transformative technology that at least has a monthly subscription business model that many people are willing to pay.

How about the VR headsets? I doubt the inventor(s) sat down with the explicit goal of solving a problem after extensive market research and customer interviews.

My initial take: on the surface I think this is a "high risk, high reward" approach to technology entrepreneurship. If you take the opposite approach, problem-focused, iteration-driven, lean startup, etc, you're far more likely to end up with something like payroll or accounting software. Not that there's anything wrong with stuff like that, it's very important in its own right to solve more mundane problems well.

Curious to hear your thoughts.

[InkCanon]

I somewhat dislike the SISP saying because it describes an attitude, not an actual product. Many successful companies have started from a "solution" (more accurately a technology) like Google and Akamai, and it is perfectly legitimate behavior to try to find problems with a solution. The situation described by SISP is when a founder does not respond to a real, serious customer problem and instead has a problem conjured on his head which he "solves". A better saying would be "Person Not In Search of a Problem".

On a broader level, pure solutionism tends to only really work when you have some novel research, then need to find a use for it. And these inevitably comes from academia because you need someone to subsidize the risk. Then you plug it into some real problem. Many people who start with the goal of being a founder naturally try to start with problems. OpenAI was very of the first, VR was the second (it tried to respond to the soft data, mostly an idea, that people really wanted VR but ultimately this was false).

[iiJDSii]

Indeed that's a good distinction that I don't think is made. Some of the other advice I've gotten from YC/PG essays is of the form 'build what seems cool' (if you're a technical and curious person). This seems to be the 'high reward, high risk' route. I suppose VR would fall into this bucket, it's definitely cool to a lot of people, but from its inception, has taken decades to really get anywhere in a commercial sense - and it still seems quite niche.

Other examples: even though PG mentions them as solving problems, would Microsoft (starting with OS software for a niche machine) and Facebook (college kids online directory) really have been starting as 'problem-first' products? It seems like the founders were more building what seemed interesting to them, and got the high reward by stumbling on the right things (and building them better than competitors).

Still even with the distinction ('person not in search of a problem'), it seems there could be a hammer/nail phenomenon going on if someone, say, builds cool tech of a certain type, and then tries to commercialize from there, in that order. What seems to make sense is to 'build what seems cool', keep an up-to-date mental model on new and emerging technologies, and stay open-minded to problems arising that can be solved. But even then, without a 'problem-first' mindset, could one get too pigeon-holed I wonder.

Not sure where I'm going with this, just thinking out loud. The topic fascinates me.

[PaulHoule]

How about this distinction.

There are some things where the "lean startup" applies. For instance if you made an Ebay or AirBNB or Reddit or Substack kind of a site you could get a rough prototype running quickly. The software is maybe 20% of the effort, but 80% of the effort is in business development (recruiting people to kickstart the market)

Some products on the other hand take years of development and may or may not work. Golden Rice, Falcon 9, the LLVM-based C compiler are all examples.

I worked on a system which was uncomfortably in between these models. On one hand were developing LLM-like systems before LLMs as we know them were available so we could have spent a few years on development. However we could sell projects to customers which caused us to zig and zag a lot to meet their needs. That was a good thing because we learned a lot about what was possible (it contributes to the research) but we wasted a lot of time with spoiled work in progress, etc.

In our case I think the investors believed in our vision but were skeptical about our ability to execute (rightly so: I couldn't even get the data scientists to use a standard version of Python even though that was what I got hired for) and would bring in consultants that were often counterproductive (zigging and zagging to meet customer needs meets customer needs but spending weeks writing up OKRs is busywork.)

I believed in the story more than anyone but the C-levels because I had been working on a similar thing on my own account. I'd tell people when it was tough that if our product was sufficiently realized it would be worth it for one of our customers to buy us. I thought it would be a Big 5 accounting firm or an airplane manufacturer but it turned out to be a major consumer brand.

That's honorable and probably paid the VCs back what they put in, but had we had the funding to develop technology for a few years and enough contact with applications to know what direction to go in, switched to transformer models the moment BERT came out, and if we were more disciplined about our streaming engine so it always gave the right answers (wrote down what the algebra was for it rather than argue about whether we should call it an algebra) we could have changed the world.

[iiJDSii]

Interesting examples. Personally, I don't really consider Ebay, AirBnB, Reddit, or Substack to be "technology companies". They are businesses that happen to be online, imo. I'm sure that e.g. AirBnB has lots of machine learning technology now, but I reckon it's doing niche stuff like optimizing conversion rates and profits by customer tracking, recommending, etc. So basically, after-the-fact value optimization. Nothing really invented or new created, aside from their novel ideas and unique executions.

Other actual tech examples seem to fall into 1 of 2 camps: obvious but hard to do (better search engine, better rockets, electric cars, etc), or cool but non-obvious customer end uses (maybe LLMs, VR/AR, curved or flexible high def screens, etc). The latter category has more risk but probably lower hanging fruit to get started, because the market needs are less obvious.

In your example, do you think the customer focus lead to pre-mature optimization and kind of tunnel-visioned the team away from further LLM development? That's another type of trade-off that's probably impossible to predict at the time. I mean who doesn't want customers.

I'm not entirely surprised that OpenAI was able to achieve so much given their structure - they had the mandate of a trendy new research lab, top talent, with 100M+ funding and no need to cater to any early customers. Seems like a great (though typically impractical) way to build big new things. Then they had the right top-level guidance when the tech was getting ready, to pivot and raise more money (unlike XEROX PARC for example).

[jasdi]

Think about Google. They start with page rank to solve info explosion, collect mountains of info, and end up building tech to improve efficiency of Advertising. Not exactly solving info explosion (Youtube is quite a large noise generator/info polluter).

Whatever approach is taken, it exists inside an ever changing complex external environment that no one controls. So the approach is always reactive and adapting to the environment. One day it can look revolutionary, another day it can look mundane. Explore-Exploit tradeoff.