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This is a good summation of the current challenges with startups that are attempting to compete on technical merit. I know this first hand as I started a comput

This is a good summation of the current challenges with startups that are attempting to compete on technical merit. I know this first hand as I started a computer vision company right around 2012 when the "AI" boom was really picking up steam. I would never dream today of starting a very technical company because every major will poach your talent (happened to me), invest in your competitors or attempt to acquire you (some people don't want to be acquired). Contrary to your points, I don't see any way this will change until some big technology revolution happens that puts power back into the hands of creators - though that might be a pipedream. Note, that doesn't mean you can't start and be successful with a company. What I'm trying to point out is that the days of a scrappy upstart coming along to "unseat the leaders" doesn't seem anywhere near realistic. The only counter to this might be worldwide competition - so for example I could see Tencent getting bigger than Facebook in total user numbers. I'm not sure if these are comparable because they are in segmented markets.

streetcat1 7y ago
So there are actually three tech revolutions going on in parallel: 1) Kuberentes 2) Microservices. 3) AI All of them are based on open source, and all of them are permissionless (I.e. the startup does not need permission from a big company, e.g. an app store owner). They also erode the build-in advantages of big tech: 1) Kubernetes erode cloud lock-in on compute/storage. 2) AI is best serve on the edge. I would say that everything today is up for grab.
- buzzkillington 7y ago
  Kubernetes is a solution worse than the problem. Microservices are unix pipes but slower. AI is linear algebra that costs x1000 because of the brand name. There is no revolution in software. We are just building crud as high and as fast as we can because the quality of developers tends to zero as the number of developers increases.
  - crtlaltdel 7y ago
    ^the account name that posted this comment is the single most honest thing i have seen on the internet today. well done!
  - abhs 7y ago
    I like that username... :D
  - EdwardDiego 7y ago
    > Kubernetes is a solution worse than the problem. I disagree. Maybe it's because we haven't gone "full Kubernetes" where every cronjob is now a Kubernetes cronjob, or what have you, but for all the complexity we've had to overcome, we've seen substantial benefits. For example, autoscaling instances of my data pipeline apps based on Kafka lag. Was it complex? Yes. We had to expose Kafka topic lag as a metric to Prometheus, then configure the k8s metrics server that HPAs (Horizontal Pod Autoscalers) use to scale to pull that in from Prometheus using k8s-prometheus-adapter as a custom metric, then set reasonable scaling limits in the data pipeline apps HPAs. Was it worth it? Fuck yes. We no longer have to worry about data arriving out of time at our data warehouse, because the scaling we've configured (in YAML, god rest its dirty soul) ensures that our data will always arrive at the data warehouse within 5 minutes, which is ideal for a near real time reporting product that greatly enables our end users. Is Kubernetes worth it? For us, definitely. For anyone else? Really depends on your use case, don't cargo cult this shit.
    - pnako 7y ago
      How much data do your "data pipeline apps" process with Kafka, Kubernetes and probably other K-named things?
      - EdwardDiego 7y ago
        Several terabytes a day, but it varies, hence why I love the auto-scaling.
        7y ago
- deleted 7y ago
  [deleted]
echelon 7y ago
> I would never dream today of starting a very technical company because every major will poach your talent (happened to me), invest in your competitors or attempt to acquire you (some people don't want to be acquired). Damn, this is exactly what I want to do. Computer vision too. Do you have any advice other than "don't"? Would basing the company on the East coast near a tier-1 research university help with talent? Could you have strong software engineers support the research roles? Does that pattern work effectively? Another thing I really want to do is offer a 4-day work week with the promise of sabbaticals and/or remote work. That might not be attractive to everyone, but it's something that would speak to me and that I would want in addition to equity and salary. Are you still interested in computer vision? Want to chat sometime?
- mifeng 7y ago
  My first startup used computer vision to extract tables from PDFs. We had great engineers but my co-founder and I didn't know enough about ML back then to design a proper feedback loop. My advice is to find a technical co-founder who has built and scaled a CV system at a larger tech company, but who is looking for a new challenge. There are a lot of annoying problems that can be solved with CV as long as you create a way for users to help you get labeled data.
- AndrewKemendo 7y ago
  Happy to chat, first initial + last name at google. I think the answer is, know exactly how you want to exit before you start and plan for that. Realize also that is 180 degrees polar opposite of how the major VC's want you to approach it - so if you wanted institutional money from Sequoia etc... they won't invest if they know that is the case. There are a lot of good reasons why they take that approach, power law and all. The reality is, your best case scenario is most likely an acquisition. Which might be exactly what you want, and if so absolutely go for it, but you need to really understand how and why and when the companies you are targeting do their acquisitions. This is incredibly hard to do if you haven't gone through multiple acquisition Due Diligence processes before, but I know of people who have repeatedly done this, so it can be done. As to the other points, there are too many variables to say whether those are good ideas or not, totally depends on what you're trying to do.
jandrewrogers 7y ago
Your points are well taken, and I have thought about them at length because I like to build startups in areas where this situation is the norm. One unorthodox strategy, which I've discussed with investors, is funding early stage such that you can pay comp that BigCo can't easily compete with -- their scale works against them. In early stage, the absolute amount of capital is trivial (and there are trillions sloshing around these days), but the quality of the initial engineering team will have an enormous impact on the success probability of the investment. A few extra million in wages can have enormously high leverage in generating billions in returns. Imagine poaching literally the best engineers in FAANG and the kind of core team that would give you to attack markets no one has attacked before. I'd like to see top engineers in tech compensated like top professional athletes. Investors pay the wages for athletes because the returns justify it, the scale of the wages is not material if you are chasing results. I believe that model applies in tech as well.
- candybar 7y ago
  I think we may be past the point where even this would work - too many startups are offering deals that are transparently awful in a way that discredits the startup ecosystem at large. I've gotten a few early-stage startup offers recently and every single one came out to be over a 50% paycut (even accepting their generous valuations) despite the fact that I made it relatively clear where the total comp needs to be for me to move forward and I was assured that at the end of the process they would be willing to make it work. Meanwhile, if you apply the same math to the founders and their equity, in every single case they are paying themselves multiples of what they are offering me and significantly above their own market value, despite them not having done anything yet beyond getting seed funding. What then happens is that startups like these end up getting what they pay for - they get the appropriate level of talent at market price. This maybe a market-optimal result but the cumulative effect of all these startups doing this is that most top people have sort of written off "working at a startup" as a reasonable career option, because of pay and what pay implies about the talent level of the people they would work with. This hurts all startups, even those that are willing to pay up to get the right talent. And if top engineers won't even consider your company, offering top-tier comp may backfire - you run the risk of overpaying for the same talent you could've gotten at a lower price. It's hard to break out of this cycle - consider how the same few schools are able to persistently attract the best students - and I suspect the entire startup ecosystem is trapped in it.

streetcat1 7y ago

So there are actually three tech revolutions going on in parallel: 1) Kuberentes 2) Microservices. 3) AI All of them are based on open source, and all of them are permissionless (I.e. the startup does not need permission from a big company, e.g. an app store owner). They also erode the build-in advantages of big tech: 1) Kubernetes erode cloud lock-in on compute/storage. 2) AI is best serve on the edge. I would say that everything today is up for grab.

buzzkillington 7y ago
Kubernetes is a solution worse than the problem. Microservices are unix pipes but slower. AI is linear algebra that costs x1000 because of the brand name. There is no revolution in software. We are just building crud as high and as fast as we can because the quality of developers tends to zero as the number of developers increases.
- crtlaltdel 7y ago
  ^the account name that posted this comment is the single most honest thing i have seen on the internet today. well done!
- abhs 7y ago
  I like that username... :D
- EdwardDiego 7y ago
  > Kubernetes is a solution worse than the problem. I disagree. Maybe it's because we haven't gone "full Kubernetes" where every cronjob is now a Kubernetes cronjob, or what have you, but for all the complexity we've had to overcome, we've seen substantial benefits. For example, autoscaling instances of my data pipeline apps based on Kafka lag. Was it complex? Yes. We had to expose Kafka topic lag as a metric to Prometheus, then configure the k8s metrics server that HPAs (Horizontal Pod Autoscalers) use to scale to pull that in from Prometheus using k8s-prometheus-adapter as a custom metric, then set reasonable scaling limits in the data pipeline apps HPAs. Was it worth it? Fuck yes. We no longer have to worry about data arriving out of time at our data warehouse, because the scaling we've configured (in YAML, god rest its dirty soul) ensures that our data will always arrive at the data warehouse within 5 minutes, which is ideal for a near real time reporting product that greatly enables our end users. Is Kubernetes worth it? For us, definitely. For anyone else? Really depends on your use case, don't cargo cult this shit.
  - pnako 7y ago
    How much data do your "data pipeline apps" process with Kafka, Kubernetes and probably other K-named things?
    - EdwardDiego 7y ago
      Several terabytes a day, but it varies, hence why I love the auto-scaling.
      - 7y ago
deleted 7y ago
[deleted]

echelon 7y ago

> I would never dream today of starting a very technical company because every major will poach your talent (happened to me), invest in your competitors or attempt to acquire you (some people don't want to be acquired). Damn, this is exactly what I want to do. Computer vision too. Do you have any advice other than "don't"? Would basing the company on the East coast near a tier-1 research university help with talent? Could you have strong software engineers support the research roles? Does that pattern work effectively? Another thing I really want to do is offer a 4-day work week with the promise of sabbaticals and/or remote work. That might not be attractive to everyone, but it's something that would speak to me and that I would want in addition to equity and salary. Are you still interested in computer vision? Want to chat sometime?

mifeng 7y ago
My first startup used computer vision to extract tables from PDFs. We had great engineers but my co-founder and I didn't know enough about ML back then to design a proper feedback loop. My advice is to find a technical co-founder who has built and scaled a CV system at a larger tech company, but who is looking for a new challenge. There are a lot of annoying problems that can be solved with CV as long as you create a way for users to help you get labeled data.
AndrewKemendo 7y ago
Happy to chat, first initial + last name at google. I think the answer is, know exactly how you want to exit before you start and plan for that. Realize also that is 180 degrees polar opposite of how the major VC's want you to approach it - so if you wanted institutional money from Sequoia etc... they won't invest if they know that is the case. There are a lot of good reasons why they take that approach, power law and all. The reality is, your best case scenario is most likely an acquisition. Which might be exactly what you want, and if so absolutely go for it, but you need to really understand how and why and when the companies you are targeting do their acquisitions. This is incredibly hard to do if you haven't gone through multiple acquisition Due Diligence processes before, but I know of people who have repeatedly done this, so it can be done. As to the other points, there are too many variables to say whether those are good ideas or not, totally depends on what you're trying to do.

jandrewrogers 7y ago

Your points are well taken, and I have thought about them at length because I like to build startups in areas where this situation is the norm. One unorthodox strategy, which I've discussed with investors, is funding early stage such that you can pay comp that BigCo can't easily compete with -- their scale works against them. In early stage, the absolute amount of capital is trivial (and there are trillions sloshing around these days), but the quality of the initial engineering team will have an enormous impact on the success probability of the investment. A few extra million in wages can have enormously high leverage in generating billions in returns. Imagine poaching literally the best engineers in FAANG and the kind of core team that would give you to attack markets no one has attacked before. I'd like to see top engineers in tech compensated like top professional athletes. Investors pay the wages for athletes because the returns justify it, the scale of the wages is not material if you are chasing results. I believe that model applies in tech as well.

candybar 7y ago
I think we may be past the point where even this would work - too many startups are offering deals that are transparently awful in a way that discredits the startup ecosystem at large. I've gotten a few early-stage startup offers recently and every single one came out to be over a 50% paycut (even accepting their generous valuations) despite the fact that I made it relatively clear where the total comp needs to be for me to move forward and I was assured that at the end of the process they would be willing to make it work. Meanwhile, if you apply the same math to the founders and their equity, in every single case they are paying themselves multiples of what they are offering me and significantly above their own market value, despite them not having done anything yet beyond getting seed funding. What then happens is that startups like these end up getting what they pay for - they get the appropriate level of talent at market price. This maybe a market-optimal result but the cumulative effect of all these startups doing this is that most top people have sort of written off "working at a startup" as a reasonable career option, because of pay and what pay implies about the talent level of the people they would work with. This hurts all startups, even those that are willing to pay up to get the right talent. And if top engineers won't even consider your company, offering top-tier comp may backfire - you run the risk of overpaying for the same talent you could've gotten at a lower price. It's hard to break out of this cycle - consider how the same few schools are able to persistently attract the best students - and I suspect the entire startup ecosystem is trapped in it.