3 ms·

Wish them luck but having gone through something similar with another deep learning company that raised a ton of money, things will probably get a lot harder fo

by m_ke 4y ago

Wish them luck but having gone through something similar with another deep learning company that raised a ton of money, things will probably get a lot harder for them after the C round when investors start looking at revenue and not growth, and with a large valuation that limits the pool of potential acquirers.

hackernewds 4y ago
Interesting you mentioned revenue and not net income / profits. How are revenue and growth detached?
- m_ke 4y ago
  A lot of companies use other metrics to show growth, like github stars and package downloads
lumost 4y ago
I recently searched through the ML companies listed on https://topstartups.io/ https://topstartups.io/. One repeating pattern that surprised me is that there are few successful ML startups, the ones that are there don't seem to be remotely close to self-sustaining - or even product market fit in many cases. Why is ML such a struggle? are we overstating the impact relative to "old fashioned" data collection and analytics? Is the tech to expensive for companies to adopt in terms of man hours?
- joshcryer 4y ago
  A lot of people come up with an idea and then have no product. And investors are just happy to throw money at them. It's that whole idea that if you want to start an AI company, hire a bunch of people in India to do your work, then once the funding round is done, actually build the system. Hugging Face actually has a product and I think they'll be fine. I actually think $100 million is undervalued, because when I think "open models" or "training datasets" I think "Hugging Face." If you really want free money as an AI startup just say you're going to solve safe / friendly AI. People throw money at that without even showing anything. Hugging Face actually recently put out a job for someone who can work in "bias mitigation": https://nitter.net/mmitchell_ai/status/1520483233132990464 https://nitter.net/mmitchell_ai/status/1520483233132990464
  - Melting_Harps 4y ago
    > If you really want free money as an AI startup just say you're going to solve safe / friendly AI. People throw money at that without even showing anything. Hugging Face actually recently put out a job for someone who can work in "bias mitigation": https://nitter.net/mmitchell_ai/status/1520483233132990464 https://nitter.net/mmitchell_ai/status/1520483233132990464 This is the hardest part about going from Fintech to AI/ML for me, it all seems like it's vaporware and you really don't know where to apply your skillset from previous work in several Industries. My focus is on AI/ML based solutions for Supply Chain and Logistics, because it's already broken and needed a re-work for the last 2 decades. But I'm realizing that there is almost no way to raise these kind of funds and still have a viable business model beyond just setting arbitrary benchmarks and re-tooling your term sheet to reflect the 'new way' things are done in this space now. I'm still wondering if this twitter stream [0] is just a calloused AI guy ranting, or is it as prophetic as it was when I projected practically the same thing for enterprise based 'blockchains.' My dillema is wondering how Tesla and Mercedes are all trying to solve FSD, but at the same time we have things like Kiwibot which rely on people piloting them via Wifi from developing countries and pay them sub-standard wages ($2-3/Hr) to deliver food to affluent college kids on campuses. We already have doordash and all of it's incarnations that are bloated with VC money, how can this be a thing? 0: https://twitter.com/amanrsanger/status/1521654821211631616 https://twitter.com/amanrsanger/status/1521654821211631616
    - lumost 4y ago
      I'm becoming more suspicious that there is a chasm between what investors think ML can do, and what it can do. These trends never end particularly well, but by the same token - it's hard to tease out the real wins from the noise. Part of this is probably the software lifecycle. Everyone wants AGI, but AGI is always further than we think.

by m_ke 4y ago

hackernewds 4y ago

Interesting you mentioned revenue and not net income / profits. How are revenue and growth detached?

m_ke 4y ago
A lot of companies use other metrics to show growth, like github stars and package downloads

lumost 4y ago

I recently searched through the ML companies listed on https://topstartups.io/ https://topstartups.io/. One repeating pattern that surprised me is that there are few successful ML startups, the ones that are there don't seem to be remotely close to self-sustaining - or even product market fit in many cases. Why is ML such a struggle? are we overstating the impact relative to "old fashioned" data collection and analytics? Is the tech to expensive for companies to adopt in terms of man hours?

joshcryer 4y ago
A lot of people come up with an idea and then have no product. And investors are just happy to throw money at them. It's that whole idea that if you want to start an AI company, hire a bunch of people in India to do your work, then once the funding round is done, actually build the system. Hugging Face actually has a product and I think they'll be fine. I actually think $100 million is undervalued, because when I think "open models" or "training datasets" I think "Hugging Face." If you really want free money as an AI startup just say you're going to solve safe / friendly AI. People throw money at that without even showing anything. Hugging Face actually recently put out a job for someone who can work in "bias mitigation": https://nitter.net/mmitchell_ai/status/1520483233132990464 https://nitter.net/mmitchell_ai/status/1520483233132990464
- Melting_Harps 4y ago
  > If you really want free money as an AI startup just say you're going to solve safe / friendly AI. People throw money at that without even showing anything. Hugging Face actually recently put out a job for someone who can work in "bias mitigation": https://nitter.net/mmitchell_ai/status/1520483233132990464 https://nitter.net/mmitchell_ai/status/1520483233132990464 This is the hardest part about going from Fintech to AI/ML for me, it all seems like it's vaporware and you really don't know where to apply your skillset from previous work in several Industries. My focus is on AI/ML based solutions for Supply Chain and Logistics, because it's already broken and needed a re-work for the last 2 decades. But I'm realizing that there is almost no way to raise these kind of funds and still have a viable business model beyond just setting arbitrary benchmarks and re-tooling your term sheet to reflect the 'new way' things are done in this space now. I'm still wondering if this twitter stream [0] is just a calloused AI guy ranting, or is it as prophetic as it was when I projected practically the same thing for enterprise based 'blockchains.' My dillema is wondering how Tesla and Mercedes are all trying to solve FSD, but at the same time we have things like Kiwibot which rely on people piloting them via Wifi from developing countries and pay them sub-standard wages ($2-3/Hr) to deliver food to affluent college kids on campuses. We already have doordash and all of it's incarnations that are bloated with VC money, how can this be a thing? 0: https://twitter.com/amanrsanger/status/1521654821211631616 https://twitter.com/amanrsanger/status/1521654821211631616
  - lumost 4y ago
    I'm becoming more suspicious that there is a chasm between what investors think ML can do, and what it can do. These trends never end particularly well, but by the same token - it's hard to tease out the real wins from the noise. Part of this is probably the software lifecycle. Everyone wants AGI, but AGI is always further than we think.