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Andreessen-Horowitz craps on “AI” startups from a great height

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Re: Andreessen-Horowitz craps on “AI” startups from a great height

#161

Post by mapgrep »

inthewoods wrote: post #154

Having briefly worked for an AI company, I agree with the conclusion that AI companies are more like services businesses than software companies. I would add only one other thing: to me going forward there likely won't be "AI companies" - AI exists to power applications. And in my experience, unless the output is truly differentiated, customers aren't willing to spend more for something "powered by AI" - they just ex…

Aren’t software businesses increasingly like service businesses though?

They deliver now often with backend cloud storage, update near continuously, integrate frequently with outside services, sometimes open source major components iteratively, typically have an evolving API and developer ecosystem to educate, and are sold as subscriptions. It’s not as “human in the loop” as some of the AI described in this article but it’s clearly moving toward services in terms of margins.

Nothing is like the old shrink wrapped software business, basically.

Re: Andreessen-Horowitz craps on “AI” startups from a great height

#162

Post by jotakami »

ativzzz wrote: post #26

I agree with the author's opinion about > I’ll go out on a limb and assert that most of the up front data pipelining and organizational changes which allow for it are probably more valuable than the actual machine learning piece. Especially at non-tech companies with outdated internal technology. I've consulted at one of these and the biggest wins from the project (I left before the whole thing finished unfortunately…

I was a consultant at one of the giant outsourcers and nod my head vigorously at this comment. The least sexy projects were MDM (master data management) but they were absolutely essential to the success of any other fancy analytics/BI/ML project.

Re: Andreessen-Horowitz craps on “AI” startups from a great height

#163

Post by jorblumesea »

raiyu wrote: post #6

The number of places where machine learning can be used effectively from both a cost perspective and a return perspective are small. They are usually tremendously large datasets at gigantic companies, and they probably have to build in house expertise because it's hard to package this up into a product and resell it for various industries, datasets, etc. Certainly something like autonomous driving needs machine learn…

It's interesting that the industry constantly has to relearn the idea that tech needs follow business needs, not the other way around. As you said, so many teams rushing to containerize, but if the services you run are piles of junk, do your users care about whether kubernetes can scale based on memory instead of cpu? Similarly, many effective "recommendation engines" are just inverted indexes and not fancy ML models, and are a hell of a lot cheaper.

Re: Andreessen-Horowitz craps on “AI” startups from a great height

#164

Post by scottlocklin »

seibelj wrote: post #7

I wrote an article I published a week ago about how AI is the biggest misnomer in tech history https://medium.com/@seibelj/the-artificial-intelligence-scam... I wrote it to be tongue-in-cheek in a ranting style, but essentially "AI" businesses and the technology underpinning it are not the silver bullet the media and marketing hype has made it out to be. The linked article about a16z shows how AI is the same story ev…

Coming from a fellow masshole: that's a great rant.

There was this meme in the 70s about "self driving cars" following magnetic strips in the road in restricted highways. I remember at the time, being, like 8 and thinking "sure seems like an overly complicated train."

Re: Andreessen-Horowitz craps on “AI” startups from a great height

#165

Post by izendejas »

shoo wrote: post #159

> most people haven’t figured out that ML oriented processes almost never scale like a simpler application would. You will be confronted with the same problem as using SAP; there is a ton of work done up front; all of it custom. I’ll go out on a limb and assert that most of the up front data pipelining and organizational changes which allow for [ML to be used operationally by an org] are probably more valuable than t…

That's because, ML and operations-research problems can be simplified to set of optimization problems and the underlying math and statistics are all very similar if not identical in some cases.

And the input matters, a lot. So the differentiating factor isn't the models, it's the data and companies like Google figured it out a long time ago.

In short, find interesting problems, then the solutions -- not the other way around.

Re: Andreessen-Horowitz craps on “AI” startups from a great height

#166

Post by EamonnMR »

yubozhao wrote: post #62
scottlocklin wrote: post #24

Earlier quoted context omitted.

I suck at spelling. If I was one of the cool kids I'd claim to be dyslexic.

hi OP. We built an open-source library called, BentoML( https://github.com/bentoml/bentoml ) to make model inferencing/serving a lot easier for Data scientists in various serving scenarios. Love to hear your thoughts on our library

I was really hoping that you where about to offer an ML framework to improve spelling.

Re: Andreessen-Horowitz craps on “AI” startups from a great height

#167

Post by shoo »

inthewoods wrote: post #154

Having briefly worked for an AI company, I agree with the conclusion that AI companies are more like services businesses than software companies. I would add only one other thing: to me going forward there likely won't be "AI companies" - AI exists to power applications. And in my experience, unless the output is truly differentiated, customers aren't willing to spend more for something "powered by AI" - they just ex…

For an example of a genuine software company vaguely in this ecosystem, consider companies that build the tools that some AI/ML/optimisation systems use as building blocks. Eg optimisation algorithms.

If you need to solve gnarly industrial scale mixed integer combinatorial optimisation problems in the guts of your ML / optimisation engine, the commercial MIP solvers (gurobi , CPLEX ) or non-MIP based alternative combinatorial optimisation systems (localsolver ) can often give more optimal results in exponentially less running time than free open source alternatives.

1% more optimal solutions might translate into 1% more net profit for the entire org if you've gone whole hog and are trying to systematically profit optimise the entire business, so depending on the scale of the org it might be an easy business case to invest a few million dollars to set this system in place.

Annual server licenses for this commerical MIP solver software was 0(100k) / yr per server & the companies that build these products bake a lot of clever tricks from academia into these products that you can exploit by paying the license fee. ( my knowledge of pricing is out of date by about 7 years ) .

Re: Andreessen-Horowitz craps on “AI” startups from a great height

#168

Post by inthewoods »

mapgrep wrote: post #161
inthewoods wrote: post #154

Having briefly worked for an AI company, I agree with the conclusion that AI companies are more like services businesses than software companies. I would add only one other thing: to me going forward there likely won't be "AI companies" - AI exists to power applications. And in my experience, unless the output is truly differentiated, customers aren't willing to spend more for something "powered by AI" - they just ex…

Aren’t software businesses increasingly like service businesses though? They deliver now often with backend cloud storage, update near continuously, integrate frequently with outside services, sometimes open source major components iteratively, typically have an evolving API and developer ecosystem to educate, and are sold as subscriptions. It’s not as “human in the loop” as some of the AI described in this article b…

Not from what I see - what I see is software companies using services as a way to shorten time-to-value for the customer. They do this either themselves or via professional services firms.

To me, the services you describe are software-as-a-service - they scale well without adding more humans to the mix. Services businesses, in contrast, generally need more humans to do more work.

I do think you are right that we are entering an age where the margin pressures will continue to increase. As the Amazon quote goes "your margin is my opportunity." In that world, strength accrues to the largest players - which is why AWS is so strong.

I like to joke that AWS should refund money to the startup that buy booths at re:Invent only to find out AWS is rolling out a competing service (with the acknowledgement that AWS entering a space doesn't necessarily mean the end of the competing company.)

Re: Andreessen-Horowitz craps on “AI” startups from a great height

#169

Post by mrpidgeon »

fxtentacle wrote: post #58
m0zg wrote: post #8

"Huge compute bills" usually come from training, or to be more precise, hyperparameter search that's required before you find a model that works well. You could also fail to find such a model, but that's another discussion. So yeah, you could spend one or two FTE salaries' (or one deep learning PhD's) worth of cash on finding such models for your startup if you insist on helping Jeff Bezos to wipe his tears with cris…

No, also inference is quite expensive. You'll have 100% usage on a $10,000 GPU for 3s per customer image for a decently sized optical flow network. That's 3 hours of compute time for 1 minute of 60fps video. Now let's say your customer wants to analyze 2 hours = 120 minutes of video and doesn't want to wait more than those 3 hours, then suddenly you need 120 servers with one $10k GPU each to service this one customer…

"supervised learning => remember this

unsupervised learning => approximate this"

Lol this can't be more wrong lmao. Both areas "remember" and "approximate" things trough training. The difference is that unsupervised learning does not have labeled data, thus it has to search for some pattern. Honestly not even computer science graduates would say something like this.

Re: Andreessen-Horowitz craps on “AI” startups from a great height

#170

Post by bsenftner »

joshuaellinger wrote: post #76

I just spent $50K on coloc hardware. I'm taking a $10K/mo Azure spend down to a $1K/mo hosting cost. But the real kicker is that I get x5 the cores, x20 RAM, x10 storage, and a couple of GPUs. I'm running last-generation Infiniband (56gb/sec) and modern U.2 SSDs (say 500MB/sec per device). I figure it is going to take me about $10K in labor to move and then $1K/mo to maintain and pay for services that are bundled in…

I ran a ML based 3D reconstruction service for 7 years - given face photos of a person, reconstruct a realistic 3D likeness. I licensed a finished 3D reconstruction algorithm, purchased $50K worth of servers plus a federal reserve bank quality hardware firewall, and put it all in a Los Angeles downtown co-lo (the former Enron data center, actually.) I paid $600 a month to run that, as opposed to the equal compute capability being $96K per month if run at Amazon.

It kills me to see people being raped by the cloud, but everyone just lines up like good little boys...

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