The new business of AI and how it's different from traditional software - <AUTHOR> https://a16z.com/2020/02/16/the-new-business-of-ai-and-how-its-different-from-traditional-software/ <comment><USER3> About 6 years ago I invested in a ML as a startup company as an angel. The proposition was that they had a very nice workflow tool to take data, scrub it, partion it, build models against it, view performance and deploy it through and api. At the time there was nothing else out there really that was as polished and understandable to a business person. Then, very rapidly, the field got democratized. People were firing up notebooks, tensor flow popped into existence and their edge eroded. They were pitching to teams who had just hired data scientists who of course didn't want to buy something that made their job look easier! The other reality, which ties to this article, is that whilst they had to do a lot of hand holding with clients so the service cost became high although this wasn't custom model development but more training on the platform so they eventually worked out a way to package it in the sale as they got established. The company in the end changed course. They built out the product to focus on a single vertical that was getting traction in finance. This was huge contentious change and they lost some people along the way - but - it ultimately saved them from being wiped out. In the end the sold out to a much larger player in that vertical who was had no AI strategy and we all made a decent if not mind blowing return on our investment. Lots of lessons learnt on the way and it was a fun ride overall.

<comment><USER4> > They were pitching to teams who had just hired data scientists who of course > didn't want to buy something that made their job look easier!

I don't really follow. Nobody thinks a guy driving a forklift is working just as hard as a guy lifting crates by hand. But he's doing more lifting! Why wouldn't you want your job to look easier? The easier it is, the more you can do.

<comment><USER5> Imagine for a minute that you are new on Earth, first day, and you are in a car as a passenger on a highway. You watch the driver look, hold the wheel, use the brake and drive.. It seems very important what the driver is doing, so you notice many small details. However, you have no idea how much the car weighs ! or on a hill that force changes ! You watch the driver so carefully but have no idea about the basic weight of the car..

Now, you are a business person in a room with your thoughts. A presenter talks and you listen, but you watch the posture, the haircut, the order of speech .. very carefully But you have no idea about how the data moves, or the learning curve to use the tools, or even more how to innovate against "ordinary" .. you have no idea ! How could you.. so you watch the presenter carefully..

Basically, everyone has an idea of lifting a box.. so no matter how detached your life in the office is, you can appreciate a fork-lift. However, you have no idea about thirty years of *nix development, the toolkit evolution, the language wars.. etc

Here is the hard part -- many small-minded people (who run money) think NOTHING of learning.. its not important.. they care about control of the situation, and who gets the profit. Some leaders actually cultivate a smug disdain for "workers" .. some of those leaders have money.. etc...

Know this well - it is hard to believe how true it is, but perhaps you will find out over the years.

<comment><USER6> That's another perspective on bike shedding.

[http://bikeshed.com/](http://bikeshed.com/)

</comment></comment></comment></comment><comment><USER7> Ya know what, AI might be the most handy-wavy term ever adopted in the tech world (and this is compared to big data, IoT, blockchain, etc.)

No one really can really define what precisely AI is. Just in autonomous driving alone, there are 6 different "levels" of AI. If I write a decision tree algorithm, is that AI?

My point is, you can basically use the term AI to justify virtually anything when it comes to the value of software, which just makes articles like this not valuable at all. Why not just call it "this is what software is capable of doing for a business"?

One thing is for sure about "AI", and really just "advancements in software", is that repeatable human tasks are and will be replaced by automated systems. The only thing left for non-software business interaction will be to simply deal with other humans beings.

<comment><USER8> If you take a look at the papers in this field[1] you will see the almost never mention the words 'artificial intelligence'. It's 99% in the press, peddled by journalists trying to exploit every angle of sensationalism and fear.

[1] [http://www.arxiv-sanity.com/](http://www.arxiv-sanity.com/)

<comment><USER9> It's because "artificial intelligence" is a very broad expression and doesn't say a lot when you're in the domain. You won't find the expression "computer science" a lot either, it doesn't mean "computer science" is a sensationalist expression only used by journalists.

</comment></comment></comment><comment><USER10> This is a really good article. One company that I always think of when I think machine learning is the computer vision "startup" Clarifai. At one point in time they were cutting edge, filling the need for large scale image classification that enterprises had. This was when computer vision neural network architectures were rudimentary and hard to train (they still kind of are). Then in-house data science teams sprang up, tooling got better, better network architectures came out, and Clarifai essentially lost all of their edge over night. Machine Learning in itself is basically never the edge, it has to be a unique data set or sticky user base, something else that builds a moat.

<comment><USER11> I was one of the first few employees at Clarifai so I can add to this.

When Matt and Adam started the company there was no Tensorflow and outside of Hinton/Bengio/LeCun triangle nobody was doing deep learning yet. Matt just beat Google on ImageNet (around the time when he was doing an internship at Google Brain under Jeff Dean) and was one of the few experts in the field as he was lucky enough to have Hinton as his masters thesis advisor at UofT and did a PhD at NYU under Rob Fergus and Yann LeCun.

We had a clear technological advantage for about 2 years but thanks to the open nature of deep learning research (arxiv and willingness to open source code) the whole field caught up. Google giving out tensorflow and pretrained models for free made a bit of a dent as well.

A big problem with the "machine learning model as a service" business model is that each customer has slightly different needs and a different source of data so an off the shelf solution is usually not good enough. Because of that you end up being forced into doing consulting for large companies that don't know how to hire machine learning engineers. You end up spending months building them a model that they won't even know how to use and move on to the next customer.

IMHO there are only two viable AI business models right now:

1\. Spin out your research lab into a company and keep churning out papers without ever thinking about having real customers and make enough noise to get acquired by FAANG, ala Deepmind, Metamind, etc.

2\. Find a problem with a lot of repetitive manual labor and slowly wedge a machine learning model into the process. Design a good feedback loop by first augmenting the workers and use their feedback to keep improving the model until it's good enough to replace them. Doing so requires you to actually build a real product in that domain so you'll need much more diverse team than a paper mill. Most companies doing this shouldn't even call themselves "AI" companies since their customers don't care how the solution works as long as it solves their problem.

I believe that companies pursuing option 2 will take over a lot of large established players because building machine learning driven products requires buy in from the whole organization and it's not something that's easy to do at large enterprises. You need to be able to design the product in a way that helps you collect feedback, build data infrastructure to collect it and be willing to accept a solution that won't always be right.

<comment><USER12> Awesome to hear from somebody close to the source. There's a third option. There are entire industries where automation has not been practical because of the necessity for many hardcoded rules - problems outside of bland image recognition, including those solved by novel architectures like GANs and encoders and such. ML has finally gotten to the point where complex heuristics can be learned to automate those tasks which were impractical previously. Now you can legitimately write and sell ML services which meaningfully analyze, catalogue, and search data in ways that are currently human intensive, but just unique enough that regular programming won't cut it. There will be a proliferation of such businesses in the near future. The first wave is in development now. I've said it repeatedly, and I'll say it again, we are on the verge of an internet-like change in society. ML is poised to take human endeavors to new heights in the next decade...and if innovation and hardware continue to progress, I think the recent proliferation of the ML zoo and associated theory has given us the foundational tools for true AI which we may see in our [distant] lifetimes.

<comment><USER13> I am not sure what the difference between this and the "option 2" in the post you replied to. Some examples would be helpful. It seems to me both options replace human intensive tasks.

</comment></comment></comment></comment><comment><USER14> "AI companies simply don't have the same economic construction as software businesses"

Last I checked, "AI" is software.

Nowhere in here did I read anything about the fundamental truth of modern AI in production:

[AI] is a feature inside a successful product.

There are no "AI" companies, or "AI" products. There are companies which provide services to do inference on data, or in some cases tools/platforms so you can do it on your own.

They also confuse me by using the term services to mean bespoke Non Recurring Engineering efforts, instead of including something like a REST based API service that reflects most "pure" AI companies. Amazon Rekognition or MS Cognitive Services API are perfect examples of SaaS like AI services, but they aren't products exactly because they are used inside some other product as a feature.

At the end of the day, if you look at successful uses of AI in products they are one tiny piece of a larger product, that helps the product scale where it couldn't have before. That's pretty much the only place where it is proving very successful.

Even then, more and more of that is being done in house with the rise of things like Sagemaker and other turnkey ML inference tools.

<comment><USER15> I agree that this is a common misconception people have about ML and how it fits into our world. If you want to see successful ML products, you don't have to find some AGI stealth mode startup—just look at your phone:

\- Gmail's Smart Compose \- Netflix's Recommendation Engine \- Uber's ETA Prediction

ML functionality is becoming a standard feature in software, and in my opinion, that's the real "ML Revolution." As you mention, turnkey inference tools like Cortex (full disclosure/shameless plug: I'm a contributor) are making this accessible to virtually all eng teams.

<comment><USER16> The AI companies the article is referencing are trying to provide those examples: smart compose, Netflix recommendation engines, etc. to customer companies without an army of PhD's. However the article makes it clear that doing that in a general way is hard, much harder than a normal SaaS business.

<comment><USER14> It's a difference without distinction.

The services that ML based SaaS are offering are currently harder than the majority of other services.

However you could have said the same about any number of services/products over the years that were in the early adopter curve, which I will state confidently that we're still in for ML.

At the end of the day it's a hard SaaS just like VoIP and search and geoservices once were.

<comment><USER16> Yeah, the article doesn't say don't do a ML SaaS. Just that it won't have 80% margins like a typical SaaS business and you have to specialize to give you any sort of scale or it's just a Service business.

</comment></comment></comment></comment></comment><comment><USER17> I like the hard-nosed business evaluation of this article. However, the difficulties of a successful "AI" company are the same difficulties a "web" company had in the 90s. How many "World Wide Web" (back when we used that phrase) products survived the bubble? And how many people made a lot of money while creating software of very little value? People understood, correctly, that the "Net" (sorry, can't help my nostalgia) was a breakthrough technology that would change everything. But very few people understood how to use it effectively in that moment to provide business or consumer value.

The same is true today for deep learning models. The tech has advanced to the point where there are clearly products waiting to be built upon advances that are largely open-sourced (if not pretrained). But building a product that is useful hasn't changed from being a very difficult task to easy simply because a new technology exists. Moreover, as the article points out, costs in terms of dollars and manpower are currently higher than a making another web or mobile app.

All this should make hackers salivate. The higher than challenge and the more advanced the stack, the greater an opportunity becomes.

A useful way of thinking about this moment is that we have another platform to launch products from: first desktop, then web, then mobile, now "AI". Each platform enabled a new kind of product. Unlike the other platforms, which were essentially hardware mechanisms of software delivery, ML/DL is a software mechanism of delivering software. It is a fundamentally different way of creating software that produces fundamentally different kinds of software — the kind you couldn't make any other way.

The article is right to throw the cold-water of reality on the hype of AI. But the backdrop is one of breakthrough technologies that are just waiting to be leveraged.

<comment><USER18> > the difficulties of a successful "AI" company are the same difficulties a > "web" company had in the 90s.

I had a similar thought reading the comments here about AI hype. In the mid to late nineties, there was a lot of hype about the Internet, and a lot of smart people dismissed the Internet as nothing more than hype. In the end, though, the Internet changed our world. I suspect that, eventually, AI will do the same.

</comment></comment><comment><USER12> It is increasingly clear to me that vast majority of programmers do not understand the profound difference between modern Machine Learning and software development.

AI may just be a buzzword, but neural networks and ML are not. The tech has just arrived, this is fresh, evergreen territory, and the first wave of applied machine learning is quickly approaching, in quiet development across industries. Things are about to get _really_ interesting with the academic explosion of neural network architectures and theory, and I would strongly recommend to any developer to spend a few months getting up to speed!

<comment><USER19> > I would strongly recommend to any developer to spend a few months getting up > to speed!

As someone who spent more than a few months on a couple courses on various platforms, a few months is NOTHING in this field.

ML is not something where most people out of bootcamps can make a living out of.

ML is the ultimate winner-take all technology.

Sure, there might be some fast.ai examples of self taught ML scientists, but it is orders of magnitude harder than software development, mainly because of much longer learning feed-back -- you can't just printf or debug your code and expect to learn something new every 5 minutes.

<comment><USER20> > ML is the ultimate winner-take all technology.

Hence the flood of ML grads which will only get bigger and bigger as time goes on.

As a general observation of business, returns per unit of labor can range from completely linear (factories, hospitals) to more nonlinear in things like software or art. ML researcher is on the far end of this. Yes, Bengio and Goodfellow get paid 7 figures, but that's because even if you pay 100 amateurs 1/100th of that, they will still produce worse work.

If you want to be a good ML researcher, the time to get in was 10 years ago.

<comment><USER21> For broad research yes, I think there are still so many niches though where if you can combine domain expertise with good knowledge of ML you can achieve great results, simply because there will not be many people with this skill combination.

</comment></comment></comment></comment><comment><USER22> I wrote an article I published today about how AI is the biggest misnomer in tech history [https://medium.com/@seibelj/the-artificial-intelligence- scam...](https://medium.com/@seibelj/the-artificial-intelligence-scam-is- imploding-34b156c3537e)

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 a16z article shows how AI is the same story everywhere - enormous capital to get the data and engineers to automate, but even the "good" AI still gets it wrong much of the time, necessitating endless edge-cases, human intervention, and eventually it's a giant ball of poorly-understand and impossible to maintain pipelines that don't even provide a better result than a few humans with a spreadsheet.

<comment><USER23> Well said, the tech is not ready for what people are trying to use it for (for the most part). The problems people are trying to solve is still best served by clever algorithms + data.

</comment></comment><comment><USER24> This article really resonates with me: I'm currently working on bootstrapping an applied AI service and faced most of the challenges mentioned, to the point where I was doubting myself as I was not as efficient or productive as other Saas / Software founders. So this article is kind of reassuring to me.

A few more comments:

Cloud infra For a traditional web app you can quickly deploy it on a cheap AWS on Heroku VM for a few dollars/mo and later scale as you get more traffic. With AI you now need expensive training VMs. There are free options such as Google Collab but it doesn't scale for anything else than toy projects or prototyping. AWS entry point GPU instance (p2.xlarge) is at $0.900/hour i.e. $648/mo, and a more performant one (p3.2xlarge) at $2160/mo. Yes, you should shut them down when you are done with training but still. You can also use spot instances to reduce cost but it's not straightforward to set up.

For inference, you also need a VM with enough memory for your model to fit in, so again an expensive VM from day one.

Datasets if you rely on a publicly available dataset, chances are there are already 10 startups doing the same product. In order to have a somewhat unique and differentiated product, you need a way to acquire and label a private dataset.

Humans in the loop The labeling part is very tedious and costly both in terms of time and money. You can hire experts or do it yourself at great cost, or you can hire cheap outsourced labor who will deliver low-quality annotations that you will spend a lot of time controlling, filtering, sending back, etc.

For inference, depending on your domain, even with state-of-the-art performance you may end up with say 90% accuracy, ie 1 angry customer out of 10. that's probably not acceptable, but it gets worse: chances are you will attract early-adopter customers who are faced with hard cases, whose current solution doesn't work so that's why they want to use your fancy AI in the first place. In that context, your accuracy for this kind of customer might actually be much worse. So again you need significant human resources to control inferences in production. It will be hard to offer real-time results, so you may have to design your product to be async and introduce some delay in responses, which is maybe not the UX you initially had in mind.

I still think there are tremendous opportunities in applied AI products and services, but it's important to have these challenges in mind when planning a new product or startup.

<comment><USER25> How does it compare with building your own GPU-heavy computers? I'm not too familiar with it or how consumer-grade GPUs fare in these workloads, but training sounds like it in theory could happen locally easier than any consumer facing parts.

</comment></comment><comment><USER26> My greatest takeaway from fiddling around with machine learning for a past few years is this:

Most of the business use cases are deterministic in nature. It is really hard to shoe in a probabilistic response in such use cases, as illustrated in this article. It's even harder to convert this probabilistic response to a deterministic response (humans in the loop), at a cost comparable to just maintaining the deterministic system.

There are a few business cases that naturally are probabilistic - ad bidding, stock price prediction, recommendations. The giants have taken over this already.

I am still scratching my head to find a use case for a startup to jump in, use machine learning and solve a growing business use case. Probably there are some healthcare related use cases but I have no idea about that domain.

<comment><USER15> To me, the more interesting question with ML isn't "How can I build an entire business around a model," but rather "How can I exponentially increase a product's value with machine learning?"

For example, I don't theoretically need Netflix/Spotify/YouTube to have a great recommendation system. All I _need_ them to do is stream media to my device. However, their recommendation systems add immense value to their product for me.

A similar example would be navigation services (Uber/Google Map's ETA prediction, for example). I don't necessarily need those apps to give me predictions about traffic patterns—their core functionality is just giving me directions—but it makes the product much more valuable to me.

For those businesses, this increase in value leads to an increase in usage and therefore revenue—it's not simply an abstract "nice to have."

I think oftentimes the argument about the value of ML comes down to a binary decision of whether or not ML can 100% replace humans in a given domain, and that this is a flawed model. ML is capable of improving most products, in my opinion, and we're already seeing it happen.

<comment><USER27> I would take it a step further. What you're talking about is incremental improvements to traditional services or products.

What the poster above you is getting at, whether they realize it or not, is that they are trying to identify markets where ML could exist solely as the service or product.

I don't think the latter is ever going to be possible for ML much like that same comment alludes too.

Maybe makes ML a bit less sexy, but also is probably true of any innovative technology.

</comment></comment></comment><comment><USER28> I've revised the naivety out of their 3 points at the beginning of the article

\</comment><comment>--

In particular, many AI companies have:

1\. Lower gross margins due to heavy cloud infrastructure usage and ongoing human support ---> Their product is actually an army of expensive humans

2\. Scaling challenges due to the thorny problem of edge cases ---> Their products don't actually work

3\. Weaker defensive moats due to the commoditization of AI models and challenges with data network effects ---> No truely valuable technology or systems

<comment><USER29> It's important to keep in mind that most of the AI "innovation"/hype is coming from a handful of companies looking to make money from cloud computing or whose moat is built on data. The good old story of making money in a gold rush by selling tools...

I would venture that the most hyped stuff (deep learning) doesn't really work very well in practice, and the stuff that works reliably well is boring enough to barely register on the hype train as "AI". To be fair, that's probably an exaggerated caricature, but that is my reading between the lines of SOTA AI results.

Very few people (even among ML researchers) appreciate the fundamental limitations of associational reasoning (rather than causal reasoning). It's going to be an interesting time when this message actually sinks in...

<comment><USER30> I really like your point of this being "associational learning". There is a lot more to intelligence than just learning associations

</comment></comment></comment><comment><USER31> As they say, ML is written in python. AI is written in power point.

<comment><USER32> I mean you could write AI in power point ... [https://www.youtube.com/watch?v=uNjxe8ShM-8](https://www.youtube.com/watch?v=uNjxe8ShM-8)

/s

</comment></comment><comment><USER33> My favorite way of describing how AI is different:

      Traditional software: input + program = output
      AI:  input + output = program

</comment><comment><USER34> I don't think AI is a business.

There are lots of business workflows that AI can improve or enable in the first place, and you might be able to make a business out of _that_ , but you should be aware of the difference.

If you want to be in the business of enabling / improving business workflows, you should be very explicit about that, and AI could be one of many techniques in your tool belt -- but hopefully not the only one.

Starting an "AI business" sounds like "I've got a shiny new hammer, what nails can I hit with it?", which is kinda backwards from my understanding of how businesses should be run. It might work, but it's also easy to fall into the trap thinking AI should be your only tool.

</comment><comment><USER35> I think the main difference between AI and traditional software is that the latter is actually useful in the real world.

The place where AI has had the most impact is image recognition—and that's cool, but that's a very small part of what we use computers for. As someone who spends almost every waking hour in front a computer, I don't think I use any software that employs AI for any useful purpose except maybe search engines—and I'm pretty sure that they were a lot better before they used AI. GPT-2 is neat, but there's literally no circumstance under which I would want to use GPT-2-generated text for anything except experiments or toys. And while I'm sure Google makes good use of AI for spying on people and recommending YouTube videos to turn my brother into a nazi, I'd much prefer if they didn't do that.

<comment><USER36> I worked at an AI startup that pioneered modeling over inbound leads and Sales outcomes (most of this data is in Salesforce + Marketo/Pardot/Hubspot) and that application was hugely valuable for a few companies with massive inbound volume. Zendesk and New Relic were the poster child success stories, since they didn't have to massively scale their sales development teams to call on every mom and pop / student exploring a trial. It highlighted the 5-10% of their volume that could actually yield real money very effectively.

Unfortunately, there's not that many companies with that problem, and the market never materialized to the necessary degree and they sold the existing contracts and pivoted. That said, it was a home run for what it did.

If you find the right problem, AI can create massive value. The hard part is never the learning, it's finding and framing the right problem.

</comment></comment><comment><USER37> Interesting take. Someone who comes up with a good NLP AI though will probably be closer to 80% margins because you've got a shared model. Image processing too is usually built on top of pre-trained models and work surprisingly well. This is known as transfer learning. So I think we will see more online AI models.

<comment><USER38> I agree that cloud operating costs are lower for NLP AI, but for a different reason than you cite. I think they are lower because the type of data that they operate on is much more compact than AI that is operating on images, video, or audio.

</comment></comment><comment><USER39> "AI" products are consulting heavy as 80% of the effort is spent on data. Real world data inputs are messy, most need human curation up front.

Simple example: FMCG/Retail would like to use apps to scan shelves for their own product placement and their competitors. Simple, right? But getting those packshots, account for lighting and placement conditions, ... you need an army of people to do QC.

AI really just moves the analytics/BI load from a company to a vendor - <USER2> it is consulting. Good business to have, but human labor always has shittier margins than software.

</comment><comment><USER40> Isn't 99.99999...% of any currently used AI good ol' statistics or (not XOR) classical operations research stuff or (not XOR) well-known and rather simple NLP?

<comment><USER41> The more specific the use case, the more that human-engineered tools/rules will dominate. E.g. Regex beats BERT 99% of the time for any specific use case of value to a real customer, but BERT does a decent job in open domains without much effort.

<comment><USER40> Where is such 'decent job' part of the wonders touted as "AI", which widely successful product/service sold as AI-powered benefits from it?

</comment></comment></comment><comment><USER42> Over the decades that computers kept getting smaller, there were lots of new businesses, services, and products that were created thanks to this process.

Now that we're in a phase where we're making our DNNs deeper and deeper, what are some new successful businesses, services, and products that are springing up as a consequence? I can think of Amazon Echo. Any others?

<comment><USER43> How about the ability to search for objects in your pictures on your phone (granted, this is not a business in itself, but rather a new feature on existing products), use an online translator to translate almost anything in any language (including idioms) using something like google translate or deepL, fun niche products that give us a glimpse into what could be possible in the not so far future like AI Dungeon ... and that's excluding SaaS businesses that sell solutions to other businesses directly, so as a consumer you wouldn't really hear about it. I've worked at two of those companies in the past.

Machine Learning techniques have been applied to numerous different fields. Right now, they're often more subsidizing than a product in itself. But they are out there. And while I agree that a lot of companies are just throwing out buzzwords, it's definitely not all snake oil.

<comment><USER44> >ability to search for objects in your pictures on your phone

This is pretty cool, can save a lot of time in some cases, and doesn't disappoint because object recognition (even by humans) is unreliable by nature.

>use an online translator to translate almost anything in any language (including idioms) using something like google translate

That's debatable at best. Google Translate at least is still horrible, to the point where it is actually pretty much completely useless, at translating Asian languages to English. For longer texts, even German to English or Spanish to English produces a lot of nonsense, although it is usually at least somewhat intelligible if you're familiar with the sort of mistakes that Google Translate tends to make.

Anecdotally, I don't use Google Translate any differently than I did 7 years ago. It's good for single words or expressions, it's okay for single sentences if you are aware of it's pitfalls, it's pretty useless for large articles unless all you want is getting a general idea of what the article is talking about.

>AI Dungeon

I mean everything we've seen from AI Dungeon was hilarious because of it's nonsensical flaws. If those flaws can somehow be removed in the future we'll be stuck with a version where every individual sentence kind of makes sense but there is no coherent connection or story line, i.e. a product that nobody would use.

<comment><USER43> You definitely have good points, but since the post I responded to gave Alexa as an initial example, I kind of took that as baseline.

I can't really argue with anecdotes, but I can ensure you that a whole lot has changed in Machine Translation in the past 7 years, starting with Google introducing Neural Machine Translation[1] to replace their statistical model, which would often behave in the way you described. That's why I specifically included idioms, which hadn't really been possible until then. It's not yet perfect, but it's crazy good at what it does and only getting better.

I know AI Dungeon was really wonky, but I also said it gives us a glimpse of what may be possible. Because the way it interprets natural language is really something else (granted, that's the underlying model, of course). It's really a product still entirely in its infancy. And I don't think AI Dungeon will take the world by storm much more than it has done thus far, but I could imagine countless applications for a similar but improved technology.

I don't know, OP was asking for products and services, and I gave some examples. Are they flawless? No. Is most technology flawless? No. Will there be growing market for somewhat imperfect AI-based applications? I surely think so. In the end, humans aren't flawless either.

[1] [https://en.wikipedia.org/wiki/Google_Neural_Machine_Translat...](https://en.wikipedia.org/wiki/Google_Neural_Machine_Translation), [https://arxiv.org/pdf/1609.08144.pdf](https://arxiv.org/pdf/1609.08144.pdf)

</comment></comment></comment></comment><comment><USER45> I worked on the recommender and personalization stuff for a midsized fashion retailer. It took me about 2 years to figure out most of the work done by our ML & big data teammates was pure fiction.

I believe, but have no conceivable way to prove, that all of the easy wins have been won by the first movers.

It's weird. I have almost irrational optimism about the unbounded potential for deep learning, ML, big data, and so forth. Amazing, almost magical, stuff like KP figuring out that Vioxx was killing their patients once they had enough data and asked the right questions.

But I'm also completely skeptical about using these techniques for culture and entertainment and advertising. Methinks that ceiling has already been hit and there's nothing on the horizon.

Too bad. I really wanted our internal version of Stitchfix to actually work. Now I'm not sure it's possible. Or that we (or maybe just me) were asking the wrong questions.

</comment><comment><USER46> Are AI startups just a business model built around some proprietary code/software?

</comment><comment><USER47> Great article, so... the opportunities for innovators and investors are:

\- new OS paradigm: skunkworks in a garage somewhere in the world

\- new hardware paradigm: skunkworks in a garage somewhere in the world

\- new AI business: quietly disrupting a niche of a niche

</comment><comment><USER48> Just a side note, but please; on-premises, not on-premise.

<comment><USER49> Just a side note, but please, use a comma to separate two dependent and incomplete clauses, not a semicolon or colon.

</comment></comment><comment><USER50> data is the new oil!

</comment><comment><USER51> My prediction is that the first, really huge, AI breakout company will be the company that can figure out how to deploy AI to automate and scale data normalization/processing/integration pipelines and the resultant downstream features that drive value.

<comment><USER52> Is this satire? Because I can't tell anymore

<comment><USER53> I expected to read the keyword 'webscale' at some point but was left disappointed ...

</comment></comment></comment><comment><USER54> Ways to solve some of the issues mentioned:

1) Using containers, the SASS software can be downloaded to the client cluster and managed by Kubernetes operators. Hence the cost of training and storage will be bare by the clients themself and not by the SASS company.

2) Second, the use of AutoML should increase the productivity of the startup employees (especially with the ongoing retraining of models, deployment, monitoring, etc).

The one problem that will always be there is new data and edge cases in the data. I do believe that this would be the major obstacle for the next 5 years.

Also, I would expect to see the number of models actually explode (assuming that they are trained and deployed by AutoML). Case in point is Uber with models per city/ time of day (with 1000's of models in production).

<comment><USER55> This doesn't really work as most AI Modeling is very resource intense (GPU / TPU etc.) that most business aren't going to have.

<comment><USER56> This can be circumvented through the use of transfer learning and super convergence.

You can now train a State of the art image classifier with nothing more than a google colab notebook (free to use).

That's for deep learning. For classical ML (I.e. XGboost) it really doesn't take much time or compute to create a model that has business value</comment></comment></comment>


Show HN : small weekend project for your suggestions / criticism - <AUTHOR> http://www.tmemo.me

<comment><AUTHOR> this is a small project, a kind of "post-it notes" for twitter. we tried to put some "polish" on its look and feel, but any suggestions to improve it, any criticism are welcome. Tx.</comment>


Ask HN: How to promote a B2B StartUp? - <AUTHOR>

If you had a B2B StartUp (Service) what are some of the ways you use to promote your service?
So far I am using conventional methods
- Google Ads - Facebook Ads
I have a couple of other ideas such as conventional print (trade magazines) etc but I want to understand what your experience has been (hopefully one I can learn from)? Please share your experiences. We might be able to ping some ideas back and forth.
Thanks in advance <comment><USER2> There's quite a few ways, here are a few that we're using for our new app (www.yobiz.com):

* Affiliate Networks - Affiliates who target your markets can really drive qualified traffic.

* Blog posts - good, high quality blog posts on topics relevant to your business can drive good traffic.

* Email Newsletter - Market either to established email newsletters or generate a list from your existing customers

* Facebook Ads

* B2B Forums - Participate in the discussions but be cautious about self-promotion

* Get featured in an article - Spend time cultivating press relationships in order to get featured in press.

* LinkedIn Posts

* Local meetups - Attend local meetups in relevant industries and network, network, network

* Mobile Apps - Create a mobile app that drives subscriptions.

* Press releases - really good for SEO and not much else.

* SEO - good SEO will really help make your app visible.

* Social bookmarking: Digg, Reddit, delicious, etc. Activity aiming to get website posted onto mass-sharing networks.

* Attend conferences & trade shows

* Tweet - be part of the conversation and generate a following with occasional self-referential tweets.

* Video - Video on relevant topics with good quality can really drive traffic.

Hope that helps!

<comment><AUTHOR> Thanks for the extensive list.

Email newsletter is not really working for me since I only have a handful of customers at the moment. I do not want to go ahead and buy a list and spam people either since I find that rather unethical (I hate spam as much as the guy next door).

One thing I have not yet tried is the Forums. In terms of B2B Forums, is there a starting point that would let me do a more directed path or is Google my best friend in your opinion?

As per the SEO I am seriously considering doing that. Although most small businesses would not directly search for the service (since most of them are not aware of the need) I still think it would help greatly to rank on the first page. So I will definitely consider the Press Release.

Again, thanks for the input, I appreciate it.

<comment><USER2> You're right to avoid email spam -- not only is it frowned upon, but it could fall afoul of the FTC CAN-SPAM rules. I was referring more to getting included in established email newsletters (not dedicated email blasts). For example, your industry might have a weekly newsletter targeting the things you are interested in. See if you can get included in that (for free, optimally), or take out a low-cost text or other ad in the newsletter. They usually go out to already qualified, opt-in audiences and have a better response rate than other forms of advertising.

<comment><AUTHOR> Yep, totally agreed. I will look into different email newsletters. In the mean time like I replied on another comment, the service is not for a specific vertical. It's more of a shared IT kind of service that targets very small businesses (low-cost service).

Do you have any pointers for me to take a look into specific newsletters?

<comment><USER2> For both forums and email newsletters, Google (or Bing if you must) are your friends -- just do search "[industry] newsletter" and "[industry] forum". Yes, it takes some time to do the research, but you'll end up with a good pile of resources that will be helpful.

Subscribe to the newsletters you find and read the forums -- it's another great way of finding out where you should market, hang out, and who to talk to.

</comment></comment></comment></comment></comment><comment><USER3> It likely depends on what kind of B2B service you are promoting, the size of the companies you are targeting, and how deeply your service needs to integrate with the target customers' existing software systems. In my experience marketing to businesses via the same channels that work well for consumer web products is no easy task.

Give serious consideration to making a real effort to discover, meet, interview, get to know, perhaps even partner with a talented salesperson with great personal and/or professional connections in the industry you're trying to reach.

Here are a couple posts that you might find useful:

      http://www.gabrielweinberg.com/blog/2010/09/sean-murphy-on-the-first-1-6-enterprise-customers.html

      http://www.walkersands.com/Comm_Selling_to_CIOs.htm

</comment><comment><USER4> It depends on what kind of business you are targeting-- what's the niche?

If you have a clear idea of who your target customers are, you can intuit what methods would be most appropriate to use to promote your service to them. For some niches, this could be cold calling. For others, there might be trade shows or publications.

In my previous startup, we had a tiny, very focused niche (where about 25 firms were responsible for more than 75% of the revenue in the industry), so we did no marketing at all, and instead focused on cultivating personal relationships with the decision-makers of the top 10 firms. But that approach would be wildly inappropriate in other situations.

So: it all depends.

<comment><AUTHOR> The niche are 1-2 people service firms that work with clients on a schedule. So this is more of an infrastructure utility for a specific type of company rather than an entire vertical. For cold calling did you use a firm to do this?

<comment><USER4> In your case, cold calling would be very inefficient.

Have you thought about targeting a sub-niche, to try to get some traction, before going broad?

For example, Dentist's offices: if you were to call and visit a few local dentists, see if there are any customizations that would help them specifically (like recurring appointments a year in advance, etc.) and then brainstorm with them to see how they would recommend you advertising to their colleagues.

Once you get some traction in one vertical, you can leverage it to the next one.

<comment><AUTHOR> Honestly, I think that is the best approach. Because the market is so broad, it will be hard to reach out to everyone based on company size. I am currently focusing on 2 segments incidentally one of them is dentists the other is accountants.

Thank you for the links.

</comment></comment></comment></comment><comment><USER5> sourc3, Try www.efactor.com. It is a social network for entrepreneurs, and there are already 800,000+ users. You can connect with the businesses you are trying to target.

Disclaimer: I just got hired there</comment>


Orchestra: An open-source system to orchestrate teams of experts and machines - <AUTHOR> http://orchestra.unlimitedlabs.com/ <comment><USER2> Hi, Hacker News!

Today we're excited to be making our first contribution back to the tech community by open sourcing Orchestra, a system for coordinating project teams of experts and machines. It can be used to reimagine and implement all sorts of workflows, from professional services (like legal, design, and data analysis) to new product experiences (like M from Facebook). Orchestra is based upon years of research into machine-mediated expert teams. We hope you'll come up with new and creative ways of using it!

At Unlimited Labs, we're excited about building a brighter future of work and are currently building our first product on top of Orchestra. We'd love to answer any questions you have on here, or feel free to email the team at email@example.com!

</comment><comment><AUTHOR> (Orchestra dev here)

If you're curious how it can be used, Daniel Haas put together a wonderful example of how Orchestra could be used in a newsroom:

[http://orchestra.readthedocs.org/en/latest/example_use.html](http://orchestra.readthedocs.org/en/latest/example_use.html)

Happy to take questions!

</comment><comment><USER3> This looks absolutely wonderful. A few random thoughts:

1\. I'm going to play with this as a way of documenting processes. Being able to answer "how do you develop features" or "what's best practice for X" with a usable process is great.

1 a) I'm particularly interested in using this for documenting how to perform experiments, and tracking experiments as they go.

2\. I think this is amazing for progressively automating things. Split the task up into manageable, clear chunks to be performed by people. Each step can then (if it seems worth it) be replaced by code.

3\. I don't think this is present at the moment (and it probably shouldn't be for a first release) but I'd like to see process hierarchies and importing others. I'd like to say 'Use github's "GITFLOW V2" process for managing feature additions and use "MY_COMPANY_QA V3" for reviewing features before full release'.

Anyway, this is great, and I look forward to playing around with it.

edit -

Even without these, I can already see myself using it just for me.

edit 2 - It'd be good to work on the getting started, so that there's just a "run this script" or "clone this repo". There's a lot of "download this file and copy it here" that feels like it should be scripted.

<comment><USER4> Thanks for the feedback!

Your comment about embedding external QA workflows is super interesting. Right now Orchestra does support what we call 'Review Policies', which allow you to specify that a given workflow step be reviewed k times, or with probability X, or by specific expert Z. But it would be interesting to think of whole workflows for QA.

Re: getting started, yeah, we're working on a script--turns out cross-platform support is hard!

<comment><USER3> > Right now Orchestra does support what we call 'Review Policies', which allow > you to specify that a given workflow step be reviewed k times, or with > probability X, or by specific expert Z.

That's handy, I can think of a bunch of things that fit well with that (particularly review a subset of x%)

> Re: getting started, yeah, we're working on a script--turns out cross- > platform support is hard!

Hah, yeah it is!

I've got a version setup now, but am struggling a bit to setup workflows & figure out quite what goes where, but I think I've found the right bit of documentation: [http://orchestra.readthedocs.org/en/latest/example_use.html](http://orchestra.readthedocs.org/en/latest/example_use.html)

As a suggestion, if you add a new example, try doing it step by step of what you need to add & do. It's more work, but I find it really helps show what someone will actually experience while creating / updating workflows.

<comment><USER4> We have an issue open to improve the journalism exaple: [https://github.com/unlimitedlabs/orchestra/issues/32](https://github.com/unlimitedlabs/orchestra/issues/32). If you have concrete suggestions for how to make it better, we'd love to hear them--especially for documentation, which can always use work!

Also feel free to reach out to us on gitter ([https://gitter.im/unlimitedlabs/orchestra](https://gitter.im/unlimitedlabs/orchestra)) if you're having trouble getting your local setup to run the workflow and we can talk through the details.

</comment></comment></comment></comment><comment> usman-m Great stuff! For now it seems like this leaves out the job of finding crowd workers. Could it be possible to do something like designing microtasks in MTurk and "stitching" the workflow together using Orchestra?

<comment><USER4> (Orchestra dev here too!) That's a great question. We're hoping that emerging online expert communities like Upwork or Dribbble will make it easier and easier to find experts suited to the work, but it's definitely not a solved problem (or tech recruiting would be so much easier!).

Re: MTurk, there's nothing stopping you from stitching microtasks together with Orchestra, but I think that undersells Orchestra's strengths (e.g., collaboration among workers) a bit.

</comment></comment><comment><USER5> This might seem a dumb question, but can you talk about how this improves on existing productivity workflows? Does it integrate with tasking systems like Jira or is this meant to be a complete a replacement?

It seems high-level (and great) I'm just curious what implementing it "in the field" would look like.

<comment><USER4> Products like Jira help organize tasks within workflows, but don't automate any that organization. Orchestra takes care of things like "which reporter should I assign to a new story?" without requiring humans to make those decisions.

Research from the Flash teams project that I mentioned in another comment ([http://stanfordhci.github.io/flash- teams/index.html](http://stanfordhci.github.io/flash-teams/index.html)) has shown that this type of automation can decrease end-to-end workflow time by as much as 2x!

</comment></comment><comment><USER6> Does this tie into products like PivotalTracker or JIRA to make it useful for agile software development?

It would be nice to be able to create tasks from stories in Pivotal, assign a reviewer to them, and, when a reviewer thumbs-up, have it automatically built into a staging server. Anything like that in the works?

<comment><USER4> We haven't built that integration yet, but it's totally possible! Really all it should take is a Machine Step that calls out from Python to, say, Pivotal's APIs ([https://pypi.python.org/pypi/pivotaltracker](https://pypi.python.org/pypi/pivotaltracker)).

</comment></comment><comment><USER7> This seems very cool - are there case studies of adopting this type of workflow for complex projects?

<comment><USER4> I don't know of any formal case studies, but Adam Marcus and myself (two devs on the project) worked on a large-scale workflow at GoDaddy where we automatically organized data extraction experts into hierarchies of review. That work actually got published at a Databases conference: see Adam's nice summary here: [http://blog.marcua.net/post/128120315677](http://blog.marcua.net/post/128120315677)

On the research side of things, the flash teams work by Daniela Retelny at Stanford ([http://stanfordhci.github.io/flash- teams/](http://stanfordhci.github.io/flash-teams/)) has done great proof-of- concept things like build an app in a day with distributed teams.

</comment></comment><comment><USER8> This looks really exciting, and I can't wait to try it. I think it will be very helpful for my business.

</comment><comment><USER9> i think this looks potentially great - would definitely benefit from more real world examples of workflows

</comment><comment><USER10> This seems very cool!!!

</comment><comment><USER11> Are musical instruments provided?

<comment><USER4> They don't come in the box, but you're free to hire musicians for your workflows!

</comment></comment><comment><USER12> This is very exciting!</comment>


Without mobile, Adobe Flash is irrelevant - <AUTHOR> http://www.zdnet.com/blog/perlow/without-mobile-adobe-flash-is-irrelevant/19247

<comment><USER2> This is all horsecrap. Adobe flash never ONCE ran successfully on my HTC Evo. It is absolute garbage and is never useful. IDK why anyone mentions "we support flash on our phones/tablets"... its all crap. It barely works if it even does. Read any tablet review including playbook about how "wonderful" flash is there.

Dropping support is basically saying... "oh yea, since nobody is developing for it AT ALL and no system can successfully give a decent experience with it, we'll just stop supporting it since it won't affect anyone wanyways."</comment>


How do I develop skills necessary for a job as a programmer at a startup? - <AUTHOR> <comment><USER2> You could start with one of the many courses from the following sites:

a)[https://www.edx.org/](https://www.edx.org/)

b) [https://www.coursera.org/](https://www.coursera.org/)

c)[https://www.udacity.com/](https://www.udacity.com/)

Projects + Github + Blog would be all that you really need. Example from Udacity you could take one of the many specializations:

 _Android:[https://www.udacity.com/course/android-developer- nanodegree-...](https://www.udacity.com/course/android-developer-nanodegree-- nd801)

_IOS: [https://www.udacity.com/course/ios-developer-nanodegree-- nd0...](https://www.udacity.com/course/ios-developer-nanodegree--nd003)

*Front End Developer: [https://www.udacity.com/course/front-end-web-developer-nanod...](https://www.udacity.com/course/front-end-web-developer-nanodegree--nd001)

And many more

While learning try to do bite size projects that can also be portfolio items that you push to Github. When times get difficult and it appears that Stackoverflow and other online sources will not able to answer your questions, do not be afraid to jump onto one of the many IRCs

</comment><comment><USER3> I agree with the comment below, specific skills will be different depending on what company. I came into programing and CS from concert Lighting 5 years ago. I started learning iOS however if I did it again I would learn something like Javascript/ html of Java first to get an understanding of the concepts. Check out Code Academy.

Also I would get an idea of what kind of role your after and learn the relevant language.

If you are interested in basic programming courses and have the time the Stanford courses (CS106A and CS106B) which are on iTunes U are good starting points.

Hope that helps.

</comment><comment><AUTHOR> Thanks for your suggestions.

Maybe I should elaborate on my background a little more. I recently completed my Masters in Computer Engineering from University of Florida and am working at Ebay in Philadelphia. I know my basic algorithms and data structures, and can confidently write programs for implementing them.

I would like to work as a full stack developer in a start up. The technologies I am interested in developing my skills are : Java, REST API programming, Javascript, HTML, CSS.

Could you suggest good sources online or otherwise to develop my skills in these areas?

</comment><comment><USER4> 1\. Learn to program for fun. Forget you are looking for a job at a startup and build something you like. Having said that if you can learn HTML, CSS, Javascript, JQuery, Angular it would make you very marketable.

2\. Apply for jobs on a daily basis, searching for jobs listed in the last 24 hours.

Don't work for free (just do more open source rather than doing that)

If the above doesn't work expand your job search to include all companies not just start ups. Also expand to other cities if required and be prepared to relocate.

</comment><comment><USER5> Become an autodidact on your own time.

That means investing money and time into your own education and skills. Then, when you have the skills, you can apply at startups with examples of your work.

The same way artists create art for their gallery/portfolio and show that portfolio before getting a job. Programming is an art - you should have a portfolio.

That means you need to invest time into creating that portfolio.

</comment><comment><USER6> You'll need to be more specific, since there are startups in many different businesses. Some startups sell t-shirts to people on the web, some implement messaging services on mobile devices and still others build custom hardware with embedded software. The programming needs of these businesses are very different.

</comment><comment><USER7> First, Learn to love code. Pick a language. Start here. [https://projecteuler.net/problem=1](https://projecteuler.net/problem=1)

Then projects + github + blog + never giving up.

</comment><comment><USER8> Having some side projects, being active in communities (Stack Overflow, Github), and having a constant thirst for learning new technologies have been the keys to developing my skills and acquiring jobs in startups.

</comment><comment><USER9> I've been enjoying [http://www.freecodecamp.com](http://www.freecodecamp.com).

<comment><AUTHOR> Thanks for the link, it looks very interesting.

</comment></comment><comment><USER10> First, figure out how to solve problems.

</comment><comment><USER11> Build something small, anything, from scratch and launch it.

</comment><comment><USER12> What you should do is pick one stack to work with, and become good at it.

It's okay to not like the stack you picked, as long as it's popular. But learning is hard, so stick with whatever you picked to the point where you have a mobile app, or a web app on a server with a rest API that your front end code uses.

Some stacks you might look at: Have a new Macbook and an Iphone? learning swift and objective C and picking up IOS programming will get you work. Have an Android phone, and any sort of computer that's moderately fast? Learn Java and becoming an Android Developer.

But maybe you want to do a web stack. You generally want some experience on the back and and the front end. When in your learning, you don't know what to use out of a bunch of tools, pick what is most widely used, and if it's a tie, go with the older thing, or the simpler thing.

You will be learning Javascript, as well as HTML and CSS for the front end dev work, so for the back end Node.js, which is a server side javascript platform, is a good thing to learn. Express is one framework, but it's most popular, and you want to pick up a popular stack.

Like Python? Learn Django. If the company you want to work for uses a microframework like Flask or something, they will still probably hire Django devs, but the opposite may not be true.

Like Ruby? Ruby on Rails isn't the only framework, but it's super popular, and you want to get the skills, right?

Learn to use the command line (Mac, Linux, or cygwin if you have to stay on windows) to do things. Sign up for Amazon's free 1 year of cloud services, so you can practice setting up stuff over ssh and using git.

Learn git, enough to be able to put projects up on github, and to get them down onto your server.

In terms of front end, learn enough jquery to interact with a REST API and get data from it into your app. Consider a front end framework like angular. Learn bootstrap, which is a CSS framework, because again, its popular.

It's not necessary to understand big O or data structures in detail for a startup job, but it might be to get hired. Knowing SQL is probably helpful eventually, but don't worry if you don't.

Why do I keep telling you to pick the popular thing? Because startups want people who may not know the stack, but know how to use a stack and accomplish something. You can get hired to do android dev knowing only IOS, or ruby on rails knowing django, or using react and backbone when you know angular.

Learning to program is hard. Try to get in an hour or two every day. Once you know how to make something specific in a specific stack, branch out and learn more broadly.

Good Luck.

</comment><comment><USER13> Other startups.

<comment><USER4> How do I develop skills necessary for a job as a programmer at other startups?</comment></comment>


SpaceVim – Use Vim as a Java Ide - <AUTHOR> https://spacevim.org/2017/02/11/use-vim-as-a-java-ide.html <comment><USER2> Having tried to write Java in vim for a few one-off tasks, it's been extremely painful. From what I've seen, most Java code is selected from a menu, rather than actually typed. So I can see the appeal of having that in vim if you're forced to write Java, but at the same time the whole "use vim as an ide" meme has been nudging me away from vim for a while now.</comment>


Synthetic prawns: a bid to make 'seafood' that's sustainable and slavery-free - <AUTHOR> https://www.theguardian.com/environment/world-on-a-plate/2016/aug/26/synthetic-prawns-a-bid-to-make-seafood-thats-sustainable-and-slavery-free <comment><USER2> Personally, syn-foods have the same problem as humanoid robots: the uncanny valley.

My daughter is vegan and has introduced us to all manner of syn-foods, like chicken and beef based on soy, and cheese based on nuts. In some dishes, particularly those that are significantly spiced, I'd fail a blind taste test. But as soon as I know its syn, I am repulsed. If I can tell it's not right while eating it, it's really hard for me to finish.

It might be just me and my focus on what exactly I am chewing on, but in order to be environmentally (and morally, some argue) responsible, I'll have to just be kept ignorant and not know when I'm eating a synthetic food.</comment>


Ask HN: Is the WSJ's "Pro Edition" a good business model? - <AUTHOR>

The Wall Street Journal is charging extra for a "professional" subscription with access to premium features.
From TechCrunch:
"Dow Jones debuted a premium business news site dubbed The Wall Street Journal Professional Edition in an attempt to get companies to pay up $588 a year for access to more personalized, business-related news and analysis."
See: http://techcrunch.com/2010/04/08/wall-street-journal-professional-edition
Does anyone know how well this "super-premium" business model is working for WSJ?
I keep seeing links to articles on WSJ.com marked up with the "Pro" tag, and it got me wondering whether or not this was a viable monetization strategy for other business-focused content sites.
For instance: why doesn't Google have a similar "Pro" edition of Google Finance? Is it because "freemium content" is a poor business model? <comment><USER2> One indicator of possible success would be seeing its major competitor, the _Financial Times_ , follow it. I'd have to wonder if Google Finance is even in the same business, e.g. unlike the WSJ or FT it doesn't charge "subscribers" for regular content at all.

The WSJ has some free articles and lots more if it thinks the link is redirected through Google (perhaps the Pro Edition articles don't allow that???), the FT only allows you to read 3 articles per month without free registration, then at allows a bit more than a dozen per month the last time I checked.

One thing to look for is non-delayed market info, you have to pay a lot more to get it without a 15 minute or whatever delay.</comment>


A limiting factor on producing electricity in a warming world? Water - <AUTHOR> https://arstechnica.com/science/2017/07/a-potential-climate-change-consequence-not-enough-cool-water-for-power-plants/ <comment><USER2> There is plenty of sea water for cooling and for the boiler, so not really an issue.

The power station I worked at gets all its boiler water from its own onsite desalination plant in conjunction with a cation and anion plant for polishing.</comment>


Ask HN: What is the most rewarding outdoor or physical activity you do?  - <AUTHOR>

I'm trying to find new outlets for exercise and km curious what others enjoy. <comment><USER2> Road cycling. Its the only exercise I get that doesn't feel like a chore/work. My bike has been in the shop for the past 3 weeks and its been painful to go running. Cant wait to get my bike back!

</comment><comment><USER3> Speed Skating (ice).

</comment><comment><USER4> TRX for me :)</comment>


Ask HN: Feature request: Notification when I get a reply to my comment. - <AUTHOR>

Similar to the orange envelope in reddit. Apologies if I missed it. <comment><USER2> The only thing you missed was the feature request link:

<http://news.ycombinator.com/item?id=363>

(in other words, the feature you're requesting is not currently in the site)</comment>


Rent control will make housing shortages worse? - <AUTHOR> https://www.economist.com/leaders/2019/09/21/rent-control-will-make-housing-shortages-worse <comment><USER2> The housing problem is because CA et al fight new construction.

This does far more to hurt renters than anything else: [https://www.latimes.com/local/lanow/la-me-ln-housing-bill- co...](https://www.latimes.com/local/lanow/la-me-ln-housing-bill- council-20180326-story.html)

<comment><USER3> San Francisco has 100,000 vacant homes and 10,000 homeless people.

[1] [https://sf.curbed.com/2019/3/21/18276227/vacancy-rate-san- jo...](https://sf.curbed.com/2019/3/21/18276227/vacancy-rate-san-jose-san- francisco-lendingtree)

[2] [https://abc7news.com/society/homeless-population-history- in-...](https://abc7news.com/society/homeless-population-history-in-bay- area/5260657/)

<comment><USER4> Though you messed up the basic numbers, I think that the larger point needs addressing.

Why are these homes vacant? Should people be allowed to take a month of vacation without losing their home? Is it ok for an apartment or house to be empty for a few months in between residents?

One month in between tenants every two years is more than a 4% vacancy rate. What should the vacancy rate be?

And what if in addition to housing the homeless we also want to house workers with unreasonable commutes?

Every single person I know who has raise this X vacant homes and <X homeless has not bothered to ask these very basic questions.

We need more homes. 25% more than we currently have, in the high demand areas. We don't need hem in Detroit or Stockton, we need them next to jobs. And we need to stop the profiteering home owners who speculate on the value of their homes by blocking others from living close to them.

<comment><USER3> LA and Boston did this and now just have vacant high-rises to show for it. And, really, greedy homeowners are the least of our worries compared to real- estate developers and landlords.

I've seen the idea of a vacancy tax thrown around. Not sure how effective it would be, but might be worth trying.

<comment><USER4> For a city of 4 million people, LA is building something like 9 thousand apartments [1]. That's not a 25% increase in the number of homes, that's nothing at all. It doesn't cover the number of births, the number of immigrants. Building that few homes is a plan to displaced people from the city.

It's time to stop treating housing as if numbers don't matter, and the only thing that matters is our feelings about seeing a crane.

There is a massive housing shortage, and people that deny it are just as fact- free as climate deniers.

[1] [https://www.reddit.com/r/urbanplanning/comments/d0a9wb/top_2...](https://www.reddit.com/r/urbanplanning/comments/d0a9wb/top_20_us_metros_by_projected_new_apartments/)

</comment></comment></comment></comment></comment><comment><USER5> All these comments and threads fail to understand the purpose of rent control. It is not to provide the most efficient housing to new residents but to protect existing residents from being priced out.

Until there's a better solution than "tough shit" these policies will continue to be popular.

<comment><USER6> > Until there's a better solution than "tough shit" these policies will > continue to be popular.

Why do we need a better solution than "tough shit"?

Not everyone is entitled to live where they want. If you want that, try Europe.

<comment><USER7> You say that but the vast majority of housing being built in CA is "luxury" accommodations. Luxury homes, luxury apartments, luxury condos. Nothing else justifies the cost of land, the developers say, and hence nothing else gets built. Meanwhile short-term rentals are cannibalizing existing housing stock. I was born here, and like hell would I tolerate some random fuck telling me I'm not entitled to live here.

You're saying the only people entitled to live on desirable land are wealthy assholes coming from afar? "I'm sorry, the only place you're entitled to live at your income level is that shithole next to the meatpacking/chemical/whatever plant. Why are you complaining? You can live with all your other equally-abled friends!"

This is how you create ghettos.

<comment><USER6> > You're saying the only people entitled to live on desirable land are wealthy > assholes coming from afar?

Rich or poor, asshole or not asshole: No one is entitled to live anywhere.

There is only a market price. That's all.

<comment><USER7> Means is a form of entitlement. Free market economics creates massive inequality. That's the whole issue at stake here.

<comment><USER8> But the other side of the argument is that if somebody worked hard, and obtained monies, they should be able choose where to live using said monies. If i found out that wealth gained means nothing, and that the 'mob' penalizes wealth, then people would tend to create less wealth. Would society be worse off then?

<comment><USER7> I don't know but Europe seems to be doing OK

</comment></comment></comment></comment></comment></comment></comment><comment><USER9> Yeah! I should have rent control! I should be able to live where I want and pay below the market value. Screw the free market, I want to live in someone else's house and pay them less than it's worth. I don't care if their costs go up due to increased maintenance costs, or inflation, or wage increases. I don't care if the value of their property increases due to basic, fundamental, elementary economics like supply and demand, and their property taxes increase. Screw them. It's my right to live where I really really want to live. I especially don't care that nobody else can move to where I live because there are no new houses being built, and therefore no new money into the local economy. I'm not moving to a more affordable area, why should I? And I'm definitely not going to save money for a deposit so I can borrow money at some of history's lowest ever interest rates. Then I'd be working for the man. Screw that, I've got avocado toast to pay for.

<comment><USER10> I feel like this comment is way below par for HN.

<comment><USER11> It is successful at showing the root cause: people don't want to change.

</comment></comment></comment><comment><USER12> Absolutely. A market adjusts far more rapidly to match supply and demand than any committee ever could, especially as groups of people that set prices have agendas _other_ than seeking movement to a balanced price.

<comment><USER13> Hmmm, I wish that were true. It seems like it would be, but my (Canadian) province's two major cities are showing how glacially slow the market is reacting to an oversupply. Cheap interest rates mean that for a lot of builders who overbuilt, it's cheaper to pay the 2-3% interest for the year than to lower prices 10-20%. Our prices went up like a rocket around 2006-2008, and are very very gradually dropping back down. There was, of course, no significant salary increase in the same time period, or since then, so entering the housing market for first time buyers has been much more very difficult than it was in the past.

<comment><USER14> Which cities are these? It's (I think) pretty well known that housing prices are a little sticky. Even if the market reacts to changes in supply and demand, nobody wise says it reacts instantly. But, I think there's also a chance you've been misled as to exactly how much oversupply there is.

<comment><USER13> Saskatoon (worse) and Regina (not as bad). For a long read about Saskatoon, see [https://jbuc61.wordpress.com/](https://jbuc61.wordpress.com/)

We recently moved from Saskatoon to Regina, and our family sold the Saskatoon house. We priced it slightly below market and it sold in 3 days, but there were many similar houses that had been on the market for months. The buyer tried to flip it (it needed love for sure); I'd guess $15-20k worth of supplies plus his own labour. It sat on the market for a long time, and ultimately sold for only $40k more than he paid us for it.

</comment></comment></comment></comment><comment><USER15> So many people in this message board hold strong normative opinions of free markets (namely, that they are inherently good) and don't understand the basics of them.

It surprises me, because economics seem so aligned with this board's interests, and usually the conversations about these kinds of topics here are informed or at least interesting. The "free market" seems to always be an exception.

Here are a few _basic economic_ reasons why you might not want a "free" market to determine the price of some good:

1\. It's "free" but not actually free. For a free market to exhibit the beautiful self-organization and maximization of surplus, you need:

a. Zero-cost (or at least low-barrier), voluntary entry/exit from the marketplace

b. Many participants

c. Information transparency

(a) doesn't apply to housing, healthcare, food, or anything else that's required for survival, so there are no theoretical guarantees about "free" markets in those cases.

2\. Self-organization and maximal surplus are not sufficient. This is a question of values, and usually is a question of valuing equality over efficiency. You might want, for example, for everyone to have a access to something (like a home) even if it comes at an economic cost.

3\. There are costly externalities. Sometimes markets can self-organize and provide efficient distribution of resources, but also cause a lot of problems that aren't captured in the supply/demand curve. For example, not having a home might cause you to do things that is socially harmful that you wouldn't otherwise do. Like using the sidewalks as a toilet.

<comment><USER15> I also want to challenge an implicit assumption made in the article, namely that rent control inherently limits housing supply.

Suppose rentals were simply banned. That is, you can only buy, and the rental market was eliminated. Would housing supply disappear?

I'd argue no, that you'd simply end up with more longer-term, higher-rate (to compensate for default-risk) mortgages.

Which might be a good thing. You wouldn't have to worry about 40-year residents being suddenly forced out of their homes any more, and still have a "free" market.

</comment></comment><comment><USER16> What's up with the modified title? The article title is "Rent control will make housing shortages worse" \- statement, not "Rent control will make housing shortages worse?" \- question

</comment><comment><USER17> In Sweden we have a history of a very strict rent control (which will probably be scrapped for newly built homes, because of current politics) while also handing a lot of power to the local governments to regulate housing.

There are _a lot_ of reasons Sweden has absurd real estate prices (the Gothenburg area in Sweden, which is the seat of moderately famous Volvo, has real estate prices close to the Seattle area which is the seat of Boeing, Amazon and Microsoft while the median incomes are not even remotely similar), and why the bulk of the price hike happened during the last decade, but a big one that's often overlooked is that local governments don't _want_ rentals. Because of rent control they are mostly inhabited by low income earners, who pay less tax. Rent control is set by the national government agencies, who of course don't care where you live, they want everyone to have a decent home without being gouged.

Not saying that rent control is necessarily a bad thing, but if implemented on a national level they give local governments less tools to get rid of poor people. In the end, they'll find a way, and that might create even more problems.

</comment><comment><USER18> The California bill is hardly a win for renters. How many renters who do not currently have rent control have their rents go up on average of 8% a year? I have lived in SF since 2012 in non rent controlled units and have never experienced that.

The only units I've seen that have rent increases that are greater than 10% are the new luxury high rises, which will continue to be exempt. The only landlords I really see this impacting are the corporate landlords like Avalon that manage older rentals and squeeze rentals as much as they can.

<comment><USER19> I just did the calculation on an apartment I rented back in 2000 in Fremont. It was $1300 back in 2000 and $3200 now. So that is about 4.6% increase per year. And this was a new apartment back then with pool, gym, covered parking, laundry in unit, close to BART. Are people really seeing rates increase more than 8% per year all around the bay area?

<comment><USER20> Maybe not consistently year over year, but bursts of rent increase can happen to people and blindside them financially. Preventing price gouging is what this law is intended for.

<comment><USER19> I have a feeling though that landlords who used to do 5% now think it is acceptable to do 8%. Obviously the market demands will restrict the increases further but the law kind of codifies what is not price gouging. I guess we will see in a few years.

</comment></comment></comment></comment><comment><USER21> Good landlords provide vital public service, and should be able to charge a slight premium as compensation.

This is different than speculators who boot out tenants to take advantage of market swings.

<comment><USER22> There will be no incentive to be a good landlord when it legally can't net you more money than being a bad one. NYC made a law that banned landlords from doing more than some percentage worth of capital improvements. The result is nobody improves anything, and the buildings and living quality just gets worse for everybody.

<comment><USER21> That hasn't been the result in nyc to date, and there were plenty of abusive, negligent landlords before. The only difference with the new laws is that tenants have enhanced legal rights to prevent that abuse.

</comment></comment></comment><comment><USER23> Ask a rent control proponent what market rate actually is and you'll see why they gravitate towards these bandaids

People view "market rate" as "expensive so lets do _something_ so people can afford housing" , distinct from "influenced by supply and demand and a rent control solution further constricts the supply of non controlled units further exacerbating inequality"

</comment><comment><USER24> Can't read the article so I don't know if they cover it, but this whole topic is unproductive due to binary thinking. The mild rent control being enacted statewide in Oregon and California doesn't seem much like the strict rent control resulting in people having far below-market rents?

<comment><USER20> It really just prevents price gouging. I believe on average market rents rise at a lower rate than these rent control measures allow, and rent control doesn't apply to new builds.

</comment></comment><comment><USER25> The housing problem has always been the biggest problem for mankind and the most basic problem for mankind.

</comment><comment><USER26> I'm sure this is being posted because California's state wide rent control is going into effect soon enough.

Of course - my rent was just jacked up again. Got the letter in the mail today. (Now paying about 22% more than two years ago) Landlord needed to get another 10%+ rent increase in _before_ they're under rent control.

Figured this would happen. Just sad to see it actually go through. So much for protecting existing renters...

<comment><USER27> Check the wording on the bill... I think that isn't actually allowed.

I seem to remember that your rent in Jan 2020 must be lowered to what it was in March 2019.

Edit: text from [https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml...](https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201920200AB1482)

"...The bill would provide that these provisions apply to all rent increases occurring on or after March 15, 2019. The bill would provide that in the event that an owner increased the rent by more than the amount specified above between March 15, 2019, and January 1, 2020, the applicable rent on January 1, 2020, shall be the rent as of March 15, 2019, plus the maximum permissible increase, and the owner shall not be liable to the tenant for any corresponding rent overpayment..."

<comment><USER26> Interesting. This is in fact in there!

[https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml...](https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201920200AB1482)

</comment></comment></comment><comment><USER28> Basic Economics, by Thomas Sowell[1]

There seems to be a requirement that policy makers _not_ have read this book.

[1] [https://www.amazon.com/Basic-Economics-Fifth-Common- Economy/...](https://www.amazon.com/Basic-Economics-Fifth-Common- Economy/dp/B00PKQMFT8/ref=sr_1_1)

</comment><comment><USER29> I like to test my world view as policies are enacted. Decide a concrete measure for a particular outcome, write it down and check the results once a year (I do it on new years day). These rent control measures are a great target for this regime.

<comment><USER30> Why don't you just check out the numerous other historical examples of rent control and see how they turned out.

</comment></comment><comment><USER31> This article ignores the political dynamics. Pro-supply policies like zoning reform are much more popular when bundled with anti-displacement measures, which address people's anxiety about them.

A mild level of rent control such as the new California law is not nearly as damaging to supply as policies like strict height limits. Knowing that rent control isn't fully optimal, I'd still gladly trade one for the other.

</comment><comment><USER32> I'm not an expert on this, and as such, I would like to "favorite" this submission on HN without actually bestowing upon it a "point." I'm not informed enough to feel confident in _endorsing_ this with the point that comes with "favoriting," rather, I just want to save this submission in the discussion in the comments for later.

</comment><comment><USER33> Here's the episode about rent control on Freakonomics: [http://freakonomics.com/podcast/rent- control/](http://freakonomics.com/podcast/rent-control/)

If you don't know the podcast (or the books) I highly recommend those.

</comment><comment><USER34> I wish there was a requirement of building 'x' homes before you can build 'y' and rinse & repeat. Need incentives or people just build the best contract deal.

</comment><comment><USER22> If this were true, and I'm inclined to believe it is, then shouldn't current landlords be campaigning for rent control?

It basically gives them a monopoly. Rent control serves as price fixing to keep out competitors. The entrance fee (to build a new building) is too high for the returns compared to alternative investments. For current landlords, it's a trade-off of lower short-term profit for long-term stability. Isn't stability the reason you invest in real estate (as opposed to say the S&P500)?

<comment><USER35> The value of an asset is determined by the expected return to investors (i.e. rent). If you reduce expected returns by limiting rent, your asset (the apartment building) is suddenly worth less.

But the typical rent control regulation only covers older building. If you are a developer of new apartments, you can benefit here. Your competition is under rent control, so their current tenants have a strong incentive to remain in older buildings. This reduces supply, and your new apartment building can raise prices.

</comment></comment><comment><USER36> Get out of California while you still can. Gavin Newsom is hell bent on completely destroying it.

</comment><comment><USER37> I don't buy it. Even with strong rent control or tenant rights laws, there's still a glut of profit to be extracted as a landlord. I understand the arguments, and somewhat agree with the purported models of behavior that say rent control will make things worse or at best maintain the status quo.

The whinging about rent control strikes me as misunderstanding the differences between modern rent control legislation and the policies of the 20th century, and also pure libertarian whinging. The arguments against rent control are econ 101 mealy mouth crap that is not even representative of all econ theory.

I own four houses outright and rent three of them out to families, and I'm currently invested in a quadplex whose rents I use to pay the loan off; and I'm about to leverage all of these properties to invest in a 20 unit apartment complex. Even considering taxes, maintenance, legal expenses, etc. I am pulling in so much raw cash that I foresee that I will be able to expand my business indefinitely -- even with Oregon's new rent control and tenant rights law.

<comment><USER38> I'm a landlord too and would love rent control in my area. No where else is a 8-10% increase year over year tolerable, but in rent controlled areas, it's the norm.

<comment><USER20> When the maximum rent controlled rate increase is set above the market rate increases, it's not going to affect prices.

<comment><USER39> > When the maximum rent controlled rate increase is set above the market rate > increases, it's not going to affect prices.

In theory, if real estate was a perfectly competitive market you'd be right. But by setting a standard well-known rent control rate, it sets a standard that landlords can follow. It allows landlords to act in concert and collude on rent increases without any direct communication between them.

</comment></comment></comment></comment><comment><USER40> Rent is going to be a thing of the past as landlords decide to just sell their real estate.

<comment><USER41> Now they have the option instead of selling it, to make it a full time AirBnb rental.

<comment><USER42> Many cities are actually regulating/pushing regulations against short-term rentals.

<comment><USER20> With zero enforcement

<comment><USER42> Absolutely not true

</comment></comment></comment></comment></comment><comment><USER43> Every time rent control comes up, you get a lot of Econ 101 critique of it, but really, rent control is fine and can even encourage new development. This is a good discussion of it: [https://www.fresheconomicthinking.com/2019/09/rent- control-i...](https://www.fresheconomicthinking.com/2019/09/rent-control-is- totally-normal-price.html)

Like a lot of economic critique, rent control criticism is driven from cultural allegiance to landlords and the rentier class, rather than good faith.

<comment><USER44> > Like a lot of economic critique, rent control criticism is driven from > cultural allegiance to landlords and the rentier class, rather than good > faith.

What a bizarre claim. There's plenty of "good faith" criticism of rent control.

People think different things. You're allowed to advocate rent control. But assuming bad faith because "I am right" or "my opponents are in the thrall of the rentier class" is just astonishing.

<comment><USER43> Don't be naive. Think of key debates on economic issues: Deregulation, monetary policy, minimum wage, rent control, taxation, etc.

People's opinions on these is overwhelmingly driven by their political values.

Trump doesn't love the Laffer Curve for its abstract mathematical beauty or essential truth - it's merely a rhetorical device for justifying the policies that he and other wealthy interests prefer.

<comment><USER45> "Assume good faith" and "avoid ad hominem" are not "naive." They are necessary. You simply cannot have a fruitful discussion without both parties agreeing on those rules. Even if you are convinced that your opposing party is not arguing in good faith, it is still in your best interest and the interest of those observing that you argue your own side using proper technique.

If you have given up on this, there's no point in participating at all.

<comment><USER43> I am not saying these things as a means to insult. I am not up on a debate stage here debating Trump on a stage.

I'm simply describing what motivates people on the political right. There is no end of literature to support this view.

You may find it troubling to accept that many people in politics aren't acting in good faith or motivated by pure virtue. But it's true!

</comment></comment></comment></comment></comment><comment><USER46> It's not like landlords provide housing to fulfill human need, they only serve people with money. Rent control might make them sad and build less, that's why we need to go further and provide universal public housing.

[https://www.jacobinmag.com/2016/10/housing-crisis-rent- landl...](https://www.jacobinmag.com/2016/10/housing-crisis-rent-landlords- homeless-affordability/)

<comment><USER47> Tell that to people living in NY public housing...

"Imagine paying rent for a home with rats, cockroaches, lead paint and mold. That's what far too many of the estimated 600,000 residents of New York City public housing are doing, even after a lead paint scandal led to an investigation by federal prosecutors." [1]

[1] [https://www.nytimes.com/2019/07/30/opinion/new-yorks- public-...](https://www.nytimes.com/2019/07/30/opinion/new-yorks-public- housing-nycha.html)

<comment><USER46> Yes, when you make programs target only the poor it breeds resentment and makes them vulnerable to conservative attack. Thus they get defunded, look like crap, and people can point to them and say they suck. Making them universal creates a mass constituency like social security and Medicare that is protective.

<comment><USER47> I don't think by any measurements the NY State/City gov'ts and/or New York Times can be called conservative.

<comment><USER46> I would call them a mixture of conservative and liberal. They share in common an interest in preserving the power of capital and private property. I am not a liberal, but am a leftist.

I used the word conservative not because it's 100% accurate (liberals are conservative compared to leftists though so it still is accurate), but because it's the word that people more immediately understand.

[https://www.jacobinmag.com/2015/11/public-housing-social- wel...](https://www.jacobinmag.com/2015/11/public-housing-social-welfare- crisis-affordable-gentrification/)

</comment></comment></comment></comment></comment><comment><USER48> Housing should not be a commodity. Rent control is a solution to a specific problem of housing being a commodity -- landlords spiking tenants' rents, forcing them to relocate. It isn't a solution to the housing shortage. The solution to the housing shortage, which, for example Bernie Sanders proposes in his plan and this article fails to mention, is dramatically expanding affordable public housing. There is simply no other solution to the housing crisis than to decommodify housing, considering it a public good and a right. The unregulated market has never and will never provide affordable housing to all on its own.

Economists love to criticize rent control because in some abstract economic sense it is "bad", but most people don't care about the market efficiency (Which mostly means maximizing profit to landlords and developers) of the housing market. They care that their communities are not destroyed by gentrification and that they are able to stay in their homes without being dislocated, both goals that rent control is successful at achieving.

<comment><USER49> Housing is a resource. If you have a magic wand to make resources exempt from economic forces, please wave it over food prices for the rest of us.

The reason economists get so excited about abstract issues of market efficiency is because if supply and demand don't balance naturally there will either be too much or not enough of something (effectively). Introducing rent controls will push the market towards the not-enough end of the spectrum and shortages will get worse.

> The unregulated market has never and will never provide affordable housing > to all on its own.

That is a huge claim. If people could use their land as they like there probably wouldn't be a housing shortage. Developers make large fortunes out of install dense blocks of apartments; and doing that isn't going to make housing more expensive.

<comment><USER48> >Housing is a resource. If you have a magic wand to make resources exempt from economic forces, please wave it over food prices for the rest of us.

That magic wand is called public policy. There are similar things you could say about food -- i.e. how unregulated markets lead to developing countries being forced to export cash crops instead of growing food that people in that country need to actually eat.

>That is a huge claim. If people could use their land as they like there probably wouldn't be a housing shortage. Developers make large fortunes out of install dense blocks of apartments; and doing that isn't going to make housing more expensive.

4.8 million households in the U.S. depend on Section 8 to afford rent. These are direct subsidies _to landlords_ to house these people. In an unregulated market, landlords would not rent to these people, and they would be homeless, or they would live in actual slums, which don't really exist in the U.S., but would have to in a fully unregulated market.

<comment><USER50> > That magic wand is called public policy.

Public policy doesn't exempt resources from economic forces, it's just a mechanism to divert resources from one area to another. It just allows you to fudge a little bit, push resources here and there. The Soviet Union eventually collapsed because towards the end for all their fudging they couldn't keep up net economic production. All effective policymaking comes from understanding constraints and operating within them to achieve the best outcome. For that reason - pretending that constraints don't exist is a recipe for bad policy.</comment></comment></comment></comment>