Trends to Avoid When Founding a Startup
fast.ai
fast.ai
"even for highly technical aspects like deep learning, fast.ai has shown that people with 1-year of coding experience can become world-class deep learning practitioners"
Yes, any Joe can train deep network with dozen lines of Keras. Sure, your startup can use off-the self models and tweak it a bit... That doesn't make you "world-class deep learning practitioner". If you are designing the network for new problem domain, there are thousands of decisions to make - everything from hyperparameters to network architecture to distributions in data. Making these decisions without having developed lots of intuition and good foundations is very hard. I often find usual developers without ML background and experience completely lost in these cases. Debugging a model that doesn't work is super hard. There are no IDEs, no breakpoints, no watches and in fact not even error messages. Its purely statistical debugging and probabilistic fixes.
For Google etc. it probably makes sense to pay top dollar for lots of ML PhDs (specialists).
A startup looking for opportunities engendered by ML is better off with a smart generalist. E.g. Dawson Whitfield of Logojoy was a designer. [1]
[0] https://twitter.com/patio11/status/936628610474983424?lang=e...
[1] https://www.indiehackers.com/podcast/038-dawson-whitfield-of...
Reading HN and following key people gives you the impression that this is an incredibly popular field - it is. In reality, though, it's a small field and lots of companies still don't use it, intend to use it, or have any idea how to use it. Alexnet was only 5 years ago. Faster-RCNN was only 2 years ago. Compared to, say, all programmers, statistically nobody is doing this.
Debugging a model is hard, but it's unlikely your efforts will be significantly more or less productive than a leading researcher aside from obvious things like noticing that the learning rate is too high or low. There's so much hand waving, even in top papers, that mostly we've tried stuff that looked like a good idea, and it turned out to be correct.
The thing is that a lot deep learning tasks don't require a fancy model. You can take VGG/Inception/ResNet and bodge them to fit a lot of real-world scenarios. Then the problems are mostly solved by standard machine learning intuition - making sure you have an appropriate train/test/validation set, a good loss function, etc.
Edit -
Here's an example: Halcon, a popular commercial image processing library has recently included a deep learning package due to repeated customer demand. It's not out yet, but it's most likely just going to be a wrapper for train/test using a basic image classification or object detection network. That's what the market wants. They don't care about GANs, they want something which will tell them if the image represents a valid product during QA 99% of the time (and often QA failures are pretty obvious - e.g. big crack in a bottle, or a deformed label).
You could build that kind of model with very little expertise and you'd beat most of the available image processing packages easily, with practically no work. The point is that an off-the-shelf model trained on company-specific data is likely to be state of the art on that problem (because who else has the dataset?) You don't care about 1000 classes in Imagenet, you want good product/bad product.
How rare is it to find a new problem domain these days? Most if not all problems tackled with deep learning have already been done in one form or the other. Deep learning is not yet much of an academic discipline, but accessible to anyone willing to put in the hours of study and practice.
Curious if anyone's done this: deep learning on DOM trees, to recognize portions of HTML pages. This has elements in common with both CNNs (where you want surrounding context - parents, ancestors, siblings, and descendants - to influence the recognition of a particular DOM element) and with RNNs (because elements may have an unbounded number of children, and potentially a recurring substructure), but I don't know offhand how you would combine them to solve the problem.
Anyone working on this? A quick literature search turned up nothing.
Seems like a really interesting idea to work on.
While I don't entirely agree with this assumption, if you are in business it's probably a "safer" assumption to make.
See how far they get. A talented guy can solve these within one year.
Many businesses have problems that an off the rack solution substantially solves it.
If AI is an integral part of my startup I would prefer to have someone with adequate experience in the founding team or very early hire.
90% of the work might not be super deep fundamental design decisions or debugging.
Yes, that guy can become a world class practitioner.
He was talked down to junior and paid as little as possible because that's what companies do.
VC is a "trend to avoid"? Avoid "Hypergrowth"? I guess companies like Google, Facebook, Twitter, Amazon, Stripe, Airbnb, Dropbox, Pinterest and a hundred others all really messed up.
I mean I could go point by point and give examples that are the opposite of these trends, but that isn't any more meaningful than these suggestions. You can choose to run a company however you want, and you should. If you believe these things are bad, don't do them. Attract employees that agree with you. The market will decide if you are right or not.
But enough with the blog posts about it, especially if they are dismissing critical tools that many companies use very successfully. Every company is different, every product is different, every team is different. Use some common sense on what tools make sense for you, and keep an open mind.
It can be inspiring to a lot of people to simply hear about the possibility of building a small, revenue-generating business or side project.
I've been running Indie Hackers for the past year and a half, interviewing hundreds of developers about their small businesses[0], and it feels like every day I talk to someone who's never even considered bootstrapping to be an option. Spreading awareness is valuable, especially considering that venture capital is only a viable option for a small minority of companies, and the ways in which it can cause an otherwise healthy business to fail are rarely discussed by VCs themselves.
>because the VC-funded companies you named are so successful at dominating our thoughts, conversations, and news feeds (HN included)
Right - HN's corporate sponsor is YC Combinator, so it's kind of inevitable that the VC narrative dominates.
The good thing about Indie Hackers being acquired by Stripe in April [0] (congratulations!) is that, even though you now have a corporate sponsor too, in the short term at least nothing really changes.
You wrote that
>Stripe wants to grow the GDP of the internet
so having 1,000,000 Indie Hackers starting companies is completely aligned with Stripe's corporate interests and that at least provides a counterpoint to HN and YC Combinator.
Further out, if Stripe Atlas grows big enough then we could end up with an Amazon Marketplace situation where small companies are forced to take whatever terms "Darth Vader" (#patio11) offers.
But we're a long way from that point yet.
A 3rd way is not to start a business at all. As Vincent Woo said in his recent podcast with you [1]:
>Right, so readers at home, if you can start a business, do that, but also maybe don't. It's not easy, it's a lot of work and there are a lot of things that valuable in life that have nothing to do with money, that's how I'd put that.
[0] https://www.indiehackers.com/blog/acquired-by-stripe
[1] https://www.indiehackers.com/podcast/041-vincent-woo-of-code...
I hate this narrative.
Yes, there are things in life worth more than money, but it just so happens that having a shitload of money makes living the life you want WAY easier.
What Vincent is saying is “Doing startups is hard, so just settle and be happy with what you have.”
What if you’re not? It just so happens that making very large amounts of money as an engineer is hard too!
Most everyone thinks it does, but that only makes it more untrue.
The HN community stopped being based in SV many years ago, if it ever was to begin with. It's heavily international, widely distributed even in the US, and significantly more sympathetic to boostrapper/indiehacker narratives than to VC ones. Which is pretty easy to understand if you think about who the users are and how many of them there are.
In terms of comment quality, the cynical end of anti-VC sentiment is a problem because it's so knee-jerk and predictable. Solid critique is welcome, but alas, there isn't much. As a moderator I don't care what people like or hate but I do care about getting the best insights from all sides, and this is one area where we don't, really. Or rather, we do, but it's drowned out by Dunning-Kruger and reversion to the mean and all the other phenomena that make internet forums suck.
It's mind-boggingly to me that this can be an option that people have never considered but I totally believe you. I've seen people have the "epiphany" that companies can spend revenue in much the same way they spend VC money. I guess their model is that only after that money is distilled into an up-and-to-the-right graph and put in front of VCs and they give you money do you actually get something you can spend.
> Therefore, VCs often push companies to grow too quickly, before they’ve nailed down product-market fit and monetization
[citation needed]
> Staying small keeps you focused on a small number of high-impact features.
[citation needed]
> This is not just a few bad actors: the behavior is wide-spread, including by many well-known and ultra-wealthy investors
[citation needed]
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I'm not going to get on some high horse here defending VCs, but these aren't original lines of argument and when unsupported I'm not sure what value they are providing. For example, YC is a VC. Do you think these 3 things apply to them?
Again, good for this company, I'm really happy they are succeeding and I'm glad they are doing it without VCs or other trends they don't like. This blog post is very shallow marketing though, I don't see how this is adding to the accumulated knowledge of creating a startup.
You could argue that VCs want to see product-market fit before they invest, but this is certainly not true of monetization.
I'm not going to say that VC isn't ever or usually the right move, but this comment is the height of survivorship bias. Not every business idea should be a VC-funded startup and trying to shoehorn every team and every business model into that path is a mistake. Whether to take VC capital is situational and depends on the team, market, product idea and all other factors of that specific startup. The businesses you listed were all the proverbial winning lottery tickets. There's plenty of losing lottery tickets too, and many of those had great ideas that would have been better served with a bootstrapped model that looked for slower, more sustainable growth and increased founder control.
A great startup is like a great food recipe. You have to mix the right ingredients in the right amounts to make it taste good. Almost every ingredient works in some dish, but many ingredients don't work in combination. Dishes can be appetizers, mains or desserts, and you'll want to add different ingredients depending on which one you're making. VC is like sugar. It can make your startup fat in a hurry, but if you're growing very quickly, all those calories can keep you from starving. It tastes great in almost anything, but it's put into way to much of what we eat today and causes a lot of premature deaths.
The irony is that when Google was young, there was a different set of "destructive trends". They still included taking VC, but it was also common knowledge that:
1. You had to sell a physical product for money.
2. You had to build out all your infrastructure before you could bring a product to market.
3. Sales & marketing were more important than engineering.
4. (When they were hiring on their hypergrowth trajectory, 2001-2004:) The web is dead, and programming is a terrible career because it's all about to be outsourced to India.
Perhaps the meta-lesson is to do what other people are not doing. The point of markets is that they reward unexpected successes, where an entrepreneur serves a population with little competition, and that necessarily means thinking for yourself and ignoring the common wisdom. Fast.ai does an admirable job at that - their deep learning course is excellent - but taking their advice at face value is just as dumb as cargo-culting Google.
A better lesson might be: stumble upon a phenomenal river of gold and dive in head-first.
Unfortunately, rivers of gold are rare. Which is why the rest of us are stuck with mundane stuff like marketing units of product or service to sell at a profit.
You can build whatever you want but if you don't tell anybody you'll fail.
There are hundreds of successful companies with rubbish products but great business connections and marketing.
Hypergrowth/VCs have been oversold as 'The one way'; I'm glad to see blog posts showing that there is a whole world out there.
VC money comes from those that have already become successful. So they are pushing for a strategy that will cause the majority of companies to fail but with the small chance that one will become the next Google, fb, etc.
Most founders can't afford that methodology. They should be looking to build a good company with good cashflow that can earn them a healthy nest egg and make them wealthy. When was the last time you heard of a VC that wanted good dividends from their portfolio companies?
So yes, if your goal as a founder is to build the next unicorn and you are willing to risk everything on that small chance to make it super big, then you should go the VC route. Problem is, you have one company and one life, so you better hope your company is the one in 10 that have a profitable exit.
Or maybe save yourself some time and just go buy lottery tickets.
Indie.vc (Bryce Roberts)
For founders and employees, you can't work for 50 different high risk, high return companies at the same time, so that might not be what they want.
Hence what could have been a decent company making a cool few million a year is instead one making huge losses on the assumption it's the next Facebook.
Google and Amazon are hardly the classic 100x-hypergrowth model.
Facebook and Twitter DID really mess up, have you seen how much utterly vile stuff you can report to them and it will still be around months later? Have you been at the receiving end of a shitstorm or Nazi trolls? People order SWATs over Twitter, leading to deaths. The president of the US leaves the rest of the world in panic when someone will finally declare war with him after yet another tweet. The reports from former "moderators" all have one thing in common, they're exploited worse than cannon fodder in wars (the soldiers at least were dead, the moderators will have to live with the videos of beheadings and CP the rest of their lives). Airbnb screwed up entire city rental markets and squeezed out thousands of poor people, hardly call that a success. Pinterest? Wtf how on earth is this worth 12 billion dollars? Or Snapchat with 25B $? Where is the actual worth represented in positive effects for society by this?!
In the end all that creates this "wealth" is data. Data is volatile, it's intangible - and once it is too much, the noise ratio gets too high to be useful, and then most of the companies you mentioned will have a massive problem with finances because they're only worth as much as the data and eyeballs of their customers is worth.
> Attract employees that agree with you
and end up like Uber with a reputation of "every one is welcome here, as long he's a white male with tolerance for extremely high alcohol consumption"?
> The market will decide if you are right or not.
The market is so oversaturated with cash that an app that could literally do nothing more than distribute "Yo" could get 1.5M$ cash at 10M$ valuation. The corrective forces of the market have vanished long since, otherwise the business model of many VCs would have broken years ago. To make stuff worse, the employees are often enough paid with options that may or may not become entirely worthless (and some options even are until an IPO or after years of working for the company!). That's gambling with the future in a really toxic way.
And while I'm at gambling: that more and more cash, both from the VC, the institutional financial and the private sector is flowing into ever more "coins" is also something that is dangerous and set up to explode. Yet another consequence of the current cash saturation.
Don't get me wrong: VCs as an institution, startups and coins serve an useful purpose. But in current conditions (both political and financial) they and the business model they run have grown extremely toxic on society.
You know if people are actually in panic over him tweeting, maybe the problem is in those people.
It's like those folks posting pictures of their kids crying when Trump won, saying "my 6 year old is now scared he'll be murdered". Uh, you did that to yourself.
This reeks of selection bias.
The way I'd frame it is:
1) If you want to build a company that is venture scale, taking VC funding is a great option to consider. If you don't want to build such a company, VC funding is a very poor option.
2) There are good and bad VCs. The bad ones suck. The good ones will help you and support you even if the outcome is 2x or 0x. Fred Wilson at Union Square Ventures articulates this attitude well in several posts:
a) "If you look at the distribution of outcomes in a venture fund, you will see that it is a classic power law curve, with the best investment in each fund towering over the rest, followed by a few other strong investments, followed by a few other decent ones, and then a long tail of investments that don’t move the needle for the VC fund.
But that long tail is comprised of entrepreneurs and their teams. People who have given years of their lives to a dream that was ultimately not realized.
And as I have written many times over the years on this blog, I spent the majority of my time on that long tail. This is irrational behavior if you think about fund economics, but I believe it is rational behavior if you think about firm reputation." (http://avc.com/2015/11/power-law-and-the-long-tail/)
b) "There are two interesting things here that I always think about. The first is that even the very best investors in the VC business only get a hit about 1/3 of the time. That means that they have their share of "slog it outs" and "hit the walls" too. I am certainly in that camp. The second is that we end up spending an incredible amount of time and energy (hopefully not money) on the 2/3 of our investments that don't work out. When everything goes well, you really don't need that much from a VC. Of course, I have added value in all of my winners. But its the ones that don't work that I have left my blood, sweat, and tears on. And that's the paradox of being a VC that cares. Which is the only kind of VC you want to work with." (http://avc.com/2013/03/when-things-dont-work-out/)
Time is also a currency, so you make a trade-off between time (extra years of your life spent acquiring funds to scale, and delayed product launch) and control.
Time is also an issue when you have competition, in the sense that your competitors can get there first by raising money.
For a decent, traction-but-no-rocketship product oriented startup, you need one solid back-end+ops guy, one solid front-end+UX guy, and one programmer for each mobile platform you wish to support. Add a CEO+sales+finances person, to keep the business side of the business compliant.
Yes, you won't get a cool continuous integration autoscaling whatnot doodad. YAGNI also applies to infrastructure, people and organizational hierarchies. Don't build them just because Google and Facebook have them.
Look for a profitable company that's just one size bigger than you are today, and aim for that. They've already proven that it's possible to operate at that size with the whatever they've got. Repeat as you grow.
And they are building their app in react native with one developer (which can then be launched in iOS and Android simultaneously).
To elaborate on what the piece touches on but doesn't specifically say:
> You will need to make hard decisions for the sake of > the business. You can’t actually offer people anything > remotely close to lifelong loyalty or security, and it’s > dishonest to implicitly do so.
To be clear(er): You will have to fire people, and firing someone who thinks of themselves as a family member or who you think of as similar makes the whole thing much more painful. Further, it can make you, as a leader, hesitate when it's an action you really need to take ("but this person is like my brother - we'll make it work!").
At my most recent company, we took the opposite approach -- we all liked each other a lot, we worked well together, and we ate lunch together as a team, but at 6pm everyone went home to their own lives and families. The lines were clear, the understandings were there, and I think it was a much better way to run things.
https://qbix.com/blog/index.php/2016/11/properly-valuing-con...
Just being able to solve some of those problems allows me to provide employers with major value.
In that case, I can readily see that. Especially if they have good teachers and teach the concepts thoroughly.
There are canonical examples of PhD theses that became successful companies, not to mention the dozens of companies who exited (e.g. CV companies to Qualcomm).
The examples of successes is both anecdotal and survivor bias, you have to look at all attempts and weigh all the successes against all the failures.
There will be future successful businesses based on theses, among the many more failures. The advice here is really aimed at people who think that a great technical solution to something automatically makes it marketable, which includes many thesis authors.
Someone who's done their real market due diligence, and knows which thesis to productize or which problem to solve, they have a good business idea. But for most PhDs, the advice is good advice -- don't sink all your time & money into a business based on your idea if you haven't done as much study of the market as you have on your thesis topic.
Most PhD theses would NOT become companies, so this is good advice for a PhD student but not for a founder. If a founder finds a PhD thesis that is worth productionizing, then the fact that it was a PhD thesis is irrelevant.
Regarding AI / ML as a service specifically. Many startups may find themselves facing the same issues plaguing IBM Watson. Deep learning considers trillion dimensional spaces. Most enterprise prediction problems are simply not of an astrophysical scale. And where domain specific expertise is required. Such as Google Maps use of CV in rendering highres satellite image data to SVG for the browser. Simply having grad student level familiarity with OpenCV might not be sufficient.
A Year of Google & Apple Maps (warning: bandwidth & cpu intensive)
https://www.justinobeirne.com/a-year-of-google-maps-and-appl...
And there's also no concept of unconditional love in a company. There's respect, which should be a given. But you shouldn't be expected to unconditionally love your co-workers.
"Times are hard, Jimmy. So Mom will drop you off downtown."
The team I was on waaaay back in the prehistory of the web era was warned against hiring too many PhDs. We disregarded the advice. (These were smart guys, yo! They had PhDs!) We spent a couple of years tackling interesting problems instead of relevant problems, and just about entirely missed out on the first web bubble as a result.
I love smart academic people. I love those big brains. I'll never hire one ever again. Give me someone who's more interested in working around a problem than in studying it, and we'll get things done. Give me a PhD and we'll have a nice paper about it about 5 years after anyone other than their thesis advisor cares.
"""Hiring a bunch of academic researchers will not improve your product and harms your company by diverting so many resources (unless your goal is an aquihire)."""
Disagree. Ceteris paribus a previous academic researcher isn't necessarily a worse hire than a graduate or someone who has worked at other tech companies. I'm really not a fan of this "don't hire people from academia" narrative. A good scientist is well versed in systematic problem solving which doesn't sound like a horrible skillset for someone working at a startup. Sure there's enough people with PhDs that can't write working code but there's also enough people that can. I'm not sure about the US but hiring a PhD isn't necessarily more expensive than hiring someone with job experience in the tech sector either. I get their basic idea but it's news to me that even AI startups are on "only PhDs all the time" hiring frenzies.
A large part of one's experience at a startup is unique to that context.
Certainly not all. But certainly a lot -- Probably the majority of what counts as advice.
Being able to filter what's generalizable and what's not is the most useful skill an advisor can develop -- ironically, even more than the experience. (And I'm breaking my own hypothesis by giving advice there).
This is rich coming from the founder of a startup that's indistinguishable from all the other coding bootcamp, MooC startups out there with a dash Deep Learning thrown in to make it extra trendy.
As for the actual advice, I think it's pretty hit or miss.
> 1. Venture Capital Nothing in her description of VC is wrong here. But the lesson: "VC is to be avoided" is an oversimplification. VC money is a tool. It's not always the right tool, and it certainly has its downsides. But it has its upsides too. In many cases it's the only way to get a business off the ground.
> 2. Hypergrowth I don't really know where the author puts the line between hypergrowth and just growth, as PG has said: starts are growth. They must always be growing, is there such thing as too much growth? Maybe, if really bad things are happening in the company because of the growth like people are burning out and quitting... but really that's only bad because eventually it will hurt growth. So I think the simple lesson is grow as fast as possible, don't be short sighted.
> 3. Trying to be “like a family” This one I agree with. Trying to be like a family in a professional setting is dishonest, and eventually the truth of the situation becomes clear. It's better to be honest from the start.
> 4. Attempting to productionize a PhD thesis We'll have to tell Larry Page he's wasting his time trying to turn that dumb Page Rank thesis into a company. There are a lot of companies that fail this way and there are a lot of PhD theses that shouldn't be commercialized but people try none the less. As with all things startups, there's a thousand failures for every success and the only way to tell the difference is to actually found the company.
> 5. Hiring a bunch of academic researchers The only company I've really observed doing this is Google and it seems to have worked out for them. I can easily believe that their are companies out there that higher a bunch of researchers to do a job they're not really capable of doing. I've also known academic researchers who were able to have a huge impact in the role they were hired for even though it wasn't really research. The general statement of this is that you needed to hire people for roles they want to and can perform. Researchers might be a particular anti-pattern in this but it's far from the only one.
Doubling headcount in a year seems also fairly routine for early-stage companies (assuming funding and cachet is in place).
This is obvious, yet an easy mistake. Behind it is applying the same values to a business as to a PhD - not only in topic selection, but also in execution. It's related to the conundrum that solving a problem that real people have now does not always require a technically great soluton. Even a revolutionary business does not necessarily require a technically great solution.
Academic success requires academic values and skills; business success requires business values and skills.
Of course, if you want to create a technologically revolutionary business, you'll need both.
Studies have shown that customer service, ability to execute & perform, and general quality of service correlate with whether or not the employee is an IC/temp or not. I've wondered if any startups have gone the opposite direction and hired folks FT/PT for roles that otherwise would seem to be filled by ICs.
I thought the definition of 'startup' is a company aiming for massive growth. If not you are a regular business.
So maybe the message is "You don't have to be a startup"
On front-end side adding redux when a normal react-app would be just fine.
No, just no. I disagree with this. I’m just a lowly employee and I spend 1/3rd of my life with the office.
I want that office to be my family when I’m spending so much of my life there.
Well, that's what an AI told me to do.