That pretty much sums it up. VCs are looking for the 10x return (depending on what size funds they've raised and their thesis). Those type of opportunities are dwindling (for many reasons), but there are many, many smaller markets which you can carve 2% of the market at and not be a billion dollar business but be a $50 million and own all of your company.
Exactly. And it's not just that there are nice size opportunities left, it's that it's easier to succeed as a bootstrapper with a lot of people funded because there's less competition in the spaces that bootstrappers generally play in.
This is definitely a hot area, but unfortunately, it is also becoming the thing everyone wants to be attached to.
And so the term is becoming increasingly meaningless.
It's 2012s "location-based services" or "gamification" or "cloud" (wait, that's still hot). That said, I suspect big data (at least as I think I understand it) has more legs. But defining what it is is important else it becomes yet another buzzword.
Are compete.com and quantcast big data? Is eBay who analyze terabytes of user meta data "big data"? Is SeatGeek big data? Is Twitter big data?
Just because you have a potentially large database of stuff doesn't mean you are big data. Hopefully the term comes to mean something but right now, I fear it does not.
In systems research, it seems to mean something slightly more specific: how do you architect systems that can cope with crazy amounts of generated data? Being in the middle of academic job talk season, we hear people talking about a petabyte of total data and issues with generating and dealing with terabytes of data per day.
Some of the problems:
- It can take you longer to transfer off the data than your data acquisition source will allow you to store it there for.
- Even if you could transfer the data off, now you have the problem of storing it on your site and distributing it intelligently among processing nodes.
- Even if you could solve both of those, the projected power costs assocated with that scale of data are infeasible.
Most of the talks I see and papers I've come across seem to be focused on better scheduling and more experiment/gather-side filtering based on what you are planning to do with the data. But take this with a grain of salt, as I'm a compilers guy, so I just see the systems stuff secondhand and only know enough to talk about the languages-related issues with people who work in this space for real.
Depends on how you want to frame the problem. I work on sensor systems where we could only save off a small fraction of the raw data coming off the sensor. All data processing must be done in real time, in-memory, with the system only saving off (sending out) a reduced set of processed output products. That problem isn't new, though; military, meteorological, seismic, and space sensors have operated with that constraint for decades, since the advances that allow us to collect data have consistently outstripped the advances that allow us to record data.
Processing these problems requires a different mindset, and we are reaching a point now where business and web data flows are reaching the point that these sensor applications have been at for decades: they need real-time processing, with a concept of perishable data and a deadline for processing that data into some intermediate or final product that can be stored for later use.
Your second point is still very much a concern with this paradigm, though. The applications I speak of usually have some degree of natural parallelism in the sensor hardware, typically tied to the number of A/D channels coming off the sensor. Despite that, there are still unsolved[1] computational mapping issues with respect to breaking these processing tasks up beyond their natural boundaries. These sensors, and many of the emerging analytics applications, are not processing sets of independent jobs the way the MapReduce paradigm envisioned them. The parallel threads need information from each other to generate their output products, which complicates the division of labor and the execution control.
From what I have seen thus far, few big data platforms address the real-time or near-real-time use cases. The applications I work on currently use MPI grids on a cluster for parallel processing, which to me is the original big data platform. Not saying its the best way to do it, but nothing I've seen with the label "big data" can replace it.
[1] Unsolved in the sense that there is no one right answer or set of answers. There are certainly application-specific ways to "make it work".
Unfortunately, I would probably agree. Right now, big data means very little. The buzzwords may come and go, but the ability to extract useful information from data is here to stay. Honestly, extracting useful information has always been around. It is just now getting popular.
You're spot on. If you make it about "what's in it for the reader or prospective customer", their receptivity and reaction to it will be much better. That is in stark contrast to "here's why we're great" emails which are the norm.
The ultimate user of this is Amazon who recommend you stuff based on what you've looked at or bought.
I think TylerE maybe in the vocal minority here (no disrespect TylerE) but the type of customized "what's in it for you" email your talking about works. And many users who are looking to make their lives easier actually may appreciate it.
Not to take the discussion totally off-topic, but if you are a portfolio company of the Crunch Fund, is this type of thing by Arrington a net positive for the portfolio co?
I suppose it could be him "getting their back", but I wonder if his invectives agst Bilton in this case or others more generally end up doing more potential harm to portfolio companies than they help.
While it was fine as a blogger for TC, I wonder if the no holds barred mantra of Arrington introduces an element of uncertainty for his cos, i.e., "what might this guy say that I may have to deal with later"?
Uh oh -- The downvoting has started. Feel free to do that but please explain where I'm missing the mark. Thanks.
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I'm really not sure why entrepreneurs who I'd suspect believe in free markets need to be coddled and "protected" from pay-to-pitch programs which at the end of the day are just another business - some good and some bad.
Just because you're a struggling entrepreneur doesn't mean other people shouldn't charge you or you're entitled to anything. At the end of the day, it is the entrepreneurs' choice. If you don't have the money or if the program doesn't seem legit, don't pay. And find another way to reach investors. If spending $50 or $100 gets you a credible chance at $500k of seed funding and helps accelerate your path to getting that money or getting feedback, that seems fair.
Some pay to pitch programs may add value. And some may not. If an entrepreneur learns that the hard way, so be it. Nobody ever said being an entrepreneur would be easy.
It's not about being coddled, it's about exploiting entrepreneurs who are either desperate, or new to the game. We don't want to encourage that type of behavior in our community.
"entrepreneur learns that the hard way, so be it."
That attitude is like saying children should learn to look both way before crossing the street the hard way...by getting hit by a bus!
How is anyone being exploited? You have a choice to pitch or not. It's not blackmail. It may be foolish to pitch at these events, but as far as I know, nobody is being forced into pitching or being told "you can't research this event and our track record before signing up."
Cuban's points were a bit all over the place, but if we're talking about wealth creation of the F you money type, taking some personal/career risk often is required. Whether that's being a startup founder, being a hedge fund trader, or the best investment banker in the world, etc.
So if you don't want to start your own thing or work at one or work hard or generally take any risk, the wealth thing is probably going to prove elusive.
Of course, if you want to retire with a couple of million dollars, a nice corporate job and frugal lifestyle is prob all you need.
He's recommending if you have a $100k in savings, you should invest in startup tech companies and not public equities which is somewhere between ridiculous and stupid to dangerous. I like Cuban usually but this is terrible advice. If he meant you have an extra $100k to play with that you don't mind losing, then maybe this advice is ok.
I like Haircue. When my wife gets a haircut she likes, she always wishes she has pics of it right then so she can show the hairdresser the next time (esp if she needs to use someone else for some reason). And she tells me it always looks best right in the salon.
Good vanity, sharing, social-ness to this one. And prob some sort of B2B angle with hairdressers as well from a monetization perspective.
I don't get this. I did this myself with an app called "Camera" already. When I go get my hair cut in a few days, I'm just going to say "Make it like this"
Ha. Good point. But I think with women at least (basing this observation on my wife/sisters), there is an element of sharing, i.e. "check out my new haircut" or "like my new haircut?" and so some central social way to do this could be good.
I'm not the demographic but if properly positioned, I could see women primarily using this.
PG - What is the median valuation of those fundraising rounds? Averages are not a great metric because the few big valuation winners (Dropbox, Airbnb) skew the average big time and bring up the "normals" a ton.
If Airbnb is valued at $1B and if there are 200 YC alums who've raised, that adds $5M to the "average valuation" of each YC startup. (I know those #s are not right but just for purposes of the example).
Not sure I understand your point. I'm asking for median valuations as averages often distort reality.
Plus, "median series A returned a loss" - huh? Can you clarify?
If 5 companies have valuations of $5, $10, $15, 20, $1000, the avg is $210 million. The median is $15 million. I'd argue the median is more representative of valuations received than averages. And if you're a startup founder, the median is more useful to gauge the program as that is more likely what your valuation will be near than the average.
Average is the important metric in startup investing, because the distribution of outcomes is extremely skewed, and the median is likely a net negative outcome at re series A stage. It's those few blockbuster hits that make VC investing work.
New founders might care more about the median, but big investors, not so much.
Absolutely. But it seemed the original TC article was trying to suggest which program was best for startups. So if trying to figure out which program is better for general partners/limited partners, I agree average makes sense. For startup founders, median is what matters.
Out of a group of 10 companies, the average YC company has a top valuation of about 220 million. Paul does not give the distribution other than 'a power law' distribution.
>> If 5 companies have valuations of $5, $10, $15, 20, $1000
A more likely range of valuations out of 10 companies is:
$0, $0, $0, $0, $0, $0, $3M, $11M, $55M, $220M
The YC average is quite decent. The median result is likely to be $0.
If this probability distribution scares you, it is time to rethink startup companies.
The usual equation for startups is "This has a small chance to change the world." Fiddling around with the non-world-changing outcomes is premature optimization, since the best exits come from failing to change the world and merely creating a viable business (look at Paypal; they wanted to upend the world's financial system, and instead they had a billion-plus exit facilitating Beanie Baby trades).
The median is more representative of what a founder's expected financial return is, but many folks aren't optimizing for that. If they are, you need to look at the off-balance-sheet asset: the six-figure job offer from Google, FB, MS, Yahoo, etc. etc. etc. that is generally available to people of the viable startup-starting caliber.
Since the mean startup return is so driven by a small number of massively successful outliers, it makes economic sense that the median outcome will not look so great. (If startups presented a good chance of being as good as the next best option, plus a small chance of F-U money, nobody would work anywhere else.)
The median probably matters more to individual founders. The average matters to rational portfolio managers. As far as I know there is no portfolio strategy that rewards you proportional to your median investment. So, saying that one summary statistic is more representative than the other is only reasonable if you have some implicit reference frame.
Is it that VC-backed companies cannot pursue smaller markets and so there are nice size opportunities left for bootstrapped startups?