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clare

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This was actually covered in the article and exactly the advantage of YC: the vast network that could take your innovation to the next level and to the mass public. I don't think innovation itself guarantees any business success, it has to be combined with the right execution, especially marketing, PR, etc. One good example is Dropbox: there were numerous companies doing the similar thing as Dropbox, but Dropbox excelled. Granted the technology of Dropbox may be better at some fronts, but to me its critical advantage is that being a YC company, it could leverage a huge network, and became viral before everyone else.

As the entrance barriers for internet innovations become lower and lower, the competition to attract publicity and secure critical mass has become more intense. To me that is where YC offers the most value.

Great article. One maybe naive question since I have no mobile app development experience: it seems that many apps experienced a spike in downloads when they were first launched; but the downloads tapered off in the long run. It is quite the opposite of websites where the traffic start to build up only after a period of time.

Could this have something to do with the lack of app search engines/recommendation engines to direct users to niche apps? Or is it due to the fact that mobile users only care about the latest (and presumably best) apps out there, hence the shorter life cycle of mobile apps? Thanks.

Not in defense of Wolfram Alpha, I'm just curious if W|A does this out of concern of possible degeneration effect on their search accuracy caused by programmed search queries? For a human user of W|A, he will construct the query with certain syntax, and further drill down the search query with W|A's recommendations. W|A may very well monitor the pattern of user search queries to optimize their search algorithm. It is possible that programmed search queries, especially coming from a third party application where users do not use the W|A like syntax, may "pollute" the usage pattern for W|A and affect their algorithm's efficiency. Just my 2 cents.

Agree with this point. I'm both MBA/bizdev and software engineer. When putting on my MBA hat and working on sales forecast, decision making models, spread sheet is all I use. It is quick, tweakable, super easy to share. Whereas building my site which focuses on market research services, I resorted to C and existing stats packages cause they are powerful, more flexible, and basically programmable. To me what MBA/bizdev people need is significantly different from what a software developer writing stats-related code need. It is a very different scenario from the dropbox story...

I haven't experienced the first bubble, so no context there. However at least from my own experience with VC and angel investors, they are all quite risk averse and insist on seeing solid traction/revenue befor even considering investment. Among all the most popular sites, Twitter might be the only bad example and exception here without a solid revenue model, whereas Facebook, Linkedin, and Groupon all boasted a stellar revenue already. I do think the investors have learnt from the last bubble and changed the mentality a lot.

Exact my thought too, Greplin seems to be open to public signup yesterday, and it will be really interesting to see which social search will take off. Google seems to provide a superset of what Greplin provides (your own social data plus your social network's data), and Google definitely has the advantage of being a bigger name, which might mitigate some users' concern about privacy or security; on the other hand, Greplin is really focused on this one particular service and employs a really straightforward approach, which might actually be more preferrable to other users. Google's social search might be lost in a whole slew of other services, unless they focus on marketing and education about it.

Interesting idea. Although compare this to a survey (most commonly used idea research tool): even with a survey with hundreds of responses, there is the question of the sample's representativeness. Here the sample base is so small that the results (positive or negative) just cannot be regarded as statistically significant. It may be used as a complementary tool to measure the "virality", but cannot give me a 100% confidence level within 0% margin of error.

One example I ran into is "techstar network" hosted by techstar (http://www.techstars.org/network/) which is a partnership program of Startup America. It is still a little bit vague. But my understanding is that the startup America is a loosely connected network of orgs each working on its own ways to nuture entrepreneurship in US.

I may have missed it, but from the video it is not clear how Asana targets improving productivity for each specific business role. Through my experience the adoption of such tools highly depends on your role in the business org: e.g. Yammer may be very appealing to a marketing/bizdev guy, but not so much to an engineer. Wonder how this more generic approach can work out to fit the need of everyone.