What’s Next In Tech? (probably part I)
In preparation for the What’s Next In Tech conference, I thought I’d submit my thoughts on What Next? Rather than submit a laundry list of ideas, I thought I’d focus on one of my favorites and give it a little more due. One thesis that I’ve had is that information will lead to step-function improvements in business (and consumer) applications. I think there will be fewer breakthroughs in raw application functionality, but the great breakthroughs will be the type of information consumed, processed, and delivered by these applications.
First, let’s look at what we at Fidelity Ventures like to call the train tracks. The analogy we use is that the train tracks have to be laid, before the trains can run. You can have the most beautiful trains on the planet, but if there are no tracks to run on, there’s no point. So, here are just some of the tracks being laid for the continued explosion of available information:
- Cost per MB of storage has come way down.
- 1956: $10,000
- 1984: $87.86
- 1994: $0.95
- 2004: $.001
- Breakneck growth of web pages
- 1998 Google index: 26 million web pages
- 2000 Google index: 1 billion web pages
- 2008 Google index: 1 trillion web pages
- Continuation of Moore’s law (# of transistors/IC) impacts processing speed, memory capacity, etc.
- 1971: 2,300
- 1990: 1,000,000
- 2000: ~50,000,000
- 2008: ~1,000,000,000
- Growth in Internet connection bandwidth
- Dial-up: 56 kbit/s
- T1: 1.544 Mbit/s
- Ethernet: 10 Mbit/s
- 802.11g: 54 Mbit/s
- Gigabit Ethernet: 1000 Mbit/s
- Other data points that I couldn’t quickly find information on:
- Growth in amount of information in corporate databases
- Projected broadband and fiber penetration growth (it’s growing)
- Growth in digital video, audio, and images
So, the tracks are laid for information to be stored, assimilated, processed, structured, extrapolated, and delivered. The question is what kinds of problems will information revolutionize? I’ll toss out some ideas but you can basically pick any business or consumer vertical, and I think it will be materially impacted by information.
- Manufacturing. As manufacturers spec products, their bill of materials will be dynamically priced based on real-time global pricing information delivered to their CAD systems as they manipulate their CAD drawings.
- Retail. Pricing on products of all kinds will become more dynamic based on real-time feeds of pricing data coming from local and global data sources and non-linear info like real-time weather. Coupons will also be dynamic based on real-time in-store sales information.
- Insurance. The way risk for any given type of insurance will be driven by the triangulation of non-linear information which to date hasn’t been possible. For example, a great predictor of a bad driver is having bad credit.
- Credit Information. Revolutions take down old stodgy incumbents. Say goodbye to Fair Isaac’s FICO score – completely dated. A new credit bureau will emerge to challenge the Big 3 and a new business info company will challenge Dun & Bradstreet.
- Human Resources. Hiring will entail more mathematical processing. Models will be created around a myriad of different jobs and be used to predict success (such as Unicru). HR will embrace math, like….
- Sports. It’s already started. 30 year-old quant jocks will run sports. In 5–10 years? 20 year-old quant jocks will take over.
- Fashion. The idea of merchandisers and designers going on shopping trips and runway shows for inspiration will be antiquated. Doing physical idea boards will go by the wayside. It’s already started.
- Trading. Information enables trading of just about anything and everything.
- Automation. Whether it’s robotic vacuums, unmanned aerial vehicles, automation of the warehouse, etc. Information will lead to automation in all sorts of applications.
- Many, many, others…
So, that’s one or many ideas on What’s Next In Tech. Hopefully I’ll see you at the conference June 25th in Boston. If you know any great companies along these lines, don’t hesitate to drop me a note – larry (@) fidelityventures.com or @larryvc on twitter.
Thinking About Thinking 
Good post Larry and reminds me of why it was fun working with you. I like the original thesis, and as you know I am a fan.
Another interesting view into the way data will affect our future is by looking at our kids. I teach part time at a local Middle School. these kids have multiple information sources they interact with concurrently, through multiple devices. An impact of this behavior on modern application design could be a move away applications delivering a single simple function, say a spreadsheet. Every function needs to be available everywhere and at any time.
Thanks Stephen. I’m not sure what you mean by every function needs to be everywhere. Give me an example..
Access to data and applications everywhere. Think twitter on every device, Zynga’s linking social games on multiple platforms, then do the same with enterprise apps. I want access to function and data from any device anywhere anytime.
Larry, I like the train track framework to give your thoughts direction.
Not so sure about using questionaire data to model and predict hiring decisions, but I do think that data from past performance and work history will be used to better match people and jobs.
Actually, on the hiring data, I know Unicru was really successful. They would require applicants to fill out these surveys, and they would run their algorithm. The type of worker they focused on was the hourly worker, often in retail, which has high churn. I think CVS was a client. And, they reduced churn dramatically through their profiling capabilities. I wonder if that will emerge in other areas. Unicru was acquired.
On the hiring front, I think better analysis of existing information would be very useful.
For hourly workers (Unicru case), perhaps there’s not enough info out there (or the info might be inaccurate) so tests would help. However, for traditional employers, I don’t think these tests are as useful.
When employing salaried employees, I’ve seen companies give simple coding tasks for programmers and college level math problems for insurance companies. One difficulty with an over-reliance of this data is that you’ll bias your hiring on those who are good at these tests (much the same way that traditionally people over-hire people who are good at interviewing). The other issue is that there’s a ton of information already that people generally put little weight on (GPA, test scores — for the insurance company which gave the math test, looking at their success in math classes could be a reasonable proxy for that).
I agree that 3 hours of interviews + a resume isn’t sufficient towards making a fully-qualified decision, so any additional information is helpful. Instead of additional tests/questionnaires, though, I think HR is better served seeking information themselves, which some have done in the form of web searches for individuals and other information-gathering.
Basically, the hiring process can be optimized in a manner similar to what professional sports teams do (gather as much information as possible and then try to extrapolate one’s career to past athletes who followed the same trajectory). The aspect which I think best compares to these questionnaires is the combines, where how fast someone runs a 40 meter dash on one day, a day where either the person could be suffering from jet lag, a cold, unfamiliar surroundings, sore muscles, or anything else, could affect whether he’s a first rounder or a late second round draft choice…an entire 4 year body of work is basically given the same weight as what happens in <5 seconds.
More information is always good – but I wonder if the key is actually better analysis of the existing information, extracting the fact that success as an actuary has a high correlation to performance in probability courses, which makes sense, but maybe the models may also determine that successful consultants also tend to have done well in literature classes (something less obvious).
Hello Larry, I think you’ve hit the proverbial nail on the head. We are counting on the train tracks (iinfrastructure) to develop and continually expand so that the data sets become richer and richer for organizations within all industries. It is still about the technology but really about how it supports the flow of data, not get in the way of it.
The next generation of innovation needs to be in the area of helping organizations to quickly gain a fundamental understanding of who they need to converse with and gain insight with true accuracy as to the meaning within the conversations.
We like to say that it is about building your social capital which if invested in, will protect and improve customer relationships and provide a more insightful and meaningful exchange between people for long term sustainibility.
It’s all good!!!
Best,
Wendy
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