Emerging Trend: Carbon Capture

With global warming from climate change accelerating there are a set of technologies and solutions emerging to hopefully address the challenge. NASA keeps a real time tracker of the effects of climate change here. One of the most direct and more science fiction like that is still nascent but in the pilot phase is carbon capture. Carbon capture is a set of technologies that is able to pull carbon dioxide from the air and store it back underground, hypothetically turning the clock back on global warming.

There are three startups on the cutting edge of carbon capture technologies - Global Thermostat, Climeworks, and Carbon Engineering. Their technology is based around exposing filters to air, that once saturated, are heated to free the carbon dioxide and pump it into tanks or back underground.

All three have working technologies that do this, the challenge right now is to find a business model that makes this a profitable activity. Substantial progress has been made in the last several years with the average cost falling from $600 per ton of captured CO2 to $100 a ton.

Critical to this effort right now are government tax credits that give back up to $50 for every ton of carbon stored underground. The improved efficiency of the technology and federal tax credits has captured the attention of big oil, who can use the recaptured CO2 to increase well pressure for oil extraction.

The next wave of carbon capture includes using the reclaimed carbon dioxide in transportation fuels by mixing it with hydrogen, reducing the carbon emissions of vehicle fuels by up to 20% in the next decade.

Currently the scale these firms are operating on is esmall but as they become profitable the hope is to scale up and start capturing noticeable amounts of greenhouse gases, hopefully slowing down global warming.

Emerging Trends: EMG, the Upcoming Human-Machine Interface

One of the coolest areas of frontier tech that is starting to emerge is Electromyography (EMG).  It’s part of what some firms are coining neurotechnology which is a set of technologies designed to digitize nerve and brain activity. 

EMG is hardware that uses sensors to record electrical activity from muscles.  A more thorough explanation of the science can be found here.  The technology was initially invented for medical applications but lately has been used in computer technologies by using nerve signals from muscle and hand gestures to interact with computers.  On the consumer side this type of technology is being coined intention capture because it interprets electrical impulses from nerves in a user’s muscles. The user doesn’t even need to make the motion but simply think it for their intention to be captured. Seeing is believing as the videos show. 

Two startups that are leading in this space are Thalmic Labs and CTRL-Labs.  The Myo armband from Canadian startup Thalmic labs was on the market for $130 per unit before the startup pivoted and discontinued the product. Reports from users were a lack of use cases and accuracy.  CTRL-Labs technology is still in R&D mode but seems to be able to interpret finer muscle impulses allowing users to type by capturing their intention rather than an actual movement.  They are releasing an SDK and hardware soon. So far the interface is via some sort of control armband, like a big watch band.  However, as the technology evolves its easy to see this shrinking in size and locations available to place it.

Challenges of course remain here, with one of the big ones being humans who have more body fat don’t give accurate reading to the sensors. Another is picking up signals precisely among the muscles in your body.

Most neurotech is to early for any sort of commercial application, but EMG is much closer to mainstream commercialization than the others such as direct brain-computer interfaces. This is because there is no need to break the skin-barrier or insert any sort of chip to create the interface.  I expect to see it talked about in the press and by average people in the next ~5 years.  The market and number of applications for this type of application will also be huge.

I will be watching this area develop closely and am extremely curious as to the applications that are created. 

Emerging Trends: The Future of Food

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With the world population projected to reach 8.5B people in 2030 by the UN from today's population of 7.3B, a crop of foodtech startups and technologies are under development to tackle this immense challenge of feeding such a huge population.  This challenge is further exasperated by the decline of clean accessible water and depletion of nutrients in crop soils global.  Several technology trends are arising to tackle this challenge in different ways.

Indoor Farming

This set of technologies is based on increasing crop density and yield by having perfect control of growing conditions (humidity, lighting, air composition) as well as making use of vertical farming.  The idea, which has yet to be proven economically viable, is to reduce waste of the inputs (i.e. fertilizer, water, energy, etc) while dramatically increasing the speed of crop growth and completely eliminating plant diseases and rot.  More crops more quickly for less cost then current methods is the goal.  The poster child for this set of technologies is startup Plenty which is backed by a number of big name and deep-pocketed investors.  They advertise yields up to 350 times conventional farming techniques with only 1% of the water.

New Methods of Producing Meat

It is well documented that cattle rearing for the world's ever growing hunger for meat products is horribly inefficient and damaging to the environment releasing high amounts of greenhouse gases.  A number of startups have arisen over the last few years seeking to grow artificial meat from cells in a lab environment.  Of these the one that has received the most press and funding is San Francisco based Memphis Meats.  The technology here is basically to use animal stem cells and bioreactors to grow meats in a lab setting that is indistinguishable from the traditionally raised and processed meat we eat daily.  It's a much more complex scientific challenge than it initially sounds.  The advantage is it likely will be much faster, cheaper and efficient pound for pound to produce any type of meat once the technology is developed and scaled.

Farm 2.0 - The Digital Farm

There are a ton of startups focused on improving traditional farming with everything from using drones and software to analyze crops in the field (i.e. Farmers Edge), robotics to replace human workers, software to better manage farm operations (AgCode), and many other areas.  While not as likely as the above two categories to dramatically increase food yields, this is a much faster to implement and less difficult set of technologies to bring to market.  In a word the Digital Farm is a sustaining set of technologies rather than a revolutionary set of technologies.

Some Thoughts on Voice

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Lately, I've been thinking about the rapidly emerging voice technologies, a subset of artificial intelligence and machine learning.  Most of the big technology companies now offer a voice assistant through a mobile phone or smart speaker such as Amazon with Alexa, Apple with Siri, Microsoft with Cortana, etc.  Forecasts predict that 50% of all searches done online will be through voice by 2020 (driven by mobile), with an estimated 13% of US households owning a smart speaker today.

My take is that voice is another interface, not a platform in and of itself or a new paradigm in computing.  Voice is inherently a low bandwidth medium making it strong for certain types of activities, specifically giving commands and getting information to basic questions.  “Alexa play music” or “Google what is the weather going to be today?”.  This is borne out by the plethora of studies being released lately examining how consumers use voice technologies.  The most common uses of voice today by far are asking for directions, asking a quick question, calling someone, checking time, or playing music.  

I expect to see the voice interface dominate when it comes to certain use cases such as home automation but I suspect it’s overhyped for a lot of other common digital use cases.  A big one in this bucket would be online shopping.  Humans are inherently visual creatures and the ability to see a product and read about it will trump being able to purchase purely through a voice dialog.  The only exception to this would be repeat purchases of the same product or if it’s a really basic item that you are agnostic to its brand (“Alexa, order me a stapler”).  Obviously, Amazon disagrees with me having stated it has over 5,000 employees currently working on Alexa and Echo technologies but I remain bearish on voice for eCommerce.

In terms of search, due to its low bandwidth I doubt voice will be very useful for discovery in general or for new customer acquisition by brands and advertisers.  It’s just not a good interface for comparison or discovery.  That of course won’t stop Google and Amazon from offering a new type of ad unit in the very near future where you can pay these companies to be a featured product in a voice search.

Noted technology pundit Scott Galloway for the last year has been predicting the death of brands due to voice but I have to disagree with him here and think he is greatly exaggerating the impact this technology will have.  Voice will simply becoming one of several interfaces users have access to as it seeps into common usage, but it certainly won’t replace touch, keyboards, etc. 

Where are the New S Curves?

S Curves are a business concept forecasting out the growth and development of new innovations as they progress through their technology life cycle.  They generally are considered to have 4 phases starting with Research and Development, Ascent where the technology starts seeing some real usages, Maturity where the technology is well established with slow growth remaining, and the Decay phase where other new technologies surpass it.  Wikipedia goes into much more detail here.  

S curves matter because they help you identify when the optimal time to invest is and when it's time to move on.  In other words, they are helpful in predicting growth and getting in front of it.  The big take away from the idea of S curves is that technology does not grow linearly, you have a long period of little to no growth with very few adopters, and then in a short time frame (1-3 years) the technology blossoms with it appearing to most people as coming out of nowhere  by magic to alter their daily lives.  

The dominant S curve of the last decade has easily been mobile, but its been in the maturity phase for the last few years.  Another S curve in the mature phase would be social media.  Every decade or so there is a dominant S curve but there are simultaneously other, smaller S Curves, that likely aren't as impactful in reshaping society but still bring about permanent change.

Some of the new S curves I am watching closely and where I see them in their cycle:

  • Artificial Intelligence - Despite all the hype this one is very early in the R & D phase with a ways to go.  We haven’t seen anything here yet.  This will likely really become the dominant S Curve of its time 10-20 years from now.
  • Drones - Ascent phase.
  • Autonomous Cars - R & D phase but will go into the ascent phase by 2020.  This will likely be the next big one after mobile over the next decade in that it will reshape society.
  • Blockchain - Ascent phase, even though with this one I don't think it will be as big as many people and the mainstream media will have you believe.
  • Genomics/CRISPR - Very early R & D phase.  We haven't even scratched the surface.
  • Virtual Reality - The ascent phase but I don't think virtual reality will be nearly as big or society changing as other S curves on this list.  I remain a sceptic on this one.
  • Augmented Reality - Solidly R & D phase but I think we will start to see it hit the Ascent phase in the not to distant future.
  • Alternative Energy sources/Battery technology - Early ascent phase but moving much more quickly than people think.  Check out my post here for more details. 
  • Cloud Computing - End of the ascent phase/beginning of the maturity phase.
  • Voice Interfaces - Beginning of the ascent phase.
  • 5G Technologies/IoT - About to hit the ascent phase as they come out of R & D.  The interesting thing here is I see 5G as being the catalyst that really kicks IoT (aka sensors being everywhere and in everything) into the ascent phase as well.  These S curves are even more intertwined than others.
  • Computer Vision - A subset of Artificial Intelligence above but one that is moving much faster along the S curve than other technologies that make up AI.  Currently in the beginning of the ascent phase.
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