BootstrapLabs at Davos 2020

Nicolai Wadstrom, Founder and CEO of BootstrapLabs, will be speaking on stage at the TechPark Davos Conference 2020, Global AI Thought Leaders Stream – Hyperconnected Humanity a side event in Davos, Switzerland during the World Economic Forum. 

With the perspective as Founder and CEO of the leading Venture Capital firm in Applied Artificial Intelligence, Nicolai will talk about how our world is going from Automation to Autonomous, and how that will impact people, organizations and society.

TechPark Davos 2020 Conference, the Strategic Council for Business and Economics brings hundreds of thought leaders, think tanks, tech & industry experts, and innovators together to focus on Artificial Intelligence, EduTech, MedTech, and Fintech, and turn these to societies’ best advantage worldwide. This conference, taking place parallel to the World Economic Forum (WEF) in Davos, offers a great opportunity to intensify the conversation around Artificial Intelligence on one of the highest global levels possible.

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BootstrapLabs at World Bank – Digital Mashreq Forum

Luigi Congedo, Principal, at BootstrapLabs, will be speaking during the Digital Mashreq Forum, hosted under the patronage of HRH Crown Prince Al Hussein Bin Abdullah II, the Ministry of Digital Economy and Entrepreneurship in Jordan, and the World Bank Group.

Luigi will share his story and explain how BootstrapLabs has become a leading Venture Capital firm in Applied Artificial Intelligence, and how the firm helps startups to successfully scale globally from Silicon Valley.

This two-day high-level event will serve as a platform to discuss the role of digitalization in shaping the region’s future through the lens of government and business leaders.

The Digital Mashreq Forum will bring together partners from governments and private sector representatives from Jordan, Lebanon, and Iraq as well as executives from international and regional businesses and investors. Many tech leaders will join Luigi on stage, including, Omar Razzaz, Prime Minister of Jordan, The World Bank Chief Economist, Rabah Arezk, Mr. Osama Al-Zoubi, Chief Technology Officer, CISCO, MEA, Mr. Diego Massidda, CEO, Partner Markets at Vodafone Group, and many others.

The event will take place on June 29 and 30, 2019 at the King Hussein Business Park in Amman, Jordan.

Follow the discussions via #DigitalMashreq

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BootstrapLabs To Speak at The SuperReturn US East Private Equity Venture Capital Conference

BootstrapLabs Co-Founder, Benjamin Levy, will be speaking at the SuperReturn US East Private Equity Venture Capital Conference in Boston, on June 11th, 2019.

The conference will bring together over 450 of the leading players in Private Equity and Venture Capital from the West Coast, for an unparalleled opportunity to meet, learn, and create new business ventures.

Ben, along with other leading industry experts, will share their strategies and discuss key issues during a session called: A.I. will radicalize VC and PE.

More about Ben Levy:

Ben Levy is the Co-Founder and Managing Partner of BootstrapLabs, a leading Venture Capital firm based in Silicon Valley and focused on Applied Artificial Intelligence. Ben is a repeat entrepreneur who launched, built, and exited two startups in the financial technology space. Praedea Solutions, a data mining and machine learning startup acquired by Mergent in 2006, and InsideVenture, an information portal for venture-backed pre-IPO companies and institutional investors, acquired by Second Market/NASDAQ in 2009.

Early in his career, Ben was an Investment Banker who advised CxOs of Fortune 500 companies. Over a period of 10 years, Ben helped his clients raise over $300M and close over $5B in M&A transactions. Ben worked at Lazard, SG Cowen, Houlihan Lokey, Wedbush Securities, and QuantumWave Capital.

Ben is a member of the AAAI (Association for the Advancement of AI), and a frequent keynote speaker on innovation, technology investing, entrepreneurship, and artificial intelligence in the US, Europe, and Asia.


BootstrapLabs at The World’s Largest Entrepreneurship Conference

EVENT:  TiEcon 2019 May 10th and 11th,  Santa Clara Convention Center

Nicolai Wadstrom, Founder and CEO of BootstrapLabs, will be interviewed live on stage at TiEcon 2019. Nicolai will share his story and explain how BootstrapLabs has become a leading Venture Capital firm in Applied Artificial Intelligence.

TiEcon is the largest technology anchored conference dedicated to fostering entrepreneurship. It has attracted over 60,000 entrepreneurs and professionals from over 50 countries in the past. Last year alone, there were over 5,000 participants from 19 countries.

TiEcon 2019 will focus on the hottest areas of innovation including AI/machine learning, security, FinTech, and digital health, in addition to hosting its flagship tracks on “entrepreneurship how-tos”, youth, and women.

Nicolai will be interviewed during a FIRESIDE CHAT on Friday, May 10th, from 3:00 to 3:30 pm. Join him and learn how BootstrapLabs selects, invests and supports the best Applied AI entrepreneurs.

This year, along with Nicolai, many other industry leaders will join the conference including Eric S. Yuan,  CEO of Zoom, Ashish Bansal, AI Leader, ML Engineering at Twitter, and many C-level executives from companies such as Adobe, NVIDIA, Salesforce, McKinsey, FedEX, Oracle, IBM, and many others.

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AI, Geopolitics, and National Security:

Your Information and Your Nation

Hacking is on the world stage again this week after Julian Assange, founder of WikiLeaks, was arrested in London and accused by U.S. authorities of conspiring with former Army intelligence officer Chelsea Manning leading to what is described as “one of the largest compromises of classified information in the history of the United States.” While some see Assange’s case as one strictly about freedom of speech and others see it as a question of security, his arrest brings the reality of hacking back into the spotlight, reminds governments and citizens alike about the potential vulnerability of the information they have stored digitally.

Experts suggest that this vulnerability can be pinned on the lack of attribution models, an integrated identity, and the complexity of establishing global privacy and ethical standards within the core building blocks of the Internet. While the general public watches tensions between super-powers mount on the international stage, trade tensions escalate, and the frequency and severity of cyber attacks amplify, what most don’t know is how artificial intelligence is playing an increasing role and intensifying what is at stake. This, of course, has not been lost on those in both the private and public sector who understand that how governments and the private sector engage with AI may very well dictate their future economic strength and security posture.  And yet, while many countries are active in AI, only a few have a planned national strategy for employing the technology.

As nations continue to invest in the implementation of AI, geopolitics will change irreversibly. But how remains to be seen. Experts from the United Nations and World Economic Forum explain what plans nations are already making at this year’s annual BootstrapLabs’ Applied AI Conference.

It is a critical time for nations as AI technology is rapidly developing and has already contributed to the exacerbation of state actors, terrorist organizations, and criminals’ political and ideologically motivated use of the internet for financial gain and power grabs. And when projecting into the future, some think things will only escalate, “Eventually geopolitics will no longer be territorial…but will reside mainly in the neuro- technological complex. We need to prepare ourselves for fierce power battles… plots, power grabs, secessions, manipulations, traitors and malevolence that will make the Wannacry and Petya viruses of spring 2017 seem harmless in comparison.”

But AI can also be applied as a method to not only alleviate such threats, but also strengthen our existing digital infrastructures and create stronger platforms for the future stability, economic prosperity, and security of our society, companies, and citizens. In a fireside chat at AAI19, specialists from U.S. Department of Homeland Security, Quantum Vault Inc., Atlas Organization, and MobiledgeX join to discuss what the industry can anticipate in the next few years across the security domains that AI will impact.

To further unpack the relationship between AI and national security, panelists from the Technology for Global Security, U.S. Department of Homeland Security, and Defense Innovation Unit discuss key topics covered in the annual unclassified US National Intelligence Report and what they are doing within their respective organizations to accelerate the discovery, development, vetting, adoption, and deployment of AI and other new technologies in service of protecting American lives and interests around the world.

Want to learn more about how to become engaged, what you can do, where you should look to get involved and have a broader impact, and how the Defense Innovation Unit and Department of Homeland Security are making it easier for companies to get engaged and navigate the federal marketplace? Then join us this Thursday at BootstrapLabs Applied AI Conference.

Trains, Planes, and Automobiles:


The Necessity of Real-Time Processing in Autonomous Vehicles

When thinking about autonomous vehicles, it’s easy to focus on driverless cars, how your commute to work will change, how your drives with the family will be filled with board games instead of “are we there yet?” The technology has been parodied in the HBO show Silicon Valley, is roaming the streets of San Francisco and Hamburg, and is the unforgettable protagonist in a vehicular accident that led to a man’s decapitation.

Whether you are trepidatious or excited, self-driving technology is inevitable. For the folks working to get the technology ready for wide-spread deployment, the trick is making sense of the road in real time, not if the driver’s seat can rotate to face backwards.

Ultimately, the concept of a driverless vehicle is simple, a vehicle that drives itself. But the mechanics of how that vehicle drives all by itself is where the challenge lies. In order to work without human intervention, driverless vehicles are “permanently surveying the road and sending relevant bits of information up to the cloud,” explains Christoph Grote, SVP of Electronics at BMW. But the trick is knowing and catching the “relevant” information. And this is not easy.

AEye, an artificial perception technology company, focuses their work specifically in the space of creating tech that is able to abstract and extract meaningful and necessary information from a car’s environment. Namely, they are creating AI-enabled sensors that enable vehicles to “think like a robot, perceive like a human” – seeing, classifying, and responding to an object, whether it is a parked car, or a child crossing the street, in real time and before it’s too late.

What AEye understood, is that in order to create technology that can perceive like a person and think like a robot they needed a rocket scientist (almost) literally. The Chief Scientist at AEye, Dr. Allan Steinhardt, serves this function. Dr. Steinhardt is an expert on radar and missile defense, including ground moving target indicator radars (so, a radar and missile scientist if you want to get nit-picky about the rocket analogy); space surveillance; and he is the former Chief Scientist for DARPA.

At this year’s BootstrapLabs Applied Artificial Intelligence Conference, Dr. Steinhardt delivers a keynote on how transportation AI is uniquely different from other applications of AI. In addition to exploring how AI is augmenting the collection and interpretation of environmental data the car must process, he will also address the very environments these autonomous vehicles will be driving in: the cities of the future.

It is easy to get fixated on the gadget. We are primed to want the newest artifact of technology, be it the newest phone, scooter-rideshare, or smart speaker. What is easily forgotten is the network that these technologies rely on. Up until now, the landscape of things needing internet connectivity has been able to rely on either WiFi or 4G. Autonomous vehicles, and the reams of data they are reliant on processing and distributing, require much more bandwidth. Christoph Grote explains that driverless cars are continuously gathering data from their environments, “generating a true, real-time map of a car that is pushed down to all the other cars…autonomous driving is not the capability of a car, it is really about swarm intelligence.” And in order to do this, autonomous vehicles will need to be able to connect to 5G.  

The cities of the future will be increasingly optimized to support automated functionality, including autonomous driving. But how, you ask? Join Dr. Allan Steinhardt’s keynote at AAI19 to find out.

Human Capital in the Age of Artificial Intelligence

Will Your Job be Taken Over by Robots?

In 1589, a British inventor introduced his invention, the Knitting Machine, to the Queen of England expecting to be granted a patent. Instead, she replied, “Consider thou what the invention could do to my poor subjects. It would assuredly bring to them ruin by depriving them of employment, thus making them beggars.”

The fear that technology will displace workers is not a new fearbut it is certainly a trending-fear, with a web app to match. Workers are not unfamiliar with skepticism about how new technology will change their job or make it obsolete. But the true fear the workforce has about new technology, especially automation, is not about losing a job, but about having an employable skill set. And from there, it is a slippery slope to worrying about becoming destitute.  

In 2013, researchers at the University of Oxford estimated that 47 percent of US employment is at “high-risk” for complete automation within two decades. Should their estimations be correct, we can expect that roughly 65 million Americans will not only be out of a job, but will have been asked to leave their jobs because their skill set had become obsolete, taken over by automation. Given that these are conservative estimates, perhaps it is time to get worried.

But not so fast.

Despite the dreaded prediction, that over 500 million people worldwide will lose their jobs in the first wave of the Fourth Industrial Revolution (4IR) to automation, experts agree that people, with their inherently human social savvy and creativity, will continue to remain the main drivers of innovation.

At this year’s BootstrapLabs Applied Artificial Intelligence Conference, experts from companies working to manage the transition to automation and insiders at companies like Facebook, Adobe, and Microsoft working internally to prepare for this upcoming shift in their workplaces, will discuss the Future of Work in the Age of AI.

Industry thought leaders, like Oliver Brdiczka who is building Adobe’s AI assistant and Noelle LaCharite who is working to educate developers for Microsoft about AI, are dedicated to making AI understand and respond to human behavior. Rather than sacrificing human intelligence to the might of AI, as science fiction might have us believe is our inevitable future, they are focused on harnessing human intelligence. This new wave of AI, called Contextual and Conversational AI, trusts that artificial intelligence is hardly intelligent without human intelligence, cementing the future for human capital. In fact, valuing human intelligence aids in securing human capital’s place as the most valuable resource of any company.

And yet, even if human intelligence dominates work culture, the working landscape is still destined to change in the face of artificial intelligence and machine learning technology in the workplace. And it’s going to change fast. In fact, it is the industries that are most increasingly reliant on automated work, that will need to most-quickly consider how to adapt in stride with the developments of AI and how to rethink frameworks for sound management in a world of work supported by automation, lest failure. That’s where Dinkar Jain, Head of Machine Learning for Facebook Ads, is focusing his attention: the future of management and business practices in the Age of AI.

Despite the centuries-old hesitations of the labor force surrounding the adoption of new technology, historical data shows that technology has actually created more jobs than it has displaced or made obsolete.

But the big question in a time where artificial intelligence and machine learning are leading rapid developments in automation is: will it be different this time?

Find out from the experts, and learn how to prepare and transition today’s human capital into their roles of tomorrow at AAI19.  


Get your tickets today, For a limited time, get 50% off on registration using promo code: AAI1950Percent

BootstrapLabs AAI18 Keynote and Fireside Chat – SoftBank Robotics AI Vision 2020

Don’t miss this year’s Applied AI Conference 2019

on April 18th in San Francisco

Join thought leaders leveraging AI applications to build the future of enterprise, corporations, governments, and society as we know it.

For a limited time, get 50% off on registration using promo code AAI1950Percent



In the meantime, please enjoy some of our favorite sessions from 2018:


  • Steve Carlin, Chief Strategy Officer, SoftBank Robotics
  • Ben Levy, Co-Founder, BootstrapLabs
  • Doug Aley, Chief Revenue Officer, Ever AI

During this session, the speakers discussed AI in robotics, and where SoftBank sees the future going in the next few years, some of the key challenges in hospitality and how companies can partner with SoftBank and the 2020 Vision.

Some of the key takeaways from the session are:

  • Contextualizing data is going to be a key differentiator in AI technologies moving forward like conversational UX and deciphering customer intent.
  • Robotics has the capability to disrupt the key revenue-driving experience for over 20 trillion dollar industry.
  • Be hyper focused in the use cases your technology will be able to solve instead of covering a wide range.

About Steve Carlin:
Steve Carlin leads SoftBank Robotics America and is the global Chief Strategy Officer. In this role, he acts as the General Manager for the Americas region and oversees global marketing, product and strategy. Carlin most recently came from Facebook where he held the role of Global Head of Strategy – Gaming. Prior to Facebook, Carlin was the Senior Director of Marketing and Insights for Ubisoft. He also held a series of roles in sales, shopper marketing, brand management, and strategy at Energizer and Procter & Gamble. He holds a BA in Geology from Miami University in Oxford, Ohio, and an MBA in Marketing and International Business from Goizueta Business School. He has also studied at Cornell, HEC business school in Paris and the UIBE business school in Beijing.

About Ben Levy:
Ben is a Co-founder and Partner at BootstrapLabs, a leading Venture Capital firm based in Silicon Valley and focused on Applied Artificial Intelligence. Prior to BootstrapLabs, Ben was repeat entrepreneur who launched, built, and exited two startups in the financial technology space, Praedea Solution and InsideVenture. He also was a Technology, Media, and Telecom Investment Banker who advised startup founders and CxOs of Fortune 500 companies on corporate strategy, financing, and M&A. Over a period of 10 years, Ben helped his clients raise over $300M from institutional investors and close over $5B in M&A transactions. Ben is a frequent keynote and panel speaker on innovation, technology investing, entrepreneurship, artificial intelligence, and globalization in the US, Europe, and Asia.

About Doug Aley:
Doug Aley has spent his career helping to found, lead, and scale startups. He is currently Ever AI’s CRO and a principal at Atomic Ventures. Prior to Ever AI, he held positions early in his career at Amazon, was VP of Marketing, Product, and Business development at Jott Networks, helped Zulily scale from $100M to $700M as VP of Product and Corporate Development, held the same role at Room77, and started and led Minted’s digital growth team. Mr. Aley holds a BA from Stanford University and an MBA from Harvard Business School, and lives in Greenbrae, CA with his wife, Susan, and their two boys.

AI Policy, artificial intelligence

BootstrapLabs AAI18 Panel – AI Policymakers: the Need for Public/Private Partnership

Over the next several weeks we’ll be releasing the videos from the sessions from the BootstrapLabs Applied AI Conference 2018. The yearly conference organized by BootstrapLabs, a leading Venture Capital firm focused on Applied AI, that brings together over 800 members of the Artificial Intelligence community for a day of incredible speakers and exciting conversations.

Join the Applied AI Conference 2019, on April 18, in San Francisco

Join thought leaders leveraging AI applications to build the future of enterprise, corporations, governments, and society as we know it.

A limited number of tickets are currently on sale at the Early Bird price ($1040 off) ENDING MARCH 15TH!


Moderator: Jane Macfarlane, CEO, Seurat Labs / Director – Smart Cities, UC Berkeley


  • Kay Firth-Butterfield, Head  of AI and Machine Learning, World Economic Forum
  • Norma Krayem, Sr. Policy Advisor and Co-Chair – Cybersecurity and Privacy, Holland & Knight
  • Raj Minhas,VP – Interaction and Analytics Lab, PARC

During this session, the panelists discussed AI in the context of regulation, the role of government in fostering and leveraging emerging technologies and the reasons why we need better public/private collaboration.

Some of the key takeaways from the session are:

  • Fostering and creating explainable models is going to be crucial to get government support and foster collaboration in the AI space.
  • Standards, certification, protocols, partnerships and process or outcomes based scalable laws are some of the avenues we need to explore to “regulate” AI.
  • Open, honest conversations are necessary between technology companies and regulators, it’s important to educate them on how the technology works and the real risks with integrating to different systems.

About Jane Macfarlane:
Jane Macfarlane is the CEO of Seurat Labs and is also the Director of Smart Cities and Sustainable Mobility at the University of California at Berkeley. Dr. Macfarlane has over 30 years of experience in high performance computing, data analytics and geospatial mapping. She has held various roles responsible for directing industry research groups including: Chief Scientist and Head of Research for HERE, VP of Process Engineering at Imara, and Director of Advanced Technology Planning for OnStar at General Motors. She has authored 26 patents, primarily in geospatial data analytics. She holds a Ph.D. in Mechanical Engineering from the University of Minnesota. Currently she is leading a DOE National Laboratory effort focused on the use of High Performance Computing to address Big Data Issues in transportation systems.

About Kay Firth-Butterfield:
Kay Firth-Butterfield is the Head of AI & ML at the World Economic Forum and is a Barrister-at-Law and former part-time Judge in the UK. She is an Associate Fellow of the Centre for the Future of Intelligence at Cambridge and Fellow of the Robert E. Strauss Center on international Security and Law at the University of Texas. She is Vice-Chair of the IEEE Initiative on Ethical Considerations in AI and Autonomous Systems. She is one of Robohub’s top 25 Women in Robotics and co-founded AI-Austin, AI-Global and the Consortium for Law and Policy of AI and Robotics.

About Norma Krayem:
Norma Krayem is a Senior Policy Advisor and Co-Chair of the Holland & Knight Global Cybersecurity and Privacy Team. She brings more than 20 years of experience within the global policy-making arena including executive-level positions in the U.S. Departments of State, Commerce and Transportation, and as a consultant at the Federal Emergency Management Agency (FEMA). She works on a daily basis with the White House, Executive Branch and Congress on a host of matters, including Homeland Security, Commerce, Defense, Treasury, Transportation among others. She is a member of The Chatham House and served on the prestigious CSIS Cybersecurity Task Force from 2015-2017.

About Raj Minhas:
Raj Minhas is Vice President and Director of the Interactions and Analytics Lab (IAL) at PARC. Research in IAL focuses on people and their behaviors. Raj joined PARC in September 2013 as the Program Manager for Prognostics and Health Management and was responsible for the strategy and execution for the commercialization of the related technologies. Prior to PARC, he was the Director of Xerox Research Center India where he led its growth, development, and outreach for two years. Raj earned his Ph.D. and M.S. in Electrical and Computer Engineering from University of Toronto and B.E. from Delhi University. He has eight patents and six pending patent applications.

BootstrapLabs To Speak at The SuperReturn US West Private Equity Venture Capital Conference, Feb 11-13

BootstrapLabs Founder & CEO, Nicolai Wadstrom, and Co-Founder, Benjamin Levy, will be speaking at the SuperReturn US West Private Equity Venture Capital Conference in Los Angeles on February 11th and 13th, 2019.

The conference will bring together over 300 of the leading players in private equity and venture capital from the West Coast, for an unparalleled opportunity to meet, learn and create new business ventures.

Nicolai and Ben, along with other leading industry experts will share their strategies and insights, and discuss the trends, opportunities and challenges facing the future of venture capital.

If you’re in the area, come see BootstrapLabs participate in this influential conference. For a 10% discount, purchase your ticket here and enter following code:


BootstrapLabs will be hosting a reception in a private villa in the Beverly Glen area of Los Angeles on Monday, February 11th, from 6:30 to 9:00 pm.

We are inviting family offices, institutional investors, and other limited partners who are interested in decoding the AI trends, their impact on existing portfolio allocations, and capture what promises to be one of the largest wealth creation opportunities of the next decade.

If you are interested in joining please contact

Agenda Featuring BootstrapLabs

Feb 11, 09:20 – 10:00

Benjamin Levy will be participating onstage as a panelist. The discussion will cover the following topics.

Title: Future gazing: how will the VC landscape change by 2030?

  • How do you spot the superstar of the future?
  • Where is Venture going and how is it evolving?
  • Is disruption likely to keep going?
  • Is Venture likely to cease in a recession?
  • How are VC strategies developing? What sectors are VC moving into?
  • Are there new ways to finance portfolio companies?
  • What other hybrid and innovative models are there in Venture?

Additional Panelists

  • Jeff Grabow, US VC Leader at EY
  • Xinhong Lim, Director at Vickers Venture Partners
  • Carmen Palafox, Partner at Make in LA

Feb 13, 15:40 – 17:20

Nicolai Wadstrom will be participating onstage as a panelist. The discussion will cover the following topics.

Title: Technology spotlight – Rotating roundtable presentations and Q&A

Examining the most influential technologies; what they are, what makes them important, and how to invest – 10 minutes on each topic then delegates change table.

  • Automation – How does it work, and how do I invest?
  • Artificial Intelligence & Hardware – How can it be used, and how do I invest?
  • Blockchain – How does it work, and how do I invest?
  • Cryptocurrency – How does it work, and how do I invest?

Additional Panelists

  • Peter Liu Vice, President at Pritzker Group
  • Aymerik Renard, General Partner at Hardware Club
  • Venktesh Shukla, General Partner at Monta Vista Capital

For more information please visit the conference website.

BootstrapLabs - BloombergNEF IMAGE

BootstrapLabs on Bloomberg News

In December 2018, our CEO and Founder, Nicolai Wadstrom, met with Danya Liu, Emerging Technology Analyst at Bloomberg NEF in San Francisco. He shared some details about the BootstrapLabs investment strategy, and his vision on how Artificial Intelligence will impact our world.

Each month Bloomberg features a strategic investor active in one of the key industries they track. Their profiles provide an outline of the main investors, portfolio companies, characteristics, and strategies involved in the company, as well as their own opinion on what makes their operation so interesting.

We are pleased to share with our community the first Bloomberg Tech Radar report of 2019,  featuring BootstrapLabs. You can access a copy of the report (courtesy of BloombergNEF) by clicking on this link or the image below, and entering your email address. 

(courtesy of BloombergNEF)


BootstrapLabs - Ferrari Image

Powering Autonomous Dreams


Thomas A. Campbell, PhD – Founder & President, FutureGrasp™, LLC[1] and Special Advisor, BootstrapLabs
Nicolai Wadstrom – CEO and Founder, BootstrapLabs

The Dream

Driving used to be a pleasure: the open road, Sunday drives, cruising on Main Street. Now it is more of a chore: traffic jams, road rage, avoiding distracted drivers. While in the last few years we have advanced with capabilities such as Waze and Google Maps in our unending quest to dodge traffic and still make it home in time for a hot dinner, the thrill of being in the driver’s seat is waning.

Moreover, there are few things that we spend more time doing than commuting. “New survey data show the average American’s commute inched up to 26.9 minutes from 26.6 minutes the previous year [based on a one-way commute], according to the U.S. Census Bureau’s 2017 American Community Survey.”[2] US cities with the worst traffic have more than 40 minute one-way commutes.[3] Thus, many commuters spend more than an hour of every workday just driving; our time is being consumed by this stressful and unproductive activity.

Finally, polluting cars are a significant source of carbon dioxide (CO2) emissions, a major contributor to climate change.[4] “EIA [Energy Information Administration] estimates that U.S. motor gasoline and diesel (distillate) fuel consumption for transportation in 2016 resulted in the emission of about 1,102 million metric tons of CO2 and 437 million metric tons of CO2, respectively, for a total of 1,540 million metric tons of CO2. This total was equal to 82% of total U.S. transportation sector CO2 emissions and equal to 30% of total U.S. energy-related CO2 emissions in 2016.”[5] Clearly, this is unsustainable.

So what might we do to realize the dual dreams of hassle-free, environmentally-friendly driving? One approach is to make our vehicles autonomous…to empower the car to do the thinking instead of the driver, and thus free us to do things other than focus on that next stop sign. Moreover, making autonomous vehicles (AVs) become electric vehicles (EVs) could be an even further victory for our environment.[6] This vision is being aggressively pursued by many companies and researchers; unfortunately, reality imposes several technical challenges.

Reality & Challenges

Autonomous vehicles have been considered possible with the right technology aids almost since the first mass assembly car was manufactured.[7] However, only in the last decade have AVs been taken seriously with recent advances in computation and sensors. Perhaps starting with Google’s initially secret Waymo project – itself stemming from Larry Page’s undergraduate vision of being chauffeured around Stanford University campus autonomously – AVs have started to gain traction.[8] Nevertheless, there remain many fundamental challenges, both technical- and policy-related, before we can take our eyes off the road. The one we’d like to focus upon in this note is the need for power.

A few years ago, the only EV many Americans had ever seen was a golf cart.[9] Surprisingly, several of the first automobiles were all-electric in the late 1800s. Popular in cities (where one could get electricity), EVs fell out of favor with the introduction of the mass-produced Ford Model T car, followed by the discovery in the United States of cheap Texas crude oil. EVs all but disappeared by 1935.[10]

With advances in technology – especially batteries that offer range up to 300 miles on one charge – EVs have experienced a resurgence in popularity. “Today, there are 23 plug-in electric and 36 hybrid models available in a variety sizes.”[10] The United States just exceeded one million EVs on the road.[11] [12] One of the leading companies developing EVs is Elon Musk’s Tesla. The Tesla Model 3 and its other brands are wonders of technology.[13] However, despite full touch-screen for nearly all its controls and many other technology perks,[14] Tesla cars would be just hunks of metal without power, as well as the computers for autonomous controls.

Power (Batteries vs. Fuel Cells)

Batteries have come a long way since Nikola Tesla himself did fundamental research on them in the late 1800s. Current go-to materials for EV power storage batteries include exotic compounds and elements such as nickel-cadmium, nickel-metal hydride, and lithium. “Lithium-ion (Li-ion) and Lithium-Iron-Phosphate (LiFePO4) cells have so far provided the biggest boost in energy density, between 300% and 400% more energy-dense than LA [lead-acid] batteries. Nissan Leaf, all Tesla vehicles, and most other electric cars and hybrid electric cars use Li-ion battery packs, though a few hybrid and electric car startups are toying with LiFePO4 chemistry.”[15]

Despite the popularity of Li-ion batteries, a lingering technical challenge is their long charge time and power required for each charge. A full-day charge for a Tesla Li-ion battery pack takes several hours and consumes about 2,500 Watts.[16] People who don’t have access to a charger at work typically charge their Tesla batteries overnight at home. Charging EVs puts a new strain on electrical grids.

Another option for powering EVs is the fuel cell, which has been around for decades.[17] “Like a battery, a fuel cell harnesses a chemical reaction to produce energy in the form of electricity. More specifically, hydrogen fuel cells generate electricity, water and heat from hydrogen and oxygen.”[18] A fundamental issue with hydrogen fuel cells is the storage of the hydrogen in the car before its use; one wants to have as much hydrogen as possible on-board in some compressed form to ensure long mileage. Simple hydrogen liquefication doesn’t go far enough; one must resort to trapping the H2 molecules inside or with other solid materials and then enabling their easy release.[19] Nanomaterials encapsulating or bonded to H2 have been researched for several years to do that.[20] Carbon buckyballs, nanotubes and other exotic molecular shapes have all been considered for their ability to store H2 and release it when needed. e.g., [21][22][23]

Despite their comparative environmental friendliness (hydrogen fuel cells emit only water vapor and warm air), fuel cells still have a ways to go before they can, if ever, usurp the efficiency and cost-effectiveness of batteries. “Hydrogen fuel cells offer a potentially very clean, energy dense and easy to recharge energy source for vehicles and other systems, but they are currently complicated, expensive and dangerous to operate. In comparison, lithium-ion batteries, although less energy dense and slower to recharge, are as clean, much cheaper, easier and safer to handle.”[18] Although a few models of automobile EVs powered by hydrogen fuel cells have been manufactured, they have not received wide adoption. Some fleets of buses, as well as forklifts, use hydrogen fuel cells. Wider adoption will require not only major advances in H2 storage capabilities, but also a substantially expanded fleet of charging stations dispersed across the United States.[24] For the next several years, we will most probably see hydrogen fuel cells in only limited use within EVs.

Sensors and Computation

One doesn’t realize it after some experience, but the action of driving actually requires substantial attention and skill. Distracted driving (whether via texting, taking a phone call, or drunk driving) is well-documented to cause accidents.[25] Clearly, computers don’t have the social or biological needs that humans do, but ultimately AV systems still have several significant technical specifications for sensors and computation – a few examples include:[26]

  • High-resolution, 3D maps. When driving, one needs to know where one is going. While humans can look at maps and easily adjust to changing driving conditions, such actions are not trivial for an AV. “Self-driving cars currently lack the common sense needed to navigate using a traditional human map. Since they can’t interpret context, they need to rely on a map signal that doesn’t cut out in tunnels, waver in precision or fall out of date…these machine maps must meet several key demands:
    • Incredible precision, so the car can compensate for its lack of understanding context and know where it is within 10 cm.
    • Granular instructions, like which lane the car is in, the traffic rules that apply to that lane, and even overhead clearances and road elevation.
    • Constant connection, which continues to provide information even when GPS signals are weak or missing.”[27]
    • Advanced sensors systems – increasingly prototypes incorporate LiDAR, Radar and other sensor systems that compensate for the AV systems lack of ability in some areas, but also add capabilities beyond human drivers. BootstrapLabs’ portfolio company AEye is an example of such a technology.[28]
  • Data processing. AVs place incredible demands on the volume of data needed to be processed rapidly. 5G or faster wireless speeds are said to be required. “When it comes to autonomous vehicles, the speeds and data processing capabilities needed to mimic the timing of human reflexes are incredible. Dr. Joy Laskar, co-founder and CTO of Maja Systems, believes the future self-driving car will generate approximately two petabits of data—the equivalent of two-million gigabits.”[29]
  • Object identification. Is that a bicycle or a deer ahead? While easy for a human to discern objects, AVs have troubles. One of the grand challenges with AVs is that objects on the road are in motion—by themselves and/or relative to the AV itself. Thus, AVs must not only process static images, but also moving video data. Some researchers are resorting to infrared cameras to ensure object visualization and identification, even in the worst conditions.[30]
  • Varying weather conditions. Whether you live in the mountains or on a beach, the weather can change quickly. Sensors must be able to accommodate the full spectrum of ambient conditions, including sun, rain, hail, snow, fog, ice and winds. Unfortunately, designing sensing capabilities that cover the full meteorological spectrum is challenging. “After years of testing, with hundreds of cars and vans deployed on public streets and private facilities, even the best autonomous-driving efforts still struggle with inclement weather…At the moment, autonomous cars rely on a patchwork of sensors: GPS, traditional cameras, radar, and lidar technology that bounces lasers off nearby cars and pedestrians. Mother Nature essentially sidelines two of those four applications: cameras are useless in fog and heavy snow, and lidar lasers careen wildly off raindrops and snowflakes. The remaining systems also have major deficiencies. GPS connections can be slow and spotty, and radar is lousy at distinguishing obstacles—is that a pedestrian or a seagull?”[31] New technologies and software are needed to ensure our trips aren’t limited by changing weather.

Opportunities for Applied Artificial Intelligence (AI)

The various challenges described above present unique opportunities, as well as the basic necessity, for Applied AI. As noted in a previous note from BootstrapLabs and FutureGrasp™, LLC,[32] AI can be leveraged positively for energy utilities. Similar thoughts apply in the context of EVs. Let’s review briefly how Applied AI can help us realize the dream of electrically powering AVs:

Advanced materials research. Over a hundred years ago, Thomas Alva Edison used a purely empirical approach to narrow his choice of a filament material for the first sustained light bulb. Later as computers became available, the ability to not only record data, but also to simulate how a material would behave with a given set of material properties and desired results became possible. Now with Applied AI we can take modeling advanced materials to a new level. Companies such as Citrine Informatics leverage AI and big data analytics to go way beyond the Edisonian approach. “Our platform ingests and analyzes vast quantities of technical data on materials, chemicals, and devices to streamline R&D, manufacturing, and supply chain operations for any organization that produces a physical product.”[33] Such AI-driven research can accelerate the development of new batteries and fuel cells. Instead of empirically assessing millions of compounds or spending countless hours programming and running simulations, AI can cut the time and budget spent on finding new advanced materials. Companies such as Toyota[34] and Tawaki,[35] as well as US Government efforts such as DARPA’s Make-It program[36] are using AI to tease out from Mother Nature new synthetic molecules that could be used in next-generation battery and fuel cell technologies.

Optimizing AV Performance. The aforementioned set of sensing and computation challenges for AVs all have two major issues in common: data and its processing. The sheer amount of data that a single AV accumulates on the road is astounding. “A single autonomous test vehicle produces about 30 TB per day, which is 3,000 times the scope of Twitter’s daily data.”[37] This volume of data overwhelms CPUs[38] or other lesser chips. On-edge computing (where the compute is done at the site of data generation) is also paramount as one would certainly prefer not to have loss of WiFi result in a sudden loss of computation and AV control. AI can assist with both these issues. Large data processing using AI with on-board GPU[39] packs is becoming now the go-to solution for AVs.[40][41]

Fulfilling the Dream

Autonomous vehicles powered by electricity require that AI move out of university and industry labs and into the real world—that it is actually “applied.” One means of making that happen is through the creation of start-ups that have a vision of changing the world. BootstrapLabs, a venture capital group in San Francisco, recognizes that challenge and invests in founders that dream big and are solving today’s hardest problems by applying AI to shape a better future.

Across the spectrum of challenges within AVs and EVs, applied AI has the potential to help resolve thorny problems, whether they be in discovering the next-generation advanced material for batteries or fuel cells, or in optimizing datasets and their processing. Achieving the dream of powering AVs will require capabilities beyond what a human or even big data analytics can accomplish. Ultimately, AI can help us make driving a pleasure again.

Notes (all web links accessed November 2018)

[1] FutureGrasp™, LLC,

[2] G. Salvidia, “Stuck In Traffic? You’re Not Alone. New Data Show American Commute Times Are Longer,” September 20, 2018, NPR,

[3] “These 25 cities have the worst commutes in America,” October 16, 2018,

[4] Intergovernmental Panel on Climate Change, “Global Warming of 1.5°C,”

[5] U.S. Energy Information Association, Frequently Asked Questions,

[6] Of course, a debate rages about whether EVs are actually more environmental than cars with combustion engines. Electricity doesn’t just magically appear – it has to come from somewhere, and those energy sources themselves may be big polluters. One must crawl down the whole electron chain to learn whether the local energy utility is primarily burning fossil fuels or using renewable energy sources.

[7] L. Dormehl, S. Edelstein, October 28, 2018, “Sit back, relax, and enjoy a ride through the history of self-driving cars,” Digital Trends,

[8] No pun intended

[9] Golf carts typically have speed-limit governors on them, as one author [TAC] experienced when he caddied in his teenage years. Some friends of his learned the hard way that disabling the governor still doesn’t give a cart enough power to jump a creek.

[10] “The History of the Electric Car,” September 15, 2014, Department of Energy,

[11] M. Joselow, “The U.S. Has 1 Million Electric Vehicles, but Does It Matter?,” October 12, 2018,

[12] Although one million vehicles still only represents less than one percent of all wheels on roads in the United States

[13] Tesla,

[14] Including its 2017 Easter Egg of a software upgrade: ‘Ludicrous Mode,’ providing 0-60 mph in under 2.3 seconds

[15] Chilton, “A Short History of Electric Car Batteries,”

[16] Energy Sage, “Tesla Model S and Model X charging: everything you need to know,” March 27, 2018,

[17] One author [TAC] saw a prototype hydrogen fuel cell car at General Motors (GM) in 1990 during his mechanical engineering summer internship there. Sadly, the vehicle never made it off the research floor.

[18] E. Wertheimer, “Hydrogen Fuel Cells vs Lithium-ion Batteries in Electric Vehicles,” June 20, 2018, Furo Systems,

[19] Department of Energy, “Hydrogen Storage – Basics,”

[20] “Basic Research Needs for the Hydrogen Economy,” May 13-15, 2003,

[21] E. Erünal, F. Ulusal, M.Y. Aslan, B. Güzel, D. Üner, “Enhancement of hydrogen storage capacity of multi-walled carbon nanotubes with palladium doping prepared through supercritical CO2 deposition method,” International Journal of Hydrogen Energy, 43 (23), June 7, 2018, 10755-10764,

[22] T. Liu, “Buckyball’s Hydrogen Spillover Effect at Ambient Temperature Observed Experimentally for the First Time,” March 26, 2018, Chemical Communications Blog,

[23] Y. Liu, et al., “Metal-assisted hydrogen storage on Pt-decorated single-walled carbon nanohorns,” 50 (13), November 2012, 4953-4964,

[24] Wikipedia, “Fuel Cell Vehicle: Buses,”

[25] National Safety Council, “Distracted Driving Research, Infographics,”

[26] A. Marshall, “After Peak Hype, Self-Driving Cars Enter The Trough Of Disillusionment,” December 29, 2017, WIRED,

[27] N. Garg, “Self-driving cars need a new kind of map,”

[28] AEYE,

[29] B. Khosravi, “Autonomous Cars Won’t Work – Until We Have 5G,” March 25, 2018, Forbes,

[30] N. Pinon, “Next-generation technology is coming to a self-driving car near you,” November 7, 2018, PhysOrg,

[31] K. Stock, “Self-Driving Cars Can Handle Neither Rain nor Sleet nor Snow,” September 17, 2018, Bloomberg Businessweek,

[32] T. Campbell, N. Wadstrom, (November 26, 2018), “Banish the Darkness with Artificial Intelligence,” BootstrapLabs Blog,

[33] Citrine Informatics,

[34] M. Moon, “Toyota is using AI to hunt for new battery materials,” March 30, 2017,

[35] Tawaki, “What If Artificial Intelligence Finds New Battery Materials,” June 1, 2017,

[36] Make It, DARPA,

[37] J.A. Amend, “Storage Almost Full: Driverless Cars Create Data Crunch,” January 17, 2018,

[38] Central processing units

[39] Graphics processing units

[40] See for example, NVIDIA’s overview of GPUs in AVs:

[41] For an overview of advanced semiconductor technologies relative to AI see: T. Campbell & R. Meagley, Next-Generation Compute Architectures Enabling Artificial Intelligence – Part I of II, 2 FEB 2018, and T. Campbell & R. Meagley, Next-Generation Compute Architectures Enabling Artificial Intelligence – Part II of II, 8 FEB 2018,

BootstrapLabs Applied AI Workshop: AI and Human Capital


Date and Time: Wednesday, January 23, 2019

Location: San Francisco

Registration: This event is INVITE ONLY. You can request more information at

Come join us to learn more about how Artificial Intelligence will revolutionize the enterprise of tomorrow, and how Applied AI solutions can help executives and leaders around the world better manage, attract and engage their employees and customers.

As the Fourth Industrial Revolution unfolds, companies are seeking to harness new and emerging technologies to reach higher levels of efficiency and compete on a global scale for innovation, customers, and talent.

Artificial Intelligence is here to stay, and will drastically change the way we do business and live. The economic and social impact of artificial intelligence technologies will ultimately depend on how quickly organizations and institutions are able to learn, adapt, control, and ultimately wield these powerful technologies for the greater good.

Over the last 30 years, the allocation of market capitalization value for corporations has shifted from 80% being assigned to tangible assets (machinery, plants, buildings, etc), to 80% being assigned to their intangible assets (brands, intellectual properties, trade secrets, business processes, and talent).

They are many opportunities and challenges ahead, and as a Venture Capital firm focused on Applied AI technologies, BootstrapLabs believes that the success of any business today, more so than any other time before, will ultimately be determinate by their ability to not only adopt AI solutions across their entire business but also, and maybe most critically, apply AI to known critical factors such as attracting, retaining, developing and managing their pool of Human Capital as well as delighting their customers.


  • 5:00pm – 5:25pm: Registration & Networking
  • 5:25pm – 5:30pm: Greetings and Introduction by BootstrapLabs
  • 5:30pm – 5:45pm: “Applied AI and Human Capital Investment Trends”, Ben Levy, Co-Founder & Managing Partner, BootstrapLabs
  • 5:45pm – 6:00pm: “Bringing AI in the Enterprise – Legal Considerations”, Natalie A. Pierce, Partner, Littler
  • 6:00pm – 6:20pm: “Scaling your Enterprise Conversational Knowledge”, Jonathan Eisenzopf, CEO,
  • 6:20pm – 6:40pm: “Corporate Culture 2.0”, Sabrina Atienza, Co-Founder & CEO, Stealth Mode Startup
  • 6:40pm – 7:30pm: “Applied AI and Human Capital Panel + Q&A”, moderated by Debi Hirshlag, Advisor and former VP, HR at Workday, Flex, Trimble & Ariba
  • 7:15pm – 8:00pm: Networking Reception


Ben Levy, Co-Founder & Managing Partner, BootstrapLabs

Jonathan Eisenzopf, CEO,

Natalie A. Pierce, Partner, Littler


Sabrina Atienza, Co-Founder & CEO, Stealth Mode Startup

Debi Hirshlag, Advisor and former VP, HR at Workday, Flex, Trimble & Ariba

Thank You To Our Host

Image result for littler mendelson logo

BootstrapLabs AAI18 Keynote and Fireside Chat – Death of Moore’s Law

Over the next several weeks we’ll be releasing the videos from the sessions from the BootstrapLabs Applied AI Conference 2018. The yearly conference organized by BootstrapLabs, a leading Venture Capital firm focused on Applied AI, that brings together over 800 members of the Artificial Intelligence community for a day of incredible speakers and exciting conversations.


  • Tom Campbell, Founder & President, FutureGrasp, LLC
  • Madhav Thattai, Chief Operating Officer, Rigetti Computing
  • Sateesh Kumar, Founder, Pathtronic

During this session, the speakers discussed the future of computing in the age of AI, Neuromorphic and Quantum computing and edge use cases and applications.

Some of the key takeaways from the session are:

  • The death of Moore’s Law as originally define has given birth to a variety of them depending on the technology leveraged: neuromorphic, quantum, among others.
  • There’s a shift in the industry from bigger companies to focus on creating their own use-case-specific chip architecture.
  • We need to bring more power to edge computing to really provide AI solutions that work and leverage insights in real time.

About Tom Campbell:
Thomas A. Campbell, the founder and president of FutureGrasp, which advises organizations on trends and impact of emerging technologies, and a special advisor to BootstrapLabs. Prior to this, Tom was the first National Intelligence Officer for Technology with the National Intelligence Council. Tom holds a Ph.D. in Aerospace Engineering Sciences from the University of Colorado at Boulder, and a B.E. in Mechanical Engineering from Vanderbilt University.

About Madhav Thattai:
Madhav Thattai is the COO of Rigetti Computing, a full-stack quantum computing company based in Berkeley, California. Prior to joining Rigetti in 2015, Madhav worked at Dell Computing for eight years in a variety of roles, most recently as Director of Product Operations. Madhav holds a Master’s degree in management from Stanford University, a Master’s degree in industrial engineering from the University of Michigan, and a Bachelor’s degree in mechanical engineering from Birla Institute of Technology and Science in India.

About Sateesh Kumar:
Sateesh is the Founder and CEO of Pathtronic Inc, a silicon valley start-up company focused on building revolutionary AI Processing Unit. Sateesh is a visionary and seasoned entrepreneurial executive and innovator with broad technology background. He has been advisor to multiple corporations, start-ups, incubators, and universities in the area of AI, Autonomous Vehicles and Drones, and more. As a former innovation and incubation executive at Cisco, he pioneered and led various initiatives in the area of IOT, Automotive and Connected Vehicles, Industrial Switching and Automation, Smart Cities, Mobile and Wireless, Fog Computing, Big Data and Machine Learning and more. Prior to Cisco, he founded and led multiple Silicon Valley venture funded start-ups. He has published extensively in international journals including IEEE/ACM and holds dozens of granted patents.

Banish the Darkness with Artificial Intelligence

The Need

Most everything we do in life requires electricity. Transportation, farming, homes, businesses, computing—they all necessitate the efficient movement of electrons from a power production source to an energy use site¹. Unfortunately, since Thomas Alva Edison first illuminated a Menlo Park street in 1879 – thus banishing the darkness 2 – the  demands humanity has placed upon energy utilities have skyrocketed and consequently at least parts of our energy grid have become brittle and prone to failure. Moreover, the complexity of operating local, regional and national systems that are susceptible to cyber-attacks has made electricity a critical topic for national security.

World population has quintupled since the first sustained artificial light in the late 1800s. With more than 7.6 billion people now teeming on our planet and all of us needing increasing amounts of energy 4, we cannot rely anymore on simple dynamo generators creating electricity and sending it straight to the desired location. On the contrary, our electricity grids are highly complex with numerous power sources (coal, gas, oil, nuclear, and renewables—biomass, solar, wind); switching and amplifying stations; transformers; above and below ground wiring; and storage batteries. Unsurprisingly, even considering conservative trends, our energy use is growing rapidly:

“In the New Policies Scenario, global energy needs rise more slowly than in the past but still expand by 30% between today and 2040. This is the equivalent of adding another China and India to today’s global demand.” 5

Moreover, our already highly electrified society will become even more dependent on charged electrons with new technologies. The Internet of Things (IoT), which requires cloud and edge computing at massive scale, is forecasted to exceed 75 billion devices connected to the internet by 2025 (up from 23 billion presently).6 Research into autonomous vehicles is rapidly approaching deployment stages. Testing underway in Phoenix, AZ and Pittsburgh, PA may be halted temporarily because of bad algorithms causing accidents or irregular policies, but other countries are not waiting around for the United States to perfect driverless cars. China is building cities specifically designed for autonomous vehicles.The Hyperloop – Elon Musk’s dream of accelerating people and cargo in a vacuum tube to near the speed of sound – is being tested in the United States and Europe.8

Population growth is also spurring construction of whole cities from scratch; examples abound in Asia. China plans on constructing a new city the size of Chicago (2.7 million people) with the latest technologies, and thus high energy demands.5 With support from Singapore, the Indian state Andhra Pradesh is building a new capital. “It will be a stupendous 7,235 square kilometres, 10 times the size of Singapore’s own 716 square kilometres.”9

The Risks

With so much riding on our need for energy, we must avoid costly failures. For example, when one author [TAC] worked in the semiconductor industry, the great Northeast Blackout of 2003 was tripped only a few miles from the production facility where he worked. The source of the power failure was overgrown tree branches that had fallen on a transformer. Normally, this would have remained a local issue, but the lead utility failed to react quickly enough to disengage the switches to other grids, thus causing a cascading, multi-grid failure for 50 million people from Ohio to New York to southeast Canada. People were trapped in subway cars and elevators for hours; cell phone service was disrupted for millions. 10 11

Perhaps even more dangerous than a lack of sensors on the grid and failed switches is the looming possibility of a cyber-attack targeting fragile points in the electrical system. Industry is spending billions in its attempts to strengthen cybersecurity of the brittle grid.12 “U.S. utilities will spend a cumulative $7.25 billion in security from now until 2020, with distribution automation assets as the core focus.”13 Grid security is a major issue within the US Government; simulations of cyber-attacks are routinely done by groups such as DOE and DHS.14 15

While a completely failure-proof grid may be impossible with the moving demand target caused by new technologies online and rapidly growing electricity demands, companies and government agencies must nevertheless do their best to anticipate and to prevent both accidental and nefarious situations before they happen. This presents a unique opportunity to engage newly accelerated capabilities of computers, especially artificial intelligence (AI).

The Opportunities

A core challenge with global electrical grids is their growing complexity. Growing exabytes of data from billions of IoT sensors, coupled with too-fast-for-humans reaction speeds required by increasing power demands, make a purely human-controlled electrical grid impossible. To sustain our growing needs, we must resort to a more digitalized approach.

There has been much talk recently about the so-called “smart grid,” defined as an electricity supply network that uses digital communications technology to detect and react to local changes in usage. 16 A smart grid has three major facets: data from sensors, computational power and optimized algorithms. Let us discuss the third point in detail.

Algorithm development has progressed in concert with Moore’s Law – the doubling of the number of transistors on a given semiconductor chip roughly every two years. In the early days of computers, there were a limited number of programming languages – Basic, Fortran, C/C++, Cobol, etc. Nowadays, companies such as Google develop their own proprietary operating systems as a routine course of smart business action. Increasingly, those algorithms are leveraging the power of AI.

While there are many definitions for AI, it can be defined simply as the ability of a machine to perform tasks commonly executed by a human. 17 AI is presently the hottest ICT 18 market sector. PwC estimates that global GDP will increase $15.7 trillion (a +14% boost compared to today) by 2030 as a result of AI. 19 20 Such a huge economic contribution cannot be understated, and organizations are investing aggressively accordingly. Venture capital funding pumped almost $5 billion into AI startups in 2017 alone. 21 AI has become a top corporate spending priority, with many hundreds of billions of dollars devoted to nabbing top talent and to securing algorithmic leadership. 22 There is a global race among governments to capture the title of AI world leader.  23

The reason for this flood of venture capital, corporate and government funding is that AI can solve problems far faster than humans, and in some cases solve problems that no human can. While we humans are smarter than any other living being on Earth, we are still rather dumb when it comes to absorbing and processing quickly vast amounts of data. The human mind is limited also to mostly working on a single task; despite some claims, we are generally horrible at multitasking. 24 Computers don’t have those limitations.

How might AI help the energy sector? We suggest there are two primary means by which advanced AI algorithms can improve efficiency, enhance safety, and improve the bottom line for energy utilities: grid optimization and cybersecurity.

Grid Optimization. To reap the benefits of the smart grid, AI will be a true necessity. “AI will be the brain of this future smart grid. The technology will continuously collect and synthesize overwhelming amounts of data from millions of smart sensors nationwide to make timely decisions on how to best allocate energy resources. Additionally, the advances made from ‘deep learning’ algorithms, a system where machines learn on their own from spotting patterns and anomalies in large data sets, will revolutionize both the demand and supply side of the energy economy.” 25

A challenge for utilities is maintaining consistent power. When there is a sudden increase in demand, the go-to reaction for utilities is to power-up ‘peaker plants’ that run on fossil fuels, generally natural gas. 26 Able to be turned-on within minutes of the detection of a demand spike – for example, due to increased air conditioning or heating requirements from a weather front moving through an area – peaker plants are unfortunately terribly inefficient and polluting. AI might assist in diminishing the use of peaker plants by leveraging advanced forecasting capabilities – for example, considering weather forecasts, regional demand cycles, and smart meter sensors in a single holistic package. The solution of such multidimensional problems is a strength of AI over humans. Such efforts could save utilities significant funds that they could funnel into further grid improvements.

One recent success story in using AI for energy optimization is that of Google’s deployment of algorithms to reduce electricity consumption in their server farms. Google has thousands of data centers worldwide to run search, store and process emails, etc. Those countless banks of GPUs 27 and memory storage systems consume tremendous amounts of electricity and tend to overheat. Because of their increasing presence and inherent inefficiencies, it is estimated that 2% of the world’s energy is consumed by data centers. 28 Both to save costs and to show-off its earlier purchase of the British AI startup DeepMind, Google decided to test AI in improving the cooling efficiency of their computer banks. “The autonomous AI control system initially led to a 12 percent improvement, which over nine months of operation increased to around a 30 percent improvement, with further improvements expected over time as its decisions are improved by having more training data. Google said in the long term that there is potential to apply the technology in other industrial settings.”29

Finally, AI can assist in converting incoming power to be acceptable to a grid. Every energy utility has a mix of power sources that it taps to provide its customer base their electricity. That mix is optimized based on price and, more recently, social perception for use of renewables. A challenge with multiple power sources – natural gas, oil, coal, nuclear, renewables – is to seamlessly integrate them all into the single grid. For example, wind and solar power sources require special converters to enable them to plug into the grid. “The high penetration of renewable energy systems is calling for new more stringent grid requirements. As a consequence, the grid converters should be able to exhibit advanced functions like dynamic control of active and reactive power, operation within a wide range of voltage and frequency, voltage ride-through capability, reactive current injection during faults, and grid services support.”30 AI could assist with energy conversion from renewables by leveraging deep learning to identify key performance optimization criteria and thus enable efficient uptake of the generated power into the smart grid.

Cybersecurity. Ironically, the very action of creating a smart grid makes it more vulnerable to cyber-attacks. Standardizing hardware and software, as well as creating a high degree of connectivity, enables hackers easier access to the grid through a greater variety of means. Programmed backdoors, firmware chip hacks, and even fake chips on motherboards 31 can all compromise the security of a given system.

Humans are inherently limited in what we can accomplish in cybersecurity. Our minds require rest, eating, drinking, etc.; cyber-attack bots require none of that. Thus, even though we might have the best intentions to be ever-diligent in the face of malware or phishing attacks, eventually we will slip up and allow nefarious actors access to sensitive computer systems.

Four years ago, seeing the rapid advances occurring in AI and knowing the challenges of human-centered cybersecurity, one author [TAC] forecasted that soon cybersecurity would enter the realm of “AI vs. AI,” i.e., AI algorithms would be both sources of attacks and defense. We are close to that time now. In the last few years, there has been a surge of interest in coupling the powers of AI, especially deep learning, to cybersecurity. 32 Thousands of companies globally now claim some form of AI in their cybersecurity offerings. Although it is debatable whether all these startups and corporate entities are truly using AI (some startups treat “AI” as a form of pixie dust—sprinkle it in a pitch in attempts to get funding), it makes sense to leverage the powers of learning algorithms to monitor and to react to cyber-attacks.

Ultimately, using AI for cybersecurity may not be just a choice but an imperative in the energy industry. In the United States, the complexity of the grid—with more than 8,000 power plants, 200,000 miles of high-voltage transmission lines, and 5.5 million miles of local distribution lines 33 34—necessitates that even local utilities must think hard about how they monitor their systems for intrusive cyber-attacks.

The Means

So how do utilities leverage the power (no pun intended) of AI? One challenge for cash-strapped utilities is that AI experts are in such demand that a starting salary for a star AI programmer can exceed seven dollar figures35. Thus, a more cost-effective approach may be partnerships among established startups and corporate entities that already have in-house expertise in AI. One such partnership was announced recently between BootstrapLabs (a leading venture capital firm focused on Applied AI in San Francisco) and innogy SE (a leading Germany energy company):

“BootstrapLabs and the innogy Innovation Hub will coordinate globally to build the largest artificial intelligence community for energy ecosystems, and provide a combination of capital and support to Applied AI startups that reimagine the future of energy production, distribution and management across decentralized and interconnected energy services for consumers, machines, enterprises, and public sector agencies.” 36

Sebastian Niestrath, SVP Infrastructure Platform Ventures at innogy New Ventures LLC, further clarified: “The need to interpret massive amounts of data and use AI-supported algorithms for grid operations is becoming increasingly important, especially in Germany – where electricity from renewable sources has pretty much quadrupled during the last 15 years and where millions of solar panels are now installed on residential buildings.” Moreover, with the California Energy Commission unanimously voting 5-0 in favor of mandating that all single-family homes, apartments and condominium complexes of three stories or less require solar panels as of January 2020, California will quickly follow suit in the need for AI-supported algorithms for grid operations.

Further such engagements among all major energy players – including utilities, AI experts, and local/regional/national governments – will be imperative as grid demands and threats increase.

The Future

As our population continues to grow and more technology gets plugged into existing grids, it will be incumbent upon utilities to increase efficiencies to remain cost-effective and to avoid grid failures. Moreover, the growing threat from cyber-attacks will demand that power companies better protect their grids from nefarious actors. AI is one approach that can significantly help utilities move into this challenging world. Opportunities will be strong and risks could be minimized for those leaders who embrace the capabilities of advanced computation.


Thomas A. Campbell, Special Advisor, BootstrapLabs and Founder of FutureGrasp, LLC

Thomas A. Campbell, Ph.D., is Founder and President of FutureGrasp, LLC (, which advises organizations worldwide on trends and implications of emerging technologies.

Thomas is also serving as Special Advisor of BootstrapLabs, for Applied AI intellectual propriety development as well as assisting BootstrapLabs to expand its network reach and meet new opportunities into government sectors.

From February 2015 to August 2017, he was the first National Intelligence Officer for Technology (NIO-TECH) with the National Intelligence Council (NIC) in the Office of the Director of National Intelligence (ODNI). Tom’s insights have informed senior policymakers, enabled millions of dollars in industry and academic funding, broken ground in multiple new research areas, and kept diverse groups abreast of the rapid pace and implications of technology change.

Dr. Campbell is focused on emerging & disruptive science and technologies, especially identifying, tracking, forecasting and implications. He has extensive experience in government, academia, and industry – nationally and internationally. More about Tom at this link here.

Nicolai Wadstrom, Founder & CEO, BootstrapLabs

Nicolai is the Founder, CEO and Managing Partner of BootstrapLabs, a leading venture capital firm, based in Silicon Valley and focused on Applied Artificial Intelligence.

Nicolai has spent all of his professional life building technology companies. He started his first business at the age of 15 in the late 1980s and in 2008 he founded BootstrapLabs to build a scalable investment platform focused on being the most valuable partner for entrepreneurs to build successful companies.

With decades of operational and entrepreneurial experience, and having invested in, advised, and mentored over 30 companies, Nicolai works with investment decisions, and post investment mentorship to support scaling for BootstrapLabs’ portfolio companies.

BootstrapLabs is a Venture Capital company focused on Applied Artificial Intelligence that combines Venture Capital and Human Capital to build companies that define our future.

BootstrapLabs unique Venture Builder platform provides all the resources top-tier entrepreneurs need to take their companies from innovation to a Scalable Product Market Fit and from there to growth.

Prior to BootstrapLabs, Nicolai among other things founded a cutting edge Enterprise Software company (in the ECM and EAI space), he also co-founded and IPO’d the first CFD/FSB trading platform in the Nordics, and pioneered Virtual Reality in 1996. More about Nicolai at this link.


  1. “Power is the capacity to use Energy…Power is like the strength of a weightlifter and Energy is the measure of how long he can sustain the output of power…Power is ‘watt’ and Energy is ‘watt-hour’.” 
  2. C. Klein, (December 17, 2014), “When Edison Turned Night into Day,” History,
  3. “World Population by Year,”
  4. The population considered in the middle class, and thus more prominent consumers of energy, is exploding globally. H. Kharas, February 17, 2017, “The unprecedented expansion of the global middle class,” Brookings,  
  5. “World Energy Outlook 2017,”
  6. “Internet of Things (IoT) connected devices installed base worldwide from 2015 to 2025 (in billions),”
  7. Kai-Fu Lee, “AI Super-Powers: China, Silicon Valley, and the New World Order,” 2018, Houghton Mifflin Harcourt, Boston.
  8. Hawkins, April 15, 2018, “World’s third hyperloop test track is now under construction,” The Verge,
  9. C. Cram, January 7, 2015, “Why Singapore is building a new Indian city 10 times its own size,” The Guardian,
  10. History Editors, August 21, 2018, “2003 Blackout hits Northeast United States,” This Day in History,
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