print(“Hello World!”): Why Tech is Important Now More Than Ever
Technology is not just changing the world but fundamentally altering how we interact with it
Hello World! Welcome to The Techtonic Shift and thank you for your readership. I’m starting this publication because I’m passionate about innovative technology and I want to share my interests and learning journey with the rest of the world. Specifically, The Techtonic Shift covers the rapid pace of technological progress and how it is transforming entire industries and challenging their raison d’être. From covering companies to trends to technologies to pioneers to VCs to more, this publication aims to add color to this evolution.
I’m sure at this point you’re telling yourself this looks like YET ANOTHER overzealous blogger vying for your attention and asking yourself why you should care about innovative tech NOW. Tech has certainly been in vogue for the last decade and there are a myriad of similar blogs across Medium, Substack, and the like. However, as we will cover later, the scope, scale, and speed of tech change are nearly unparalleled and have significant implications for our future. This is no longer about tech just helping profits or just being an amplifier; this is about tech increasingly acting as substitute as well as it birthing new industries. I hope that my perspective and experience can help bring interesting discussions on innovative technology; moreover, as someone who is passionate about the subject, from DeFi to gene therapy to space, I want share my active learning journey. In the future, the goal of this publication is to have articles on trends, cover specific companies, new technologies, analysis frameworks, interviews with key players in the entrepreneurship ecosystem, and even guest writers.
As a first post, I want to convince you why technology is so important NOW and what this means for the types and scopes of problems we will have to tackle. The following might sound a bit general, but it is the central thesis on which future posts will get more specific.
(Slowing) Exponential Increases in Power and Speed
It’s no secret that technology is undergoing an explosion in capability. Part of this stems from the consistent (at least on the surface) exponential growth in the power and speed of computing. As the figure below shows [1,2,3,4], since 2010, hard drive storage costs have decreased 3x, clock speeds have gone up 1.4x, and the number of transistors per microprocessor have gone up 16.2x. Cheap physical storage (cloud storage can be even cheaper) and much faster computing ability have propelled power-intensive technologies like AI and blockchain to greater prominence; implementing these is now cost-efficient and done with lower latency.
Though this is all good news, a closer look at the graph shows that, while the lines are displaying exponential trends, the rate of acceleration is dropping (i.e. a negative third derivative, “jerk” as it’s known in physics). Physical component fixed costs, innovation costs in making larger hard drives, and the shift to cloud removing competitive pressure [5] have worked together to slow the “march to zero” in storage costs. Clock speeds have resisted breaking through the 5GHz threshold owing to higher power (and cooling) requirements from Dennard scaling not holding, voltage issues from smaller wires, and transistor count acceleration going down. Moore’s law, which has historically predicted the doubling of the number of transistors in a microprocessor, has slowed down due to heat from the tight-packedness of transistors [6] and even from quantum tunneling concerns upending transistor states [7].
This is not cause for total alarm, but we should note that the growth in computational requirements of AI, for example, have outpaced the growth in computing capacity by 5x. We still can do robust technological development, but the rapid pace of change suggests capacity constraints will be pulled to the mainstream sooner rather than later. And while quantum computing is being researched and heralded as a game-changing solution to scaling issues, it is far off from efficiency and scale. So what does this mean for the future? First, it can mean investing in quantum computing to provide a positive industry shock. In the absence of the former (it is still an emerging technology), we can continue our progress on innovation by designing more efficient ways to use data and computing power: compressed/archived storage, AI programs to more efficiently process data, focusing on connections between datasets rather than doubling up on redundancies, etc. Lastly, firms can focus on aggregated solutions like cloud computing/storage, which significantly increase efficiency by lowering unused computing power and consolidating operations.
Tech is integrated, Tech is distributed, Tech is cross-generational
Tech has been scaling exponentially for decades; what makes now so special? More so than ever, technology has become a cornerstone of our society, our way of life, and the way we do business. Technological advances have increasingly great potential because they no longer affect just one process or one group; often they have implications across industries, users, and use-cases. Tech has become integrated, distributed, and cross generational: the confluence of these three elements amplifies the pace and impact of innovation.
Tech is integrated. As Standard & Poors so famously said [8] a few years ago, “Every industry is now a technology industry… and every company is now a technology company.” Tech is no longer confined to the IT GICS sector, but is a fundamental part of operations across the economy. Furthermore, it’s no longer enough for some companies to use some tech to gain a competitive advantage; for many types of applications, like cybersecurity programs, being AI-based is rapidly becoming the industry standard. For example, a survey of leading firms in 2020 found that over 90% invested in AI (though fewer reported widespread use) [9]. Globalization and maturation of older industries has contributed: with less low-tech growth opportunities and limits on labor efficiency, tech innovation for all sorts of processes is a way to drive scale in both revenue and costs. This focus on scale means that new technologies often have applications between sectors and processes; this means firms are more likely to sponsor and adopt innovations to get ahead. Blockchain is used not just in financial services, but also in manufacturing, contracts, etc. Quantum computing has expanded past academia to VC to large R&D spends from Intel, Google, and the like [10].
Tech is distributed. Computer prices have dropped 48% and cell phone prices 64% in the last 10 years [11], and currently 67% of people have mobile devices worldwide [12]. The barriers to access technology have dropped rapidly, and a larger swath of the globe has access to a universe of applications and the ability to create new ones. Consumerism, especially in the US, has driven further adoption of technologies outside software like wearables, drones, etc. All this is to say that the demand and consumer use cases for innovations continue to go up, and the more decentralized user network means that there are more points for innovation outside of research labs and large companies. It is not just lifelong programmers who can create an app today, and those with more intricate ambitions can better find the resources to do so in today’s interconnected society.
Tech is cross-generational. The traditional refrain that “old-timers don’t use technology” is fast becoming obsolete. People 25 and younger were born when the internet went mainstream, and people 50 and younger most likely started using the internet and associated technologies early in their careers (the internet and software is not the end-all-be-all, but it is the most popular tech interaction medium). This means that most of the workforce, especially in the US, has comfort with technology. Statistics support this: 93% of Millennials have a smartphone and 86% use social media, compared to 90% and 76% for Gen X and 68% and 59% for Baby Boomers (respectively) [13]. 54% of adults in the UK aged 65+ in 2019 reported using e-commerce, a statistic that greatly increased during COVID. Enterprise and consumer adopters of technology increasingly span ages and levels, and this is expected to continue to drive use-cases, demand, and even supply of tech as some go off and create their own. An important emerging trend is on the investment side spurring tech change. VC investment is not only at record highs, with 2020 seeing $300bn in global deal volume [14], but who is funding investments and spending on technology is also changing. In 2010, the Silent Generation and Baby Boomers held over 90% of US household wealth [15]. Currently, the two have less than 70%, with the Silent Generation’s portion sharply decreasing and the Baby Boomer’s portion having peaked and begun its decline. GenX and Millennials, at 27% and 5% respectively, are undergoing prime wealth accumulation and intergenerational transfers. This means that those who most use and are most comfortable with technology are deciding what new innovations to adopt and also where to allocate investment dollars. This trend is expected to drive significant funding into companies seeking to develop ground-breaking technologies.
Substitutions, not just Supplements
The previous two trends on exponential scaling and tech being a cornerstone have driven adoption and innovation, but what sets today apart from the past is the extent to which we can rely on technology to be a substitute rather than a supplement and the birthing of new transformational industries. To quote the tagline of this blog, “technology is not just changing the world but fundamentally altering how we interact with it.” When I say substitution I don’t just mean labor. Certainly some tech advances today are about automating labor, but in the past a substantial portion of these advancements were about supplementing processes: Bloomberg to enable traders, advanced flight computers to make pilots better, websites to expand brick-and-mortar’s reach (though they did end up replacing many). Our level of advancement today, combined with the demand-side willingness to adopt, means that innovations are increasingly rethinking whole processes and being able to automate non-rote and complex functions.
DeFi, for example, aims be a substitute for financial services, offering through smart contracts (lending, borrowing, exchanges, payments) what armies of people do currently at banks. Robots are not just limited to mundane sorting and processing, but, powered by AI, now can do everything from diagnosing and maintaining equipment [16] to smart assembly to surgery [17]. Even in corporate America, firms like Retrain AI are using ML to own much of the HR talent process and identify employee skill gaps and suggest career progression [18]. This is not to say that labor is being fully disintermediated or that innovations will no longer be supplements. Much of future innovations will remain supplements (or they would have a hard time getting adopted), but in the face of advancing substitutes, labor will have to move to more complex functions.
Separately, our pace of innovation has rapidly given birth to industries with ground-breaking potential. Gene therapy, aided by AAVs, CRISPR, and TALEN, promises to shift our view on what is medically possible by editing our genome (or epigenetic expression) in treating debilitating diseases like HIV, autoimmune conditions, and cancer. Self-driving cars are becoming a reality, and can alter transportation and even the occurrence of traffic. AI, better materials, propulsion technology, and the like have enabled a nascent space industry, not just for transport, but also for communication and even mining (latter is still a concept).
One of the hubs driving tech progress is artificial intelligence and machine learning (AIML). Developments in this field, neural nets, fuzzy logic, and deep learning, have enabled AIML programs to tackle complex tasks that require lots of cross-connections and context. In MarTech, for example, AIML has enabled previously-unavailable insights on high CLV opportunities and predicting customer purchases: Black Crow AI, Squark[19,20]. AIML, through better analyzing nonobvious connections and utilizing context/calculation speed to derive insights, is thus expected to accelerate our society’s progress (assuming we can keep up with its power requirements). Indeed, AIML, robotics, and interconnected networks have been pegged as key contributors to the current “Fourth Industrial Revolution” of “cyber-physical systems” [21].
Tackling Societal Problems, but Creating Others too
The widespread availability of tech, numerous innovations, better cost-efficiency, and adoption-openness of the demand-base have impacted the types of problems that can be tackled. Technology is no longer primarily a driver of profit and politics, but can also be utilized against pertinent societal problems: environmental degradation, homelessness, and inequality to name a few. Innovations in technology are accelerants to nonprofits, ESG for-profits, governments, and more to equip them with new mechanisms to be effective. The nonprofit New Story and the startup Icon are using 3D printing to create homes to house the homeless [22,23]; housing is often cited as one of the most effective ways to help the homeless into society [24]. SF-based nonprofit ShelterTech has created an app-based guide for Bay Area homeless and provides free Wi-Fi in shelters to navigate support resources [25]. Actium Health is using AI to counteract biases in healthcare and their approach resulted in more minority patient outreach [26]. Governments are combatting climate change through CleanTech, like the Croatian capital Zagreb which will adopt hydrogen-powered public transport [27].
With all the good that technology has the potential to do, it also has its own set of problems, several of which are societal stressors. Research has shown that the labor automation has significantly contributed to the wealth gap and rising Gini coefficients worldwide [28,29]. Using AIML “blindly” to make decisions (facial recognition, hiring, insurance, etc.) carries a high risk of introducing biases — racial, gender, or otherwise — due to limited context recognition [30]. AIML’s substantial power requirements pose climate issues from greenhouse gas emissions needed to run these programs [31]. Cashless economies make transactions easier and fight fraud/crime, but they disproportionately affect the poor and lower access to critical resources [32].
Does this mean we should not have AI, automation, or cashless transactions? The above is not meant to be an admonishment of technology or an argument against it. Rather, it shows that technology comes with benefits as well as costs; there is rarely a free lunch. As a society, we have to remain cognizant of these costs when making technological leaps because we risk leaving the marginalized behind. Moreover, in pushing the boundaries to invent the future, we must marshal resources to address the issues we create (correcting bias in algorithms, training labor to take on complex jobs in the face of automation, better access for the underbanked). These issues, combined with the opportunities that our technological progress creates, make our future complex and make it more important than ever to find innovative solutions.
Thank you again for your readership, and please subscribe for future posts!
