The AI apocalypse won’t happen overnight (& will be chill af)
The landscape of artificial intelligence is complex and dynamic, making any type of specific prediction impossible. Broader trends, however, do start to become clear, and it is easy to arrive at an earth-shaking conclusion: billions of individuals in the workforce and trillions of dollars of capital will forever be deployed differently as AI becomes more broadly adopted.
Forever.
Billions. Trillions.
Zillions.
Well, sure… but perhaps the drama should be toned down a bit. Zillion is not a number, and any useful development tends to change work forever (RIP carrier pigeons). AI’s impact will be an incremental paragraph in history, appended to the ‘Software’ chapter, but in no way warranting its own chapter the way globalization, or industrialization, or fire, or the wheel, or the Roman Road, or splitting the atom, or manned flight, certainly has. AI will appear slowly: incremental efficiencies will appear in software that had already added major efficiencies to whatever the software was already doing.
The stakes are nonetheless extremely high, and there will be changes. The large players will continue to try to be stealthy with their over-hyped and useless new AI tools. Upstarts will proliferate, clamor for attention, mostly die off… and a few very interesting winners will find their first niche of happy customers and build their empires from there. AI is not a panacea or a cure-all, nor is it a scourge that will rip up economies leavings humanity in the dust. It will, however, gradually change the nature of work, and it will (mostly) be beneficial for (almost) everyone.
It’s easy to get excited about what is in the pipeline. What was once the technology of the future is now being introduced to even the most important tenets of modern society–things as fundamental as everyday transportation, and as serious as cancer diagnostics.
Some types of transportation has long relied heavily on AI. As you probably know, planes essentially “fly themselves” with a pilot team present to make sure autopilot functions as expected and instructed.
Cars, however, have been hogging the spotlight lately, and rightfully so — most people spend vastly more time a car than they do in an airplane. The beginnings of self-driving cars started just as the internet did, with a military need for such technology. Google entered the game with it’s Driverless Car Project in 2009 with many other private technology companies, including Elon Musk’s Tesla, right on its heels. Since then, the race for usable technology is split among these big players, rideshare companies, and car manufacturers. Many of these players have put cars on the roads, although the death of an Arizona resident has suspended the Uber self-driving test program and sparked a great deal of discussion around the regulation of such technology.
As dramatic and tragic as these deaths are, one must remember the grisly and deadly reality of driving in general. The World Health Organization recently published a report concluding that approximately once every 25 seconds, someone is killed due to road traffic injuries. It is too early to conclude that AI-driven cars will be safer, but with this degree of mortality, careful small-scale experimentation will always be warranted considering the massive potential upside of getting it right.
In medicine, AI aims to reduce many inefficiencies in the field while staying within compliance of patient information protection laws. The balance is not an easy one to strike. Outdated hospital IT and archaic ways of routing information make the idea of AI infiltrating the industry hard to imagine. However, there are steps being taken in countless areas: machine learned CT readings, appointment reminders, and even early detection of disease, among a great many others.
Many of these endeavors show great promise of reducing the fatalities caused by accidents and inefficiencies in the medical industry.
Of course, some experimentation, and indeed some risk, is required to achieve better results — as is true of any technology advance in all of history.
Similar to the previous example of AI-driven vehicles, advances in the medical industry could save countless lives and AI should not be avoided due to vague fears of economic disruption.
Healthcare, automotive, gaming, military, and finance are the sectors most talked about in the world of AI. But this paints only part of the picture. AI can be employed to guide customer service, content creation and optimization, marketing models, research & development… the list goes on and on.
It is easy to imagine a world where the speed and precision of computers make all of our jobs easier, less mundane, and more efficient. It is also easy to imagine a world in which artificial intelligence threatens our jobs and of course–the world where artificial intelligence threatens our privacy. The reality of the world today is a marriage of the two, yet it is important to look past the fear-mongering headlines and look at it as a gradual improvement to systems that have been around for a long time.
As was true during any time in history, there will be challenges and adjustments associated with increased efficiency and progress. The better question, then, is not “is my job threatened by the potential of AI,” but rather–”in what ways will AI alleviate the burden of my smaller decisions and free up my time to do more with less?”
Now, more than ever, a single individual can get more done with fewer resources. For better or for worse, that raises the bar across the board, and employers and employees alike will continue to reset their expectations accordingly.
Of course, this is a simple way to explain an extremely complex topic. The way to approach the topic as a tech professional, investor, or entrepreneur, though, should be relatively simple. AI is coming (and fast). It’s here–and it is essential to approach the subject with flexibility and openness and to allow it to be a tool and accelerator for any endeavor you embark on.
About Andrew Boos:
Self-described ‘geek whisperer’ who specializes in building and leading the commercial launch of new technology ventures.
Leadership experience in enterprise sales, corporate M&A, and startup fundraising. Industry expertise in machine learning, artificial intelligence, financial markets, semiconductors, and enterprise-level software and IT security.
- Owner-Operator at Darwinian Ventures LLC
- Managing Partner & Founder at Darwinian Capital Management, LP
- Launch team & first business hire at Skyport Systems ($67m raised, acquired by Cisco Jan 2018)
- Chairman and CEO at Appfuel (acquired by Tune Inc Apr. 2014)
- M&A team at Juniper Networks (NYSE:JNPR)
- Yearly speaker, panelist, & mentor (Stanford GSB exec program, various accelerators, etc)
