After much debate, the debate about job losses due to artificial intelligence is settling into consensus. Historically, we have never experienced macro-level unemployment from new technologies, so AI will make many people unemployed in the long run, especially as most developed countries face a shrinking working-age population. unlikely to. However, businesses are adopting ChatGPT and other generative AI so quickly that we may see significant job losses in the short term.
Compare AI to the rise of electricity around the early 20th century. It took decades for the factory to switch the central driveshaft of each machine from steam power to electric motors. The layout had to be rearranged to take advantage of the new electrical technology. This process happened slowly enough that the economy had enough time to adjust, and at first only new factories adopted motors. As electricity creates new jobs, furloughed workers in steam-power factories may migrate. Rising wealth has created entirely new industries involving workers and raised expectations.
Something similar happened with the proliferation of computing in the mid-20th century. It moved at a faster pace than electrification, but still slow enough to prevent mass unemployment.
AI is different because companies are rapidly integrating AI into their operations, potentially increasing job losses before benefits are realized. White-collar workers can be particularly vulnerable in the short term. In fact, critics are describing an “AI gold rush” rather than a bubble by forward-thinking chip makers such as NVIDIA. Goldman Sachs recently predicted that companies would take advantage of this and cut his quarter of all current work tasks in the US and Europe. That probably meant tens of millions of people would lose their jobs, especially those who thought their expertise would provide them with job security.
There are two possibilities left to mitigate this risk. The first is for the government to step in and delay the commercial adoption of AI (very unlikely) or offer special welfare programs to support and retrain the newly unemployed. .
But there is another often-ignored possibility that does not have unintended consequences of government intervention. Some companies are rapidly adding generative AI to their systems not only to automate tasks, but also to enable employees to do more than they used to, i.e. to make them more productive. Integrated. A radical redesign of corporate processes can trigger all kinds of new value creation. If many companies did this, as a society, enough new jobs would be created to escape the short-term eviction trap.
But will it? Even the least aggressive companies tend to be fairly committed to cutting costs. Innovation, however, is another matter. In the past, I wasn’t worried about this because there was enough time for a few aggressive companies to slowly change the industry. They have innovated over time to replace the slowly disappearing jobs. This innovation has created new jobs and kept unemployment low. But macroeconomically speaking, we can’t afford to spend time migrating to AI.
An alternative to relying on government, therefore, is to have many firms innovate fast enough to create new jobs at the same rate that the economy as a whole is eliminating existing ones. Generative AI is rapidly permeating business and society, but its speed also presents an opportunity for companies to increase the pace of innovation. If we can get enough companies to go on the offensive this way, we won’t have to worry about job losses due to AI.
Of course, businesses don’t and shouldn’t rely on AI to solve their macroeconomic problems. Fortunately, they have good business reasons for doing so. Companies that create opportunities from AI are well positioned for long-term growth.
Go on the offensive with AI
Already, we can point to aggressive companies looking to innovate in AI. Elon Musk, who pioneered reusable rockets and electric cars, now promises to make Twitter as an AI leader as Microsoft and Google. But Musk is a notorious outlier, and the jury is still out on Twitter. So what does it mean for companies to go on the offensive against AI?
To answer this question, let’s take a look at what makes companies so successful in dealing with the kind of change we’re seeing today. One of us (Tabrizi) assembled a team of researchers to study 26 large companies with excellent data from 2006 to 2022. The team divided companies into high, medium, and low agility and innovation groups over time and provided comparable data and case studies for each.
What differentiates an agile, innovative company from one that remains neutral or defensive? The team narrowed down the differentiators to eight drivers of agile innovation. Survival purpose, obsession with what customers want, Pygmalion-esque influence over colleagues, start-up mindset even after scaling up, prejudice against audacity, radical collaboration, readiness for control. Works in tempo, and bimodal. Most leaders admire these traits, but large organizations have found it very difficult to maintain these traits over the long term.
Tabrizi has written elsewhere about how Microsoft has gone on the offensive to become a corporate leader by overhauling its hierarchy and pursuing partnerships like Open AI. But other companies are doing similar things with AI as a result of these impetus. Let’s focus here on two of the most important factors: boldness bias and startup spirit. Appropriately implementing these drivers can drive change across the organization, leading companies to greater agile innovation.
prejudice against boldness
In the near future, companies that invest in AI may benefit from it. But mere investments can only yield incremental returns. The numbers may look good, especially when it comes to cost savings. But the company will miss out on opportunities to make substantial profits by creating real value or a defensible niche for the future. Prudent investing will not protect you from competition in the long term, nor will it help you with the macroeconomic challenges we face.
This is the problem with any new technology. Proceed with caution and you’ll probably be fine. Big companies hate risk. As such, large companies act as well-oiled machines, churning out affordable and reliable products. That’s why many companies outsource innovation by acquiring startups. And even that approach often leads to timid improvements. All successful organizations, especially large ones, like to minimize risk and daring. But as Brené Brown points out, “You can choose courage or you can choose comfort, but you can’t choose both.”
Boldness has become a corporate trope, with leaders protesting excessively, but when it comes to AI, companies need to really engage in embracing rather than minimizing risk. Let’s take Adobe’s example. His Photoshop program in the company has held the largest share in the photo design market for many years. Adobe could have played it safe when generative AI came along and adopted it on a smaller scale while seeing how the technology worked. That’s what Kodak did with digital photography and what Motorola did with digital phones. But instead, Adobe has built generative AI deep into his Photoshop, allowing ordinary users to create all sorts of videos that were previously impossible to create. Adobe may have viewed AI as a threat or a distraction and has continued to improve Photoshop without AI. But its leaders had the courage to aggressively invest in AI to improve what users can do.
Deep in tech, chip maker Nvidia is making headlines for offering the best semiconductor chips for AI. To an outsider, the company may seem like a lucky company that got the right technology at the right time. But Nvidia’s current success is no accident. Over the past decade, we have actively acquired and developed our AI expertise, including creating customized chips and software. It is hoped that this aggressiveness will continue, allowing Nvidia to not only offer higher value products, but also better use of AI rather than simple cost cutting.
Boldness doesn’t work every time. But a bias for boldness is essential to overcome the ingrained risk aversion in the corporate hierarchy.
startup spirit
As bold as it is, equally important to AI success is embracing the spirit of an upstart, regardless of the age or size of the company. Startups are great at looking broadly at the market and pivoting quickly to what customers want right now. Larger companies have the resources to capitalize on these opportunities, but due to many barriers (and lack of audacity) and usually moving very slowly, start-ups enter the market faster. can do. Open AI, which beat Google on ChatGPT, had the best of both worlds. In other words, it had the unreserved start-up spirit that held Google back, but was well-resourced by Microsoft and other investors.
The startup mindset is about more than just courage and flexibility. It is also like a ferocious commitment to great achievements, a heroic journey to tackle great challenges. Instead of predictably churning out great products at scale (which is a perfectly worthy goal), startups want to build something special. So they value looking around and being flexible and partnering with others. They eliminate existing structures and prejudices, no matter how old and respected, in order to get the job done.
E-commerce giant Amazon has demonstrated a start-up spirit in its adoption of AI. As technology advanced over a decade ago, the company saw an opportunity to create a “smart speaker” as his new interface to the web. He didn’t have AI expertise at Amazon, but he found what he needed through hiring, acquisitions, and internal development. The result is the Echo speaker and the Alexa digital assistant. These weren’t just allowing people to order more items. This has opened up new channels for adding value (and jobs) in many areas. Amazon continues to invest aggressively in AI beyond Alexa, with CEO Andy Jassy promising the technology will “transform and improve virtually every customer experience.” said that
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Companies can’t adopt these drivers overnight, but they can start moving towards serious engagement with new possibilities. Most of these drivers also work on an individual level who seek purpose and fulfillment in their careers. They can embrace daring, start-up spirit and other obligations. Just like a company, an employee can actively invest in his AI by acquiring the necessary skills and experience, thereby not only protecting his career but adding value at a higher level. can do.
Many of our corporate activities consisted of mass production of highly reliable products at low cost. What is needed now to prevent mass unemployment is for more companies to break free from this discipline and accelerate the future of AI. The big danger is that most companies play it safe, invest cheaply, and do well in the short term.
Humanity will never thrive when we fear innovation. Imagine if the first humans were afraid of fire. Yes, they burned on occasion, but we might have been extinct if we hadn’t harnessed that power. I think the same is true for AI. We need to harness its power instead of fearing it. We must collectively achieve this higher level and leave it in the hands of all humans so that we can live.
