In the AI Era, Maintaining an Entrepreneurial Mindset is as Important as Bold Innovation

[baoshi.rao]

After extensive discussions, society has gradually reached a consensus on the issue of 'AI-induced job displacement.' Over the past few centuries, we have never seen a new technology at the macro level that has caused unemployment so rapidly. Therefore, in the long run, AI is unlikely to lead to massive unemployment, especially as the working-age population in most developed countries is declining as a proportion of the total population. However, due to the rapid adoption of ChatGPT and other generative AI by companies, we may see a significant number of jobs replaced by AI in the short term.

Comparing AI technology development to the rise of electricity in the early 20th century, we find that factories took decades to transition from steam-driven central drive shafts to electric motors for individual machines. For business owners at the time, leveraging the advantages of emerging electric technology required a complete reconfiguration of industries. However, the adjustment process last century was very slow, giving the economy ample time to adapt. In the early stages of the transition between electricity and steam, only newly built factories used electric motors, so there was no massive job displacement. Additionally, electricity created new jobs, allowing workers laid off from steam-powered factories to transition into the electric industry. Greater wealth creation led to entirely new industries that attracted labor, raising workers' expectations for life.

In the mid-20th century, as computers became widespread, a similar phenomenon occurred. Although the technology developed faster than electrification, it still provided the economy with enough buffer time to avoid large-scale unemployment.

The difference with AI lies in the fact that the speed at which major enterprises are applying AI technology in daily operations is so rapid that a wave of unemployment arrives before the benefits do. In the short term, white-collar employees are likely to be the most affected. In fact, critics argue that society is facing an "AI gold rush" enabled by advanced chip manufacturers like Nvidia, rather than a bubble. Goldman Sachs recently predicted that European and American companies will use this technology to replace a quarter of the current human labor in business operations, particularly those workers who previously believed their expertise would protect them from job loss.

To mitigate this risk, there are two possible options. The first is government intervention—either slowing the commercial application of AI (which is unlikely) or providing special welfare programs to support and retrain the unemployed.

However, there is another often overlooked viable solution that does not trigger unintended consequences due to government intervention. Some companies are rapidly integrating generative AI technologies into their existing systems, not merely to automate tasks but to enhance employee productivity. By comprehensively resetting business processes, managers can unlock new potential for value creation. If many companies adopt this approach, society as a whole can generate enough new jobs to avoid short-term job displacement.

But will they do it? Even the most passive companies can manage control, but innovation is another matter. In the past, we didn’t need to worry about this issue because there was enough time for proactive innovators to gradually transform industries. Over time, their continuous innovation allowed society to create new jobs, offsetting the slow loss of certain roles and keeping unemployment low. However, from a macroeconomic perspective, we no longer have enough time to adapt to the structural shifts brought by AI.

Therefore, if we want to avoid relying on government intervention, most companies in the market must accelerate their pace of innovation to match the rate of job displacement with new job creation. Generative AI is rapidly entering commercial and social systems, but this also presents an opportunity for businesses to speed up innovation. If enough companies proactively innovate, we won’t need to worry about AI-induced unemployment.

Of course, enterprises neither would nor should venture into the AI field solely to address macroeconomic-level issues. Fortunately, they have ample commercial incentives to adopt AI. If they can seize the AI wave and create new opportunities, businesses will have a greater chance of achieving long-term development.

Some companies have already actively taken up the banner of driving AI innovation. As a pioneer in reusable rockets and electric vehicles, Elon Musk has pledged to make Twitter an AI leader like Microsoft and Google. However, Musk is notoriously unpredictable, and Twitter's internal stance on this commitment remains unclear. So, what does it mean for a company to invest in artificial intelligence?

To answer this question, we must first examine the factors that enable companies to adeptly navigate various changes. Tabrizi, a member of our research team, assembled a group to study 26 large corporations with strong financial performance between 2006 and 2022. Based on comparable data and case studies, the team categorized these companies into high, medium, and low groups across two dimensions: agility and innovation capability.

What factors differentiate agile, innovative enterprises from those that are mediocre and conservative? The team pinpointed eight key drivers of agile innovation: purpose of existence, obsession with customer needs, positive psychological cues for colleagues, maintaining a startup mentality despite company growth, pioneering spirit, high collaboration, ability to control pace, and dual-track operations. While most leaders praise these qualities, it proves extremely challenging for large corporations to sustain them long-term.

Tabrizi once authored an article analyzing how Microsoft became an industry leader by overhauling its hierarchical system and forming partnerships with companies like OpenAI. Influenced by these drivers, other enterprises have made similar improvements in the AI domain. This article focuses on two main drivers—pioneering spirit and startup mentality. Leveraging these drivers effectively can propel companies further in agile innovation as they catalyze organizational transformation.

Recently, any company investing in AI stands to gain. Given AI's demonstrable cost-cutting benefits, financial reports may appear favorable. However, mere investment yields only singular profit growth. Firms fixated on cost fluctuations risk missing opportunities to create substantial value. Harnessing AI more effectively helps build stronger industry moats. Long-term, cautious investments cannot shield companies from competition nor macroeconomic challenges they currently face.

"Cautiously implementing new technologies, yet still performing mediocrely" might be a common challenge for all emerging technologies. Large enterprises are inherently risk-averse, which is why they operate like well-oiled machines to control production costs within certain limits. Consequently, they often prefer to 'outsource' innovation by acquiring startups, even though this approach sometimes yields limited results. All successful businesses, especially those of considerable scale, tend to minimize risks and trial-and-error costs. However, as Brené Brown pointed out: "You can choose courage or you can choose comfort, but you cannot choose both."

For corporations, "being bold in exploration" has long become a cliché, with leaders protesting too much about it. But in the field of artificial intelligence, managers must truly 'practice what they preach,' embracing technology rather than mitigating risks.

Take Adobe as an example. Its Photoshop software has long dominated the photography and design market. When generative AI emerged, Adobe could have adopted a cautious strategy, testing the waters in small-scale applications before making broader moves—just as Kodak did with digital photography or Motorola with smartphones. However, Adobe chose a strategy diametrically opposed to these tech predecessors by rapidly integrating generative AI deeply into Photoshop. This enabled even average users to create videos that were previously impossible. Adobe could have viewed AI as a threat or disruption, especially since the company had been continuously optimizing Photoshop before AI technology was invented. Yet, when faced with new technology, Adobe's management demonstrated the courage to actively invest in AI, further empowering its products.

At a technical level, chip manufacturer NVIDIA has become a household name for providing the best AI semiconductor chips. To outsiders, the company may seem to have simply been lucky, possessing the right technology at the right time. However, NVIDIA's current achievements are no accident. Over the past decade, the company has actively acquired innovative firms to develop specialized capabilities in AI. Through acquisitions and in-house R&D, NVIDIA has expanded into a wide range of businesses, including custom chip development and software. We anticipate that NVIDIA will continue its aggressive innovation strategy, which not only delivers higher-value products but also better leverages AI rather than merely cutting costs.

Not every bold innovation succeeds. But to overcome the deep-rooted risk aversion of corporate management, a pioneering mindset is essential.

To succeed in AI, maintaining an entrepreneurial spirit is as crucial as bold innovation, regardless of a company's size or tenure. Start-ups excel at spotting opportunities across the market and responding quickly to current customer needs. Large companies, while resource-rich enough to see these opportunities, often respond more slowly due to bureaucratic hurdles and a lack of decisiveness—whereas start-ups can swiftly penetrate the market under the same conditions. OpenAI, with ChatGPT, outperformed Google by combining two seemingly incompatible advantages: the agility of a start-up, unburdened by Google's hesitation, and the ample resources provided by Microsoft and other investors.

The so-called entrepreneurial mindset is not just about the courage and flexibility of a business, but also includes a strong desire to achieve great things, akin to a heroic journey facing enormous challenges. The mission of a startup should be to create something extraordinary, rather than mass-producing predictable quality products, even if the company could easily aim for the latter. Therefore, startups actively seek opportunities everywhere and adopt flexible collaboration methods. To achieve their goals, they discard existing organizational structures and biases, no matter how long-standing and revered these conventions may be.

Companies cannot adopt all the above driving factors overnight, but what they can do is start working in this direction, earnestly exploring new possibilities. For those seeking career goals and wanting to make a mark, most of the above driving factors can also function at a personal level. They can boldly experiment, maintain an entrepreneurial mindset in the workplace, and meet other necessary conditions. Like companies, employees can actively engage in the AI industry by acquiring the necessary skills and experience, which not only protects their careers but also empowers them at a higher level.

Most of the energy in business operations is focused on how to produce reliable products at lower costs. To prevent large-scale unemployment, what we need now is for more companies to break this routine and accelerate the advancement of AI. The greatest danger at present is that most companies will pursue stability, making only single investments to meet short-term benefits.