AI Rise, Economic Loom?
Updated: Sep 25
The October 9, 2023 edition of Times magazine showcases its Times 100 AI leaders on the cover. Notables include Elon Musk of xAI, Sam Altman of OpenAI, Andrew Hopkins of Exscientia, and several others from burgeoning AI enterprises. While not definitive — indeed, alternative lists circulate — the spotlight on AI luminaries by such a renowned publication speaks volumes. The high-profile coverage hints at the mainstream acceptance and recognition of AI's transformative role in modern society. Musk's extensive interview further illuminates the intricate balance of vision and apprehension shared amongst these pioneers. This attention is timely, aligning with President Biden's recent announcement in Vietnam, pledging support for AI, cloud, and IT supply chains under the IPEF initiative. Subsequent discussions will delve into whether we are on the brink of an economic depression or, more aptly, an AI-induced economic metamorphosis.
The Long Game has Begun?
Per the FACT SHEET regarding the U.S.-Vietnam Comprehensive Strategic Partnership, Microsoft and Trusting Social are poised to unveil a generative AI-centric solution crafted specifically for Vietnam and other emergent markets. NVIDIA, meanwhile, seeks collaborations with FPT, Viettel, and VinGroup, focusing on infusing AI within the realms of cloud, automotive, and healthcare. Meta Platforms and the Vietnam National Innovation Center are launching the Vietnam Innovation Challenge, a bid to usher small and medium enterprises into the digital era.
The Financial Times sheds light on the recent Vietnam-US Innovation and Investment Summit, with prominent figures at the helm – President Biden, Secretary of State Antony Blinken, Vietnam’s Prime Minister Pham Minh Chinh, and Investment Minister Nguyen Chi Dung. This summit drew the luminaries of tech and aviation: Google, Intel, Amkor, Marvell, GlobalFoundries, and Boeing.
The summit's noteworthy announcements included Vietnam Airlines' sizable $7.8bn acquisition of 50 737 Max jets from Boeing and the imminent commencement of Amkor’s $1.6bn facility near Hanoi, dedicated to chip assembly, packaging, and testing.
Such strides underscore a pivotal transformation in the U.S.-Vietnam relationship – a stark departure from the war-torn narratives of yesteryears, specifically since March 8, 1965. President Biden's stance – "I don't want to contain China" – might indeed be on point. Drawing from Nicholas J. Spykman's "rimland geostrategy," which stands in contrast to Sir Halford Mackinder's "heartland geostrategy," the objective, as we probe further, seems to be the establishment of a "chain of alliances" along the Eurasian coastline, all in the quest for peace.
Musk's AI Venture
On another note, Times magazine recently spotlighted its TIME100 AI list, an assembly that hasn’t found unanimous endorsement. Notably, Professor Pedro Domingos of the University of Washington has voiced disagreement, offering his alternative roster. Parsing Musk’s substantial feature in the magazine against more succinct pieces on other leaders provides an intriguing narrative thread.
Musk’s diversified ventures span from SpaceX, Tesla, and Neuralink to the tumultuous recent acquisition of Twitter, subsequently rebranded as X. Yet, it's worth noting that his intrinsic fascination with AI dates back to the nascent days of the industry. The interview reveals a significant intersection: Musk's introduction of DeepMind’s Demis Hassabis to Google’s Larry Page. This meeting culminated in Google’s 2014 acquisition of DeepMind. Yet, an engrossing disagreement ensued. Musk contended the potential of AI systems to sideline or even eclipse humanity, while Page viewed such an evolution with apparent equanimity. Distraught and driven, Musk partnered with Sam Altman to establish OpenAI, a nonprofit aimed at democratizing AI akin to what Linux achieved.
Yet, Musk's overarching ambition didn't stop there. His quest to embed AI within Tesla and Neuralink's framework resulted in a schism with OpenAI in 2018. His envisioned projects encompass Neuralink, an endeavor to implant microchips in human brains; Optimus, a humanoid robot; and Dojo, a supercomputer designed to mimic a human brain by harnessing countless videos. But OpenAI's triumphant launch of ChatGPT in late 2022 seemed to ruffle Musk's feathers anew. He's since embarked on the creation of X.AI. Recognizing the paramount importance of online data for AI training, notably from platforms like Twitter, Musk initiated a temporary capping on tweet viewership. This move was strategically intended to thwart giants like Google and Microsoft from aggregating vast tweet repositories to feed their burgeoning AI systems.
The Third Signal: IT Layoff
In our previous analyses, we flagged two early warning indicators: the surge in US tech investments in Vietnam and the burgeoning AI industry, exemplified by Musk's ventures as highlighted by the Times. Now, a third, more enigmatic signal has emerged: the anomalous wave of layoffs by US tech companies spanning from 2022 to this year.
According to the layoff tracker, Layoffs.fyi, the top four tech giants alone - Google (12,000), Meta (21,000), Microsoft (10,000), and Amazon (27,000) - have released a significant portion of their workforce. It's simplistic to link these layoffs to underwhelming financial performance, especially as these behemoths remain formidable forces in their respective sectors. It would be similarly naive to infer that these decisions are purely aimed at propping up share prices; the evidence doesn't quite align.
Invoking the specter of Covid-19 as the primary instigator is equally puzzling. The data – 1,061 tech companies witnessing 164,769 layoffs lat year, and 1,009 tech companies with 234,976 departures this year – doesn't offer a clear causation.
There's a whisper in some corners that Microsoft, ever the trailblazer, pioneered this approach to layoff strategy, contemplating a long-term "Business Re-Engineering and Transformation Scheme." This, on the surface, may hold some water. But the larger question lingers: Why have other tech titans mirrored this move, precipitating a veritable domino effect of job cuts running into the hundreds of thousands over the past two years? The industry waits with bated breath for a clearer understanding.
A Hype of Gartner's Hype
Gartner's Hype Cycle suggests that flagship AI technologies, such as Generative AI, currently crest the hype wave. This may foretell a wane in AI enthusiasm in the coming years, paving the way, as Gartner interprets, for a robust foundational period followed by genuine industry maturity – but less hype. However, our lens differs from that of Gartner.
Of course, Gartner doesn’t pull its conclusions from thin air. Its findings lean on frameworks, including what might be identified under the umbrella of "qualitative research methodology", particularly the Delphi method. But the academic pendulum is swinging. While "qualitative research" is grounded in the interpretivist and subjective approach, contrasting the logical positivist's backing of the "quantitative view", there's a surge in popularity for "mixed method research", particularly in medicine and health science fields, anchored in "pragmatism philosophy".
Just as the spinning jenny, water frame, and the power loom marked a momentous shift in Britain's textile industry, amplifying production capacities beyond the wildest imaginations of individual craftsmen, so too does the burgeoning power of AI herald an epoch of transformative change for our modern world. The textile revolution witnessed a profound migration from the domestic sphere, where production was undertaken by individual craftsmen or families, usually within the sanctity of their homes, to the factory system, wherein workers congregated in a centralized location to harness the might of large machinery. As we stand on the precipice of the AI revolution, it's essential to recognize and navigate the parallels. The tremors of change we feel today echo the seismic shifts of yesteryears, and it's upon us to ensure that as we steer through this AI-driven evolution, we remain grounded in the lessons of history, championing values of inclusivity, equity, and the broader well-being of society.
In our view, the relentless advances in AI research – evidenced by the incessant stream of publications on platforms like arXiv – counter any notion of waning interest. The almost superhuman pace and quality of "reinforcement" research in the AI field suggests that those in the discipline are leveraging AI not just to assist research, but in some cases, to conduct it. It's a nuanced debate: employing AI to aid research is one thing, but letting AI do all the work and presenting it as human endeavour treads on ethical thin ice. The former could be the bedrock of the "singularity" concept: the inevitable emergence of superintelligence, driven purely by computational prowess.
The Phantom Logic: The Scenario
It's becoming commonplace for professionals in creative fields, from writers to graphic artists, to lean on generative AI. The technology not only reduces their workload but, notably, maintains their earnings. This is especially pronounced in the outsourcing sector and the gig economy: more tasks are completed in less time with the aid of AI. On a per-task basis, compensation might be lower, but the aggregate income has potential to rise.
To many business leaders and executives, all seems well on the surface. Monthly payrolls are met; operations proceed as usual. Yet, beneath the surface, a quiet transformation is underway. No one can truly distinguish whether the work produced was done by a human or AI. Logic dictates that IT executives would be the first to harness this shift. After all, if comparable outputs can be achieved with fewer hands on deck, layoffs would naturally follow as a strategic move. This is consistent with reports suggesting that while layoffs are widespread, the recruitment and headhunting for AI specialists among tech giants is increasingly aggressive.
Drawing parallels to Eric Hobsbawm's concept of the dual revolution post the British industrial age, one has to ponder: what unfolds when the broader spectrum of business leaders and executives recognize and act upon this trend?
While the GDP growth rate might appear stable, the profound shifts in the labor market and industrial landscape could trigger ripple effects in various sectors, impacting daily life, financial markets, and the broader sentiment in society.
Scenario: 2.5 years post "AI Revolution Day 0"
Stagnation and Transition: Despite a stable GDP, many traditional sectors are in decline, replaced by burgeoning AI-driven industries. This shift has led to a vast "skills chasm," with workers from declining industries struggling to retrain and find employment in the emerging sectors.
Small Businesses and Local Economies: The traditional small business sector, especially those not agile enough to adopt AI quickly, faces challenges. Many local stores, unable to compete with AI-driven logistics and supply chains of larger corporations, find it tough to stay afloat.
Public Finances: Governments in developed economies face decreasing tax revenues due to the high unemployment rates, despite the GDP being stable. This puts strain on social welfare systems, which are already overburdened by the increasing number of unemployed individuals seeking benefits.
Education and Training: There's an enormous push towards "AI literacy." Universities, vocational training centers, and online platforms experience a boom, as both the young and the old seek to equip themselves with skills relevant in the AI era.
Consumer Behavior: With a sizable chunk of the population facing economic insecurity due to unemployment, there's a discernible shift towards frugality. People prioritize essential services and goods over luxury items.
City Dynamics: As AI-driven automation takes over several sectors, there's reduced human activity in previously bustling commercial areas. However, educational and retraining centers are lively, with people of all ages attending courses.
Mental Health: The uncertainty of the job market takes a toll on the mental well-being of the populace. There's a noticeable uptick in reports of anxiety, depression, and other mental health disorders. The strain on families due to financial challenges leads to societal issues.
Stock Market and Investment Climate:
Tech Domination: Tech stocks, especially those linked to AI and its applications, continue their upward trajectory. Investors flock towards any company promising the next big AI breakthrough.
Decline of Traditional Sectors: Stocks in sectors that haven't integrated AI, or where AI means a reduced workforce, experience declines. This includes sectors like traditional retail, basic service industries, and some parts of manufacturing.
Risk-averse Investment: With the uncertainty surrounding the complete implications of the AI revolution, many investors become risk-averse. There's a noticeable shift towards assets deemed "safe," like gold or government bonds.
Innovative Startups: While established companies grapple with transitioning to AI-centric operations, a new wave of startups, unburdened by legacy systems and built around AI from the ground up, attracts significant venture capital.
Global Inequities: Developing economies, slower to adopt AI, don't experience the same immediate levels of disruption. This makes them temporarily attractive to investors seeking more predictable, if slower, growth. However, concerns about long-term viability in a world dominated by AI tempers this enthusiasm.
In this landscape, it's evident that while traditional economic indicators like GDP might remain stable, the profound societal shifts and uncertainties could lead to challenges that are equally, if not more, pressing.
The potential societal upheavals stemming from significant unemployment and the rapid transformation of industries could challenge governments around the world. The rapidity of change brought by the AI revolution could exacerbate tensions, especially if there's a perception that the government hasn't done enough to address the upheaval. Here's what governments might experience and how they could respond:
Widespread Protests: As seen historically, high unemployment, especially if it impacts a broad demographic of society, can lead to protests. If citizens feel the transition to an AI-driven economy is benefiting only a select few, this can exacerbate social divides.
Populist Movements: Rapid technological change and the associated economic impacts could give rise to populist movements. These movements might demand a return to "the way things were" or push for radical solutions.
Strain on Welfare Systems: As unemployment rises, so too will the number of people relying on welfare, unemployment benefits, and other social safety nets, straining public finances.
Mental Health Crisis: The uncertainty and anxiety stemming from economic disruptions could lead to a spike in mental health issues, putting additional pressure on public health systems.
Universal Basic Income (UBI): As a direct response to unemployment, some governments might experiment with or implement a form of UBI, providing citizens with a basic income irrespective of their employment status.
Retraining and Education Initiatives: Governments could invest heavily in reskilling their workforce. This would involve collaborations with universities, online platforms, and vocational training centers to ensure citizens are equipped for jobs in an AI-driven world.
Taxation Reforms: Governments could look at progressive taxation on companies benefiting the most from AI, ensuring they contribute a fair share to the societal adjustments required by the rapid transformation.
Mental Health Support: Recognizing the mental toll of such rapid changes, governments could bolster public health campaigns and services focused on mental well-being.
Promotion of AI Ethics: To ensure that the AI revolution benefits the broader society and not just a select elite, governments could promote and enforce ethical AI standards.
Regional Development Initiatives: To counteract the decline of specific regions heavily affected by industry changes, governments could incentivize businesses to set up in these areas or promote new industries.
Strengthening Social Safety Nets: Governments might bolster existing welfare systems or introduce new ones to protect those most vulnerable during the transition.
Public Awareness Campaigns: Proactively educating the public about the changes, the government's response, and available resources can mitigate some of the unrest and uncertainty.
International Collaboration: As this will be a global phenomenon, nations can collaborate on best practices, share solutions, and jointly tackle challenges posed by the AI revolution.
The nature of media today – with its emphasis on rapid news cycles, virality, and soundbites – can sometimes amplify or oversimplify complex issues. Given the dramatic shifts that the "AI revolution" could usher in, here's how the situation might unfold in the public discourse:
Doomsday Predictions: As with any significant change, there will be a segment of media and thought leaders who will predict the worst. This might include narratives like "the end of jobs," "the fall of capitalism," or "an AI dystopia." Such dramatic claims can capture public imagination and drive clicks and views, even if they don't capture the nuance of the situation.
Polarization: As seen with other global issues, responses to the AI-driven changes might polarize opinions. One side might champion the technological advancements, emphasizing the potential for increased efficiency, new industries, and a utopian future. The other side might focus on the immediate job losses, the perceived inhumanity of AI, and the threats to traditional values and ways of living.
Blame Game: There's likely to be a search for scapegoats. Corporations profiting from AI advancements might be criticized for not doing enough to mitigate job losses. Governments might be blamed for either not regulating enough or for stifling innovation with too much regulation.
Nostalgia and the Glorification of the Past: Similar to populist movements seen around the globe, there might be a wave of nostalgia for the "good old days." This could lead to calls for a return to older methods of working, even if they're less efficient.
Grassroots Movements: On the flip side, grassroots movements might arise, championing AI ethics, sustainable development in the age of AI, or pushing for more democratic control over AI advancements.
Misinformation: As with any major change, there's the potential for misinformation to spread. This could range from incorrect assumptions about AI capabilities to conspiracy theories.
Human Stories: Media will highlight individual stories that capture the broader trends. These stories can be powerful vehicles for empathy, allowing the general populace to understand the tangible effects on individuals and communities.
Calls for Solutions: As the reality sets in and the initial shock wanes, there will be increased emphasis on solutions. This could lead to more nuanced discussions around policies like UBI, retraining initiatives, and new industry development.