Artificial Intelligence to Proliferate amid Cloudy Outlooks in 2022

In June 2022, Tesla laid off nearly 200 employees from its Autopilot solution, most of them focusing on data annotation. A month before, DataRobot, one of the most successful Artificial Intelligence (AI) development platform startups, laid off 7% of its workforce and is now embroiled in internal conflict. News of these events sent shockwaves across the AI industry, raising questions on the commercial viability of AI businesses.

Tesla and DataRobot are not the only AI companies facing this issue. AI companies of all sizes, ranging from speech and audio specialist Verbit to autonomous driving startups like Argo AI, have been shedding headcounts. At the same time, cloud AI giants like  Alibaba, Google, Meta, and Tencent are also slowing down their hiring. Although these slowdowns are not specific to the AI role, they reveal a change in market sentiment.

Most of these companies cited economic headwinds, but 2022 has witnessed a lot of unfavorable market conditions. They can be generally segmented into three major segments:

• Macroeconomic Factors: As governments were racing to keep the economies afloat, historically low interest rates, combined with goods shortages and the supply chain crisis caused by COVID-19, led to inflation in the post-pandemic world. In response, central banks raised interest rates to prevent overheating. As a result, businesses have become wary of borrowing due to high costs, making fewer investments in new technologies and hiring fewer employees.
• Geopolitical Factors: Techno-nationalism has recently been on the rise, exacerbated by the Sino-U.S. trade dispute and the Russo-Ukrainian War. The United States has restricted the expansion of Chinese AI vendors into the U.S. market. In retaliation, the Chinese government imposed the same restriction on using foreign technologies in their Information Technology (IT) systems. At the same time, the rest of the world is actively grooming its domestic AI vendors and creating more stringent data privacy and ethical regulations for foreign suppliers to comply with.
• Pandemic Effects: COVID-19 is ongoing and the industry is still recovering from the impacts of the chipset shortage. New waves of the global pandemic led by new variants continue to disrupt daily life. Determined to eliminate the virus, China imposed a severe lockdown on vital economic hubs, including mask and movement restrictions. The decline in enterprise production and operation has severely affected the performance of major Chinese AI vendors.

Given all the headwinds, cloud AI giants are taking the opportunity to optimize their product portfolios and trim off non-performing business units, while smaller AI solution providers are trying to conserve valuable internal resources. In addition, according to ABI Research’s Artificial Intelligence Investment Monitor 2021 (PT-2489), the venture capital market is likely to cool down in 2022 due to the exit of several successful AI startups and more prudent investment strategies by the investor community.

That said, AI has never been more important. The technology has been adopted across various industries, including supply chain optimization, COVID-19 response, warehouse and manufacturing automation, and healthcare support. The headwinds mentioned earlier will have a limited impact on the overall growth of the AI market, as the AI industry will continue to experience the following tailwinds:

• Significant Commitment to Building National AI Chipset Supply Chain: As AI computation relies heavily on the capabilities of strong processors, chipset manufacturing capability is essential in ensuring technological leadership in AI. As a result, major markets are focusing more on AI chipset design and development than ever before. China has been aggressively developing its domestic chipset capabilities. At the same time, the U.S. Senate and European Commission have passed major legislation to provide additional funding and tax breaks to chipset manufacturers. The goal is to alleviate the concentration of chipset fabrication capabilities in East Asia and encourage the reshoring of chipset fabrication.
• Distributed Computing Architecture on the Rise: Traditionally, AI was implemented in cloud data centers or end devices. Edge computing servers have the computing power to support AI inference workloads that are otherwise too overwhelming for end devices, while also serving as optimal AI training infrastructure for smaller AI models. AI developers and implementers can now deploy AI across the edge-to-cloud computing continuum, from hyperscale data centers to regional data centers, on-premises servers, 5G edge computing servers, gateways, and end devices.
• Advancements in AI Models: New deep Machine Learning (ML) models are being introduced every month, leading to rapid improvement in efficiency, accuracy, and the scope of applications. Enterprises are eager to take advantage of these new models to differentiate their products and services (ambient sensing and speech activation), optimize their operations (supply chain optimization, asset tracking, and fleet management), or augment their productivity through digital transformation (robotics process automation, fraud detection, risk assessment, and predictive maintenance).

Despite all the headwinds, AI is still vital in addressing existing and future challenges. ABI Research believes that advancements in computing capabilities, data center architecture, model efficiency and accuracy, maturity of AI framework, and AI design, development, and operation tools will lead to ever-wider adoption of AI across multiple facets of human society.