Source: Reuters

Taiwanese major chip supplier, TSMC forecasted an annual revenue growth of 10% in the global semiconductor industry, excluding memory chips.

“This is a new golden age of opportunity with AI,” said senior vice-president Cliff Hou, who was speaking at an event in Hsinchu, where the company is headquartered.

In April, TSMC lowered its outlook for the global semiconductor industry excluding memory to a growth rate of around 10% from a previous forecast of more than 10%. World Semiconductor Trade Statistics has forecast growth of 13.1% for the global semiconductor market in 2024.

TSMC has estimated second-quarter sales may rise as much as 30% as it rides a wave of demand for semiconductors used in artificial intelligence applications (AI). Nvidia forecasted its quarterly revenue above estimates and announced a stock split, lifting shares to a record-high territory and impressing investors who have tripled the chipmaker’s market value in the past year on AI optimism.

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The world leader in semiconductor has a recorded 60% market share by Counterpoint Research. Its close rival Samsung trails behind by 13%. TSMC is known for serving Apple, Nvidia and Qualcomm for iphones, mobile chipsets respectively.

It is also the main supplier for Artificial Intelligence (AI) chips for Nvidia, and Advanced Micro Devices’ (AMD) processors. It even has clients in the Electric Vehicle (EV) sector, working with Tesla and Toyota.

The recent quake reportedly disrupted TSMC’s production as personnel had to evacuate and paused operations. Wall Street Journal quoted the company’s admitting the tremor did cause some machinery to be out of commission, and extreme ultraviolet lithography tools by ASML were unscathed. It added construction work resumed following inspection as at 4 April 2024.

Korea May Take the Spotlight

Bum Ki Son and Brian Tan, analysts at British investment bank Barclays, said in a report:

“We believe this could lead to supply disruptions in the tech supply chain. While we note some companies have reported limited damage and many of the semiconductor fabs should have been designed to withstand strong earthquakes, halts in some operations at high-tech semiconductor fabs could mean disruptions.”

“Some of the high-end chips need 24/7 seamless operations in a vacuum state for a few weeks. Operation halts in Taiwan’s northern industrial areas could mean some high-end chips in production may be spoiled,” they added.

Kim Dae-jong, Professor of Business Administration at Sejong University, cautioned that global chipmakers need to diversify the semiconductor supply chain concentrated in Taiwan to reduce risks. Professor Kim noted,

“Chip companies need to reduce their dependency on Taiwan and diversify their orders to other countries to lower the risk when facing a special crisis like this earthquake. Chip design companies should diversify their non-memory demand to Taiwan, Korea and the U.S. as a way to prepare for crises.”

The professor added the earthquake has also hiked the prices of memory semiconductors, indicating Samsung and SK hynix have reportedly halted memory chip price negotiations with clients because the supply of memory chips will decrease, leading to higher prices.

Another Group With Mixed Opinions

KB Securities advocated the importance of diversifying supply vendors. Kim Dong-won, KB’s analyst said,

“The production disruption in foundries caused by this earthquake is poised to serve as a pivotal moment, shedding light on the industry’s structure. With 69% of global foundry production concentrated in Taiwan, it underscores the risk associated with relying on a single supply chain.

TSMC’s Fab12 plant, where all production personnel evacuated following the earthquake, is encountering operational uncertainty due to damaged water pipes and system errors in certain front-end equipment. As a result, further inspections are necessary to assess the situation. The Korean semiconductor ecosystem is emerging as the optimal alternative for diversifying the memory and foundry supply chain, with long-term benefits expected.”

Lee Jong-hwan, Professor from Department of System Semiconductor Engineering at Sangmyung University commented is too quick to assume TSMC would suffer damage from Taiwan earthquake’s aftermaths. Professor Lee noted, the tremor is not likely to have a substantial impact on the foundry industry’s market share. This is because the foundry industry has long-term contracts with chip design companies, and it is difficult to suddenly change the order and design of the chips to be produced.

“The foundry business needs to supply products tailored to the design requirements of chip design companies. Stable supply is key in maintaining long-term cooperative relationships with customers, so it is difficult for TSMC to lose its customers just because of the earthquake damage,” said Professor Lee.

Nonetheless, he projected the memory semiconductor industry, Samsung and SK could benefit from the earthquake as the industry has a different structure. 

“The memory semiconductor industry may be affected by the earthquake. As production facilities of Micron Technology and Nanya Technology were reported to have suffered damage, rival memory semiconductor companies, Samsung Electronics and SK hynix, are likely to benefit from the market where chip production and supply are more directly connected,” Professor Lee added.

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The world raved about AI last year, even Jensen Huang from Nvidia famously praised the power of Artificial Intelligence (AI). He went as far as to say anyone can be a programmer with AI softwares. This was during the period where ChatGPT got into trouble in various shapes and forms— from data security breach to fabricated content by a high ranking editor.

Now, the chip titan finds itself joining the group of defendants accused of copyright infringements via OpenAI. Three authors reportedly took Nvidia to court over using their copyrighted material without permission to train its NeMo AI platform.

Brian Keene, Abdi Nazemian and Stewart O’Nan said their works were part of a dataset of about 196,640 books that helped train NeMo to simulate ordinary written language, before being taken down in October “due to reported copyright infringement.“ In a proposed class action filed in San Francisco federal court, the authors said the takedown reflects Nvidia’s having “admitted” it trained NeMo on the dataset, and thereby infringed their copyrights.

They are seeking unspecified damages for people in the United States whose copyrighted works helped train NeMo’s so-called large language models in the last three years. Among the works covered by the lawsuit are Keene’s 2008 novel “Ghost Walk,” Nazemian’s 2019 novel “Like a Love Story,” and O’Nan’s 2007 novella “Last Night at the Lobster.”

The lawsuit drags Nvidia into a growing body of litigation by writers, as well as the New York Times, over generative AI, which creates new content based on inputs such as text, images and sounds. Nvidia touts NeMo as a fast and affordable way to adopt generative AI.

Other companies sued over the technology have included OpenAI, which created the AI platform ChatGPT, and its partner Microsoft. AI’s rise has made Nvidia a favourite of investors. The Santa Clara, California-based chipmaker’s stock price has risen almost 600% since the end of 2022, giving Nvidia a market value of nearly US$2.2 trillion.

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Vietnam has pledged tax breaks and other perks to semiconductor companies that help to develop the sector in a Southeast Asian country, according to Japan’s Nikkei Asia. The article quoted Vietnamese Minister of Science and Technology Huynh Thanh Dat as saying in an interview that the national plan for chips will include industry grants through a science fund and joint state research with private companies like FPT.

Companies from Nvidia to Samsung are looking to expand their chip businesses in Vietnam, which is slated to receive millions from the US CHIPS and Science Act and already hosts Intel’s biggest global test and assembly factory. Meanwhile, Jose Fernandez, US undersecretary of state for economic growth, energy and the environment, told Nikkei Asia that Vietnam has attracted dozens of companies in the semiconductor field, and several more US players would jump in if the country had enough renewable energy to meet their green goals.

The country needs to strike technology transfer deals with nations dominating the chip sector, Dat said, adding Vietnam will sweeten policies to bring foreign experts into the workforce, which has faced a recent slowdown in foreign work permits. Toward that end, colleges are unveiling semiconductor classes in partnership with employers like Samsung.

Vietnam aims to train 50,000 engineers for the industry by 2030. The country has a strong foundation for science and tech education, but a dearth of advanced skills has limited a broader march up the electronics supply chain.

Fernandez said in an interview while visiting Vietnam that the country is a top target for US CHIPS Act subsidies, which will be a “badge” of confidence. The dollar amount will be based on an assessment expected sometime this month.

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Source: Counterpoint Research

Besides, the overall 2023 semiconductor revenue rankings saw some big changes from 2022, like Intel reclaiming the top spot from Samsung as the latter suffered a lot from the memory sector downtrend as well as lackluster smartphone business. AI provided positive news to the semiconductor industry, emerging as a key content and revenue driver, especially in the second half of the year. NVIDIA appeared to be the largest beneficiary, followed by AMD. Both will be growing their AI-related businesses in the coming years.

2023 was a year for semiconductor companies to fine-tune their strategies/outlook and manage inventory adjustments to prepare for the impending AI boom. According to Counterpoint’s semiconductor revenue tracker, only 6 out of the top 20 global semiconductor vendors reported YoY revenue growth.

The memory sector, in particular, experienced strong headwinds and was down 43% YoY in terms of revenue in 2023. We also found that the top 20 global semiconductor vendors contributed to 71% of the market, down from 76% in 2022 and showing a 14% YoY revenue decline.

Intel reclaimed its first place in semiconductor revenue rankings in 2023, though it reported a 16% YoY decline in its revenue largely due to a double-digit YoY shipment decline in both the PC and server segments. Samsung too was massively affected by the memory market slowdown in both DRAM and NAND segments, reporting a 38% YoY decline in its revenue.

The memory market was mainly hit by soft demand in the PC, server and smartphone segments as well as oversupply and excess inventory across the market. SK hynix and Micron, two other major players in the memory market, also reported huge declines in their revenues at 33% and 36% YoY, respectively.

NVIDIA was in the spotlight in 2023 thanks to the acceleration of AI deployments. We believe the company will continue to lead the semiconductor industry’s growth because of its high market share of general-purpose GPUs used in AI/high-performance computing. NVIDIA saw an 86% YoY revenue growth in 2023 to rank third in terms of revenue, its first-ever top-five position.

Commenting on the market dynamics, Senior Analyst William Li said, “In general, we believe artificial intelligence (AI server, AI PC, AI smartphone, etc.) will continue to be a major organic growth driver in the semiconductor industry in 2024, followed by the memory sector’s rebound due to normalizing oversupply situation and demand recovery. The automotive sector could be another driver for the market due to content growth, which was already a key revenue driver for Infineon and STMicroelectronics in 2023.”

Since the industry is at the end of the inventory correction cycle and the support from clients’ demand is relatively solid, supply constraints will likely be the key variants to keep an eye on. During its latest quarterly earnings call, the world’s largest foundry player, TSMC, maintained its solid capacity expansion plan for 2024. Counterpoint Research holds an optimistic view on its utilization rate in the coming quarters.

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TSMC’s new plant brings a total investment in its Japan venture to more than US$20 billion with the support of the Tokyo government. The Taiwanese chip titan announced plans in 2021 to build a US$7 billion chip plant in Kumamoto in southern Japan’s Kyushu.

The company said the first Japanese factory would open in February 2024 with volume production in the Q4 2024. TSMC was also exploring building a second factory in the country.

In a statement, TSMC said its majority-owned unit Japan Advanced Semiconductor Manufacturing in Kumamoto would build a second fabrication plant, or fab, in response to rising customer demand. TSMC added the second fab will begin construction by the end 2024. With both factories, the site is expected to have total monthly capacity of more than 100,000 12-inch wafers to be used for automotive, industrial, consumer and high performance computing-related applications.

The capacity plan may be further adjusted based upon customer demand, it noted. TSMC is a major supplier to companies including Apple and Nvidia, holds an 86.5% stake in the Japanese venture, with Sony Group 6%, auto parts maker Denso 5.5% and carmaker Toyota with 2%.

TSMC’s expansion in Kyushu is central to the Japanese government’s efforts to rebuild the country’s position as a leading chip manufacturing centre and ensure the stable supply of chips amid trade tensions between the United States and China. The decision to build a second fab is a vote of confidence by TSMC in Japan where construction of the first fab has run smoothly and which, it sees as a source of diligent workers with a government that is easy to deal with.

Japan’s chip-making sector, the world’s biggest in the 1980s, struggled to maintain its competitive edge, going into a steady decline in the past three decades, while rivals such as Taiwanese manufacturers gained ground. While both TSMC and the Taiwan government said the majority of the company’s most advanced manufacturing would continue to take place in Taiwan, TSMC has been expanding its global manufacturing footprint in response to what it says is customer demand.

TSMC’s flagship overseas investment is a US$40 billion project to build two fabs in Arizona, supporting Washington’s plans to boost U.S. chipmaking capacity. TSMC is also planning its first European factory, in Germany, which will mainly serve the auto industry.

TSMC’s Taipei-listed stock has risen 8.9% so far this year on the back of a boom in demand for chips for artificial intelligence applications, outperforming a 0.9% gain for the broader index.

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DIGITimes reported scientists from the Institute of Computing Technology under Chinese Academy of Sciences have recently published a research paper showcasing capability to use AI in automating chip design. Titled “Pushing the Limits of Machine Design: Automated CPU Design with AI”, the research claims that a 32-bit RISC-V CPU design was automatically generated within 5 hours, and that the CPU, with a clock rate up to 300MHz, already entered tape-out in December 2021 on 65nm process.

In addition, the research claimed that the CPU was tested successfully on the Linux operating system and performed “comparably against the human-designed Intel 80486SX CPU.”

This came shortly after Nvidia’s CEO Jensen Huang declared his take about the power of A.I. — qualifying anyone as a programmer. No longer will programmers need to write lines of code, only for it to display the dreaded “fail to compile” because of a missing semicolon. 

How will this be done? By generative artificial intelligence, Huang said during his keynote speech at the Computex forum in Taiwan on 29 May 2023.

Genesis Remains From Engineers

Though modern commercial electronic design automation (EDA) tools such as logic synthesis or high-level synthesis tools are available to accelerate the CPU design, all these tools require hand-crafted formal program code as the input, the research paper explained. In other words, engineers must use formal programming languages to implement the circuit logic of a CPU based on design specification, before various EDA tools can be used to facilitate functional validation and performance/power optimization of the circuit logic.

The highly complex process typically iterates for months or years, “where the key bottleneck is the manual implementation of the input circuit logic in terms of formal program code,” indicated the paper.

What the research group aimed for was automating the CPU design without human programming. It was done by using partial input output examples to replace human-written programs as the inputs, as such examples were directly accessible from a large number of legacy test cases.

Therefore, according to the research, “The problem of automated CPU design can be formulated as generating the circuit logic in the form of a Boolean function satisfying the input-output specification.” Using the approach, the report claimed to successfully generate “a large-scale Boolean function with almost 100% validation accuracy (e.g., > 99.99999999999% as Intel) from only external input-output examples”.

Notably, the paper claimed that the adopted approach could discover not only the general von Neumann architecture, but also fine-grain architecture optimisation from scratch. Despite the claimed success, this only marks the first step.

If the design complexity was increased, with a more complex CPU and some other IP blocks, tougher performance targets have to be met to evaluate how good the AI flow is, according to Woz Ahmed, Managing Director of Chilli Ventures and former Chief Strategy Officer of Imagination Technologies.

“Setting aggressive design constraints with a new process and integrating complex IP is the real test, to see if automation will match experienced engineers in power, performance and area (PPA), testability and manufacturability,” Ahmed observed. “Given the US chokehold on EDA tools, China’s best hope in indigenous EDA is to leverage its broad AI capabilities,” Ahmed further noted, “they need to be getting hold of lots of relevant training data.”

The Real Battle

This research appears to be a direct attempt to discredit Nvidia CEO’s take on technology’s ability match human intelligence. This is in spite of Nvidia’s recent unveiling of their super computers.

Such is debatable given the recent prominent blunders caused by people using A.I. chatbot for programming. The value add of chatbots remains unquestionable as at today.

Despite the headline of its ban in many nations, the advanced countries recognise the advantages ChatGPT has to offer albeit the plethora of data security issues. Many organisations have resorted to develop their inhouse A.I. chatbot to prevent confidential data leak.

In fact, it may take competition a notch higher. Hence, if coding takes centrestage, the real battle is between the engineers’ coding prowess, instead of the program.

While the technology itself is impressive, it is the skill and expertise of the engineers that ultimately determine the success and capabilities of the chatbot. These engineers are tasked with training, fine-tuning, and optimising the model, ensuring that it can generate accurate and coherent responses to a wide range of queries.

They constantly strive to improve the system, addressing its limitations, enhancing its understanding, and refining its ability to engage in meaningful conversations. It is their collective knowledge, experience, and ingenuity that fuels the competition, as they push the boundaries of what ChatGPT can achieve, shaping the future of conversational AI.

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CNBC quoted Nvidia CEO Huang that everyone can be a programmer soon enough given the world is entering a new computing era. He said anyone can be a programmer, just by speaking to the computer, and the desired functions will come forth.

No longer will programmers need to write lines of code, only for it to display the dreaded “fail to compile” because of a missing semicolon. How will this be done? By generative artificial intelligence, Huang said during his keynote speech at the Computex forum in Taiwan on 29 May 2023.

At his first public keynote since the pandemic, Huang introduced a new AI supercomputer platform called DGX GH200, aimed at building generative AI models. Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data.

The new class of large-memory AI supercomputer — an NVIDIA DGX supercomputer powered by NVIDIA® GH200 Grace Hopper Superchips and the NVIDIA NVLink® Switch System — created to enable the development of giant, next-generation models for generative AI language applications, recommender systems and data analytics workloads.

The NVIDIA DGX GH200’s massive shared memory space uses NVLink interconnect technology with the NVLink Switch System to combine 256 GH200 superchips, allowing them to perform as a single GPU. This provides 1 exaflop of performance and 144 terabytes of shared memory — nearly 500x more memory than the previous generation NVIDIA DGX A100, which was introduced in 2020.

“Generative AI, large language models and recommender systems are the digital engines of the modern economy,” said Jensen Huang, founder and CEO of NVIDIA. “DGX GH200 AI supercomputers integrate NVIDIA’s most advanced accelerated computing and networking technologies to expand the frontier of AI.”

“This computer doesn’t care how you program it, it will try to understand what you mean, because it has this incredible large language model capability. And so the programming barrier is incredibly low. We have closed the digital divide. Everyone is a programmer. Now, you just have to say something to the computer.” Huang pointed out.

Nvidia says that generative AI is the “most important computing platform of our generation” as individuals and companies move to create new apps and leverage on generative AI in the process. Creative professionals will be able to create images with a simple text prompt, while programmers can accelerate application development and debugging efforts, the company said. Even architects can generate 3D models from 2D floor plans.

“Every single computing era, you could do different things that weren’t possible before,” Huang said, adding that “artificial intelligence certainly qualifies.” He explained that this computing era is “special in several ways”. The CEO said generative AI is able to understand information other than just text and numbers. It “can now understand multimodality, which is the reason why this computing revolution can impact every industry,” he added.

Unlike traditional models of computer application development, which require new applications and new hardware to accommodate advances like A.I., Huang said generative A.I. will not only be able to impact new applications, but enhance old ones. 

“Every application that exists will be better because of A.I. It can succeed with old applications. And it’s going to have new applications. The rate of progress, because it’s so easy to use, is the reason why it’s growing so fast.”

While writings were on the wall that AI is a massive disruption, Nvidia has appeared to have taken a good leaf out of their competitor. While it would possibly to be rude to claim an unintended blaze lit the way for another, such is apt for Nvidia after the infamous Korean chip maker’s blunder for relying too much on AI that data security was compromised.

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Bloomberg reported Nvidia Corporation, the world’s most valuable chipmaker, forecast sales that surpassed analysts estimates, showing how booming demand for artificial intelligence (AI) processors has the potential to reshape the sector and sending shares to a record high. Sales in the three months ending in July will be about US$11 billion, Nvidia revealed on 24 May 2023. That shattered an average analyst estimate of US$7.18 billion.

“We’re seeing incredible orders to retool the world’s data centers,” Chief Executive Officer and co-founder Jensen Huang told analysts on a conference call. A trillion dollars of data center infrastructure will be upgraded to handle so-called accelerated computing, he said, letting them run generative AI tools such as ChatGPT. “The budget of a data center will shift very strongly to accelerated computing.”

The outlook shows that Nvidia is benefiting even more from the AI frenzy than thought possible. Under Huang, the company has positioned itself as the top provider of components for training AI software. That helped it weather a broader slowdown in technology spending.

Nvidia had outperformed stocks in the major indexes it trades on this year, turning it into the world’s fifth-largest publicly traded company by 24 May 2023’s close. It is now on the verge of joining Apple Inc. and others in the trillion-dollar club.

By market value, the chipmaker is eight times the size of Intel Corporation, a company that doubled Nvidia’s annual revenue last year. Revenue in Q1 2023 beat estimates by the widest margin in five years. The company’s forecast for sales this period is 53% higher than analysts projected, marking a record quarterly total.

“They may be in a unique position,” Sanford C. Bernstein analyst Stacy Rasgon said on Bloomberg Television. While the company doesn’t project individual unit revenue, the overall forecast implies a 75% surge in data center revenue, he said. “Is it a one-time thing or is this the new normal? I don’t know.”

An article from Kite Rocket cited advanced computer chips that semiconductor manufacturers design and fabricate power AI. The chip industry benefits from using those AI chips and software to optimise its semiconductor manufacturing processes. In that way, AI can facilitate incredible breakthroughs across the entire semiconductor manufacturing value chain.

The piece added that chip and AI industries are at an inflection point; the same conventional chips that have been used in PCs and servers have gotten us this far. To get to the next level in AI will take leading-edge chip and packaging solutions. When these new integrated systems reach the market, everything will be set in motion for AI to truly reach its fullest potential.

The Alpha, Not The Omega

Going back to basics, an AI chip is a type of semiconductor designed for AI specific applications and different from traditional chips. Firstly, the latter is incapable of handling large amounts of data required for AI applications.

AI chips are designed to work with neural networks, which are a key component of many AI applications. Traditional semiconductors are not as well-suited for working with neural networks. 

A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.

AI chips often incorporate specialised hardware that helps speed up the training and inference process. This includes dedicated neural network processors and hardware-accelerated maths libraries.

They are often designed to be power efficient, as many AI applications are deployed on mobile devices or other devices with limited power budgets. Therefore, it is a tall order for traditional chips to keep up with the influx of new computing functions which call for substantial volume of data and algorithms.

The change resulted in a design relook for AI chip to feature multiple microprocessors and accelerators that perform the different functions needed for the AI applications. Nvidia is singled out as one of the big potential winners in AI, following the viral success of the ChatGPT chatbot and other popular tools.

The company’s chips excel at parallel processing, which makes them well suited for training software by bombarding it with data. The growth also suggests that Nvidia is getting adequate supplies from manufacturing partners such as Taiwan Semiconductor Manufacturing Company (TSMC).

Nvidia Chief Financial Officer Colette Kress said the company has secured a “substantial” increase in supply of AI-related chips from its subcontractors for the second half of the year. Nvidia’s Huang argued that the use of the technology is only in its infancy and more tailored products for specific industries are needed.

He’s built online services and software tools to help encourage the broader adoption of AI outside of his big customers — cloud providers like Microsoft Corp. and Amazon.com Inc.’s AWS. As part of its Q2 2023 forecast, Nvidia predicted an adjusted gross margin of about 70%, above the 66.9% analysts were projecting. 

The AI spending surge has boosted Nvidia’s data center unit, though a collapse in demand for personal computer components is still hammering its graphics chip business — and weighing on overall sales. Nvidia said revenue growth in its data center unit was driven by “strong demand from large consumer internet companies and cloud service providers.” Those customers are deploying its graphics chips to power generative AI and large language models, the company said.

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The world’s car industry is projected to shrink to only 10 companies over the coming decade, a Chinese rival to Elon Musk’s Tesla said, as intense competition in China’s electric vehicle market goes global. Brian Gu, Vice-Chair of Guangzhou-headquartered Xpeng, said for Chinese companies to be among the last carmakers standing, they would need to have annual sales of at least 3 million vehicles, underpinned by global exports.

Toyota sold 10.5 million cars in 2022, while Tesla sold 1.3 million. The warning comes at a historic juncture for the global car industry.

China is on the cusp of overtaking Japan as the world’s biggest exporter of cars by volume after passing Germany last year. At the same time, slowing growth and an intense price war is pushing low-cost carmakers to the brink of collapse in China, the world’s biggest car market.

“To be in that ‘3 million club’ you cannot be a China-only player, you have to be a global player. We think in that scenario, maybe close to half your volume is coming from outside of China,” Gu said in an interview with the Financial Times. “In 5 – 10 years, it’s going to be a much more concentrated market. I think the players will probably be reduced to less than 10 at the global stage.”

Xpeng was founded in 2014 and raised US$1.5billion in a 2020 initial public offering in New York, has been hit by intense competition in China. It ranked 12th by sales among electric-vehicle makers in China during the first three months of the year.

The company which sold more than 120,000 vehicles in 2022, has been hit by an almost 50% decline in Q1 2023  after Tesla cut prices. In January, Xpeng was forced to follow suit, slashing the prices of three of its four models by as much as 13%.

Gu, formerly JPMorgan’s Managing Director and Chair in Asia, struck a defensive tone over the sales slump, blaming the timing of the company’s new model launches. However, he forecast that the market would stabilise in H2 2023.

“This year, I think we’re faced with a very competitive landscape,” he said. “There’s obviously [price-cutting] pressure which not only causes competition but also creates hesitancy among consumers.” 

Gu acknowledged that deteriorating US-China relations complicated the company’s overseas expansion plans. Xpeng, which is backed by Alibaba and has invested heavily in autonomous driving, is targeting growth in Europe this year but does not have immediate plans to sell cars in the US.

Entering the US for Chinese brands “may be difficult today”, Gu said.

“We need to take time to study it and find a way to access that market.” Despite the challenges, Gu said the company saw “plenty of growth opportunities outside of China”.

Xpeng, as with all Chinese electric-car producers, depends on US chip designers including Nvidia and Qualcomm for advanced semiconductors. This has fuelled concerns that Chinese carmakers could be exposed as the US government expands restrictions on China’s access to cutting-edge US chip technology.

“So far, none of our partnerships has been impacted by any of the political noise,” he said, adding that, if the restrictions did start to have an impact on the company, “the whole China industry will find a solution”.

Domestically, Xpeng has also hit speed bumps. Last September, customers complained about the carmaker’s “confusing” models. The company was forced to rename its luxury sport utility vehicle less than 48 hours after its launch.

Shortly after the naming controversy, Xpeng began restructuring. The company recruited as co-president Wang Fengying, a former chief executive at Great Wall Motor who helped that company become the first Chinese group to export locally made cars.

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