Artificial Intelligence Will Fuel 2024 Technology Trends

Over the last year, interest in artificial intelligence has boomed. Generative artificial intelligence (AI) became one of the most disruptive technologies in 2023 as OpenAI’s ChatGPT took the world by storm. Powered by Nvidia’s powerful graphic processing units (GPUs), demand for smart solutions, particularly AI, rose exponentially. Despite the low downturn in consumer electronics and the growth in excess electronic component inventory, tech giants worldwide sought to make their own ChatGPT golden goose.  

Generative AI Continues to Dominate ‍

Nvidia, the reigning king of AI-capable solutions, saw a massive spike in orders over 2023, shooting them straight into the prestigious trillion-dollar club. As one of the few companies that can supply such powerful and flexible components, Nvidia saw challengers attempt to usurp its throne. The new competition came anywhere from local manufacturers in China to global giants like AMD.  

In June, AMD announced its latest advanced GPU for artificial intelligence called the MI300X, that has software similar to Nvidia’s CUDA, which gives AI developers the capabilities to enable the chip’s core hardware features. AMD and Nvidia GPUs have been used in recent developments within Microsoft’s Azure cloud services.

Likewise, Microsoft, spurned by ongoing innovation within the AI sector, collaborated with OpenAI to develop its custom AI-capable chips to handle its growing number of AI products. Maia's chip is designed to run large language models like ChatGPT. Currently, Microsoft is testing Maia to meet the needs of its Bing Copilot, the GitHub Copilot coding assistant, and GPT-3.5-Turbo, a large language model from Microsoft-backed OpenAI.  

Unsurprisingly, over 2023, access to popular Nvidia GPUs and other coveted AI-capable components was significantly strained. TSMC is the only firm that manufactures Nvidia’s flagship H100 chip, leaving supply limited and prices astronomically high. TSMC expects supply constraints to ease in its CoWoS packaging during the first half of 2024, but this indicates a growing problem within the industry.  

Market forecasts predict that generative AI chip sales will reach over $50 billion in 2024, with related software revenue growing to $10 billion. Generative AI will become an integral part of nearly “all enterprise software offerings in 2024,” according to Deloitte’s Technology, Media & Telecommunications 2024 Predictions.  

Deloitte predicts that generative AI will “reshape multiple industries starting in 2024…infrastructure, cloud providers, enterprise productivity software, specialized enterprise tools, and engineering and design tools.” It will continue to improve productivity and creativity and enhance workplace efficiency with accelerated analyses and more specific insights.  

Competition within the AI market will heat up through 2024 as capacity increases and further profitable applications of AI are uncovered. Even markets associated with AI are seeing opportunities for growth alongside generative AI components and applications.  

IoT Sensors Growing Thanks to EVs

Increased connectivity has risen over the last few years and only blossomed alongside AI in 2023. As corporations embraced generative AI to optimize workflows, demand for further connectivity between devices has also seen significant growth. The Internet of Things (IoT) sensor market, which allows systems to sense changing circumstances to initiate a pre-determined response, is growing alongside AI.  

The global IoT sensor market was valued at $14.84 billion in 2023 and is expected to grow at a compound annual growth rate (CAGR) of 25.3% between 2024 and 2032 to reach an estimated $113.01 billion in 2032. Continued efforts to digitalize industries with the heightened demand for smart, connected devices are some of the main trends driving this growth skyward.  

Unsurprisingly, the automotive sector has been one of the leading industry drivers that continues to fuel this trend. Consumer demand for electronic vehicles (EVs) and more autonomous cars has increased sensor use in modern vehicles. Acoustic sensors are beginning to be used in vehicles to aid the accuracy of advanced driver assistance systems (ADAS) with more environmental information.  

Furthermore, IoT applications within EVs extend beyond personalized user experience and better driver assistance. Over the last year, some of the top IoT sensor applications within EVs have been in energy and battery management, predictive maintenance, and EV charging optimization.  

The real-time data collection on various vehicle performance parameters, such as engine condition and tire pressure, can help quickly detect unusual operation patterns to alert drivers of impending failures or upcoming maintenance. This information aids automotive manufacturers and consumers by effectively reducing downtime and repair costs while delivering information on weaknesses and their improvements to design engineers.  

It is estimated that connected cars and the IoT industry will be worth a combined $42 billion in 2024 with a CAGR of 9.2%. Continued expansion of IoT applications within the EV industry to enhance the user experience will grow alongside new technological advancements from integrating AI and machine learning (ML). The predictive capabilities within AI have the potential to help further reduce downtime and maintenance regarding vehicle battery life.

Likewise, further implementation of AI in vehicles will bring the industry closer to fully automated vehicles.  

AI Will Help Industries Prioritize Sustainability

The global push toward sustainability has become a primary target of organizations across all industries. Specifically, semiconductor manufacturers have worked extensively over the past few years to continually reduce emissions from fab activities and electricity used in heating and cooling systems. These two areas, known as Scope 1 and 2, represent 65% of manufacturing greenhouse gas emissions.  

However, many emissions are still generated by materials and services used in chip manufacturing, maintenance, equipment upgrades, transportation, and deliveries, all labeled under Scope 3. Most sustainable practices focus on limiting Scope 1 and 2 emissions rather than 3. However, between 2023 and 2024, many chip suppliers aim to reach zero net emissions by 2040. That means finding ways to reduce Scope 3 emissions now.  

Decarbonization efforts can be improved through AI-infused clean, distributed energy grids, precision agriculture, sustainable supply chains, environmental monitoring and management, and enhanced weather and disaster predictions. Microsoft's commissioned research team, PwC UK, estimates that using AI for environmental applications could contribute up to $5.2 trillion to the global economy by 4.4%.

Further application of AI could help reduce worldwide greenhouse gas emissions by 4% in 2040, which is equivalent to the combined annual emissions of Australia, Canada, and Japan.  

Overall, increasing process efficiency leads to better productivity and organization output and reduces greenhouse emissions. By implementing AI in the design and manufacturing of semiconductors, chipmakers can more easily reduce waste associated with less-optimized production lines.  

In a recent research paper, Nvidia semiconductor engineers demonstrated how specialized industries can leverage large language models trained on internal data to enhance productivity. Not only can generative AI give chipmakers a competitive edge in the semiconductor industry, but it can also help optimize processes, leading to reductions in human error and time and energy waste.  

The research team at Nvidia developed a “custom LLM called ChipNeMo, trained on the company’s internal data, to generate and optimize software and assist human designers. The long-term goal is to apply generative AI to every chip design stage, leading to substantial gains in overall productivity. The initial use cases explored by the team include a chatbot, a code generator, and an analysis tool.”

From their research, Nvidia’s study shows how customizing large language models can automate time-consuming tasks such as maintaining updated bug descriptions and quickly help engineers locate technical documents. Continued use of generative AI in chip design, or any industry, will help companies meet sustainability goals and optimize organizational performance in highly competitive industries.  

Find What You Need on Sourcengine

No matter which avenue of artificial intelligence your company chooses to pursue in 2024, Sourcengine, the leading e-commerce site for electronic components, will help you meet your product goals.  

According to TSMC, supply constraints on AI chips will take 1.5 years–18 months–to begin to ease. Tech giants, including Microsoft, Google, and Amazon, continue to snap up many advanced processors available. TSMC Chairman Mark Liu says that sourcing the parts will be difficult until the end of 2024. Currently, TSMC can only meet 80% of its demand for chip-on-wafer-on-substrate (CoWoS) packaging technology, which is used in many of the advanced chips on the market, such as high-bandwidth memory (HBM). TSMC said that until it can bring additional capacity online, Nvidia’s H100 and A100 and AMD’s upcoming Instinct MI300 series accelerators will face constraints.  

Organizations must partner with a distributor specializing in sourcing hard-to-find components like Sourcengine for the upcoming strenuous year.  

Sourcengine’s e-commerce site can help users quickly locate component offers that fit their needs. For procurement professionals needing that human factor, you don’t have to look for other sites. Sourcengine’s global team of experts with decades of experience can help you source parts that meet your specifications. Additionally, Sourcengine’s bill of materials (BOM) management tool, Quotengine, can alert buyers when their wanted part becomes available at the right number and the best price.

Ready to get started on your next AI solution? Send an RFQ to Sourcengine’s experts today.