On one filling line each day, Medivant Healthcare produces 20,000 single dose injectable vials. These vials end up at patient bedsides, on hospital crash carts and in the emergency room, often for patients needing an anesthetic. 

The FDA requires that each vial be visually inspected before leaving the plant. Currently, Arizona-based Medivant uses humans to verify that there are no visible particulates or label defects that would render the vials unusable. 

But soon, the company’s vial inspection machine will take over that process. “People get tired and bored,” and can miss some defects, while the machines do not, said Medivant CEO Viraj Gandhi.

As part of the machine’s AI training process, Medivant sets standards for what the banned particles look like, to use in the algorithm training, Gandhi added that AI is seeping into many parts of the drug manufacturing process, something the FDA is monitoring. Earlier this year, the agency released a discussion paper, Artificial Intelligence in Drug Manufacturing, requesting comments from stakeholders and the public on how the technology can be best used in the sector.

Integrating AI into drug manufacturing is difficult because the industry is highly regulated, said Thomas Hartman, president and CEO of the International Society for Pharmaceutical Engineering. Any process changes, including the direct or indirect introduction of AI, can impact product quality, requiring the manufacturer to go through a comprehensive change control procedure, which is time and resource intensive.

How drug manufacturing currently uses AI

In addition to quality control, AI can help manufacturers understand how to optimize production processes. AI algorithms can help companies understand how trends and facility temperatures can impact the manufacturing process, including the impact on product quality or yield, said Sue MarchantEVP of product at MasterControl, which sells software for quality management and manufacturing execution. 

Some manufacturers use AI to focus on a single manufacturing step prior to commercialization, according to Marchant. During the R&D phase, they run through the manufacturing process changing one step at a time to determine if that improves the quality, results or efficiency. 

AI can be used to augment a company’s visibility into operations, such as an unplanned deviation on the manufacturing floor. It can also provide contextual data, like sharing other times something similar happened at the facility and what caused it. 

“We’re providing [workers] information to potentially guide their actions and assess risk,” Marchant said. “But we’re not yet prescriptively saying we believe you should take this action, so it’s just introducing AI into those processes in a very careful way.” 

The technology can be integrated on the quality side, to make it easier for manufacturers to comply with regulations and ensure their practices are safe and secure, by reviewing data and highlighting at-risk areas, Marchant noted. 

Challenges implementing AI in drug manufacturing

The biggest roadblock to implementing AI in drug manufacturing is cost, Gandhi said. Medivant spent millions of dollars on its semiautomatic vial inspection, which he says is 10 times more than the cost of human labor for the same job. 

“Everyone wants to do this, but you need enough capital,” Gandhi said. 

Medivant bought its machine four months ago and is training and validating it with sample vials for four to five months, then will test it with commercial batches. After putting it into production, the company will also perform manual observations for six to nine months, to ensure there are no false negatives in the inspections. Then it will become the manufacturer’s primary inspection method. 

Investing in automation and AI is difficult for smaller manufacturing companies like Medivant, which makes generic, low-cost drugs like epinephrine and lidocaine. The company’s products mostly sell for dollars per vial, Gandhi said. 

AI tools are, however, more widely adopted by larger manufacturers. “The big ones typically take a risk in new equipment and see how it goes,” Gandhi added. 

Another limitation to adopting AI is having the appropriate digitized data available to train the algorithms and run the analyses. This can limit the AI options to larger players, Marchant said, as many smaller manufacturers still use paper logbooks or rely primarily on on-premise data storage, rather than cloud services.

Incorporating AI may require that the “entire manufacturing process is digital, you have outlined every step in your manufacturing process, you are capturing every step digitally, you’re capturing the date and time it occurred, who participated and what the parameters of each step were,” Marchant said. Manufacturers may also need to combine and accurately integrate data from disparate sources. 

The FDA’s role

The FDA “has recognized and embraced the potential of advanced manufacturing to bring benefits to patients and consumers,” an agency spokesperson told Manufacturing Dive. 

Its Center for Drug Evaluation and Research established the Emerging Technology Program in 2014 to work with stakeholders on supporting advanced manufacturing, including tech such as AI. CDER recognized that regulatory policies and programs may need to evolve to enable timely technology adoption, according to the spokesperson.

“A lot of what we’ve seen [from the FDA] is them trying to get a handle on it,” Marchant said. “There’s not a lot of strong guidance about what companies should do.” 
The result, she said, is companies may be hesitant about using AI as they are uncertain what it will mean for them in the future.

Marchant said her customers are erring on the conservative side, asking in requests for proposals how AI is used in MasterControl software. They are concerned about whether future FDA requirements will interfere with their processes if they adopt AI tools now. 

“Everyone’s sort of in a wait and see mode,” Marchant noted. “The FDA definitely wants to encourage innovation, because their message is that they’d like to embrace innovation, and yet what does that really mean? That’s what nobody really knows yet.”

The FDA is interested in incorporating AI in drug manufacturing and highlighted for Manufacturing Dive ways it could potentially improve the process: 
•    Identify optimal process design and scale-up strategies that might reduce development time and waste  
•    Gain greater control of product and process quality while reducing human involvement and human error  
•    Better monitor manufacturing processes and detect changes in performance that trigger preventative maintenance activities and reduce process downtime  
•    Prevent more process deviations, and in the case of a deviation allow for better root cause identification of the underlying problem

In addition to requesting feedback from stakeholders and the public, in September the FDA cohosted a workshop with the Product Quality Research Institute on the regulatory framework for the use of AI in drug manufacturing. 

CDER’s goal, the spokesperson said, is to help patients realize the benefits of using AI in drug manufacturing, while minimizing risks, such as those stemming from the use of AI for unethical outcomes. 

The biggest fear, Gandhi said, is that the data can be manipulated. On the inspection side, there is already stringent FDA regulation to validate the accuracy and security of the machine and inspection results.

Dive Brief:

• Employees may be warming up to generative AI: 4 in 10 of the more than 2,500 U.S. workers surveyed by consulting firm Robert Half believe the technology will have a positive effect on their career, the firm announced Aug. 22.
• Attitudes differ by occupation, the May 2023 research found. About two-thirds, 63%, of technology professionals and more than half of HR professionals said generative AI will create more demand for their skills. Age group also plays a role, as 63% of Generation Z respondents and 57% of millennials are optimistic about AI’s benefits, compared to 30% of Generation X respondents and 21% of baby boomers.
• Generative AI’s greatest benefit is automating time-consuming tasks, more than one-third of respondents said, while 30% said the technology increased efficiency and productivity. For now, “embracing generative AI in the workplace will require a clear set of guidelines and openness to its potential,” Trisha Plovie, Robert Half’s senior VP of future of work, said in a press release.

Dive Insight:

The Robert Half findings suggest employees may be becoming more comfortable with the integration of AI into the workplace, a departure from recent studies revealing widespread concern among U.S. adults that AI will take jobs from humans, including theirs.

Recent college grads have also expressed unease about AI, according to a Cengage Group report released in July. Hiring managers surveyed for the report confirmed that AI could replace some entry-level jobs, teams and skills.

Mundane HR tasks are no exception. Earlier in 2023, Microsoft, IBM and Google Cloud announced automated tools that can create job postings and listings, identify and contact potential candidates and manage employee requests. Language-learning models like ChatGPT can take over sending emails and sorting through applications, sources previously told HR Dive.

However, many HR managers have already embraced the tech, according to Robert Half. A separate survey by the consulting firm found that HR managers said they were using generative AI to write job descriptions, find and screen candidates, and evaluate employee performance.

Managers in other industries have welcomed the technology as well, the firm’s research showed: Those in finance and accounting said they’re using generative AI to automate data entry, while managers in administrative and customer support said they were using it to analyze and categorize customer feedback.

Generative AI is also helping tech managers process large volumes of data to improve system performance, and managers in the marketing and creative sectors are using it to write copy for campaigns, social media and communications, Robert Half said.

Employees who know how to use generative AI platforms — including ChatGPT, BERT and Stable Diffusion — have reasons to be confident, a recent Upwork report found. Generative AI jobs were up more than 1,000% on the platform in the second quarter of 2023 compared to the end of 2022, Upwork said.

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By West Monroe

Artificial intelligence (AI) is all the rage, and its application to businesses is a topic of great interest across various industries. The manufacturing sector is no exception, and while leaders in the industry aren't quite ready to fully commit to the technology, they undoubtedly understand its underlying value. In fact, a substantial 57% of manufacturing companies are still in the experimental phase, testing the waters to identify the best ways to apply and manage AI. The intriguing part is that nearly all respondents, a resounding 96%, firmly believe that AI investments in the manufacturing industry will increase in 2023. It's clear that AI is at the forefront of discussions within the manufacturing world.

The challenge of data abundance

In the manufacturing sector, there is an abundance of industrial data available. However, this data is often challenging to sort through and even more challenging to determine how to effectively harness its power. In the context of AI, there is a substantial opportunity to leverage this data to expand our thinking. This transformative potential of AI extends to every aspect of manufacturing, touching every element of the industry, from the value chain to day-to-day operations to the very technology being employed. The examples provided below span the value chain, touching on product design and sales while also encompassing "back office" functions such as finance and IT. AI's influence in manufacturing is pervasive and multifaceted.

1. Product Innovation and Design: AI-driven generative-design tools are playing a pivotal role in transforming the way products are designed. These tools empower engineers to create and optimize designs for manufacturing processes, taking into consideration factors like material usage, strength and weight. What sets them apart is their ability to generate multiple design options based on specified constraints and performance criteria. This allows for the exploration of design possibilities that human engineers might never have considered.
2. Smart Products: The concept of smart products, physical objects with embedded computing capabilities, is fast becoming a reality in the manufacturing world. These smart products can interact with other devices and systems through a network, collecting, processing and transmitting data. They are equipped with sensors, processors and communication technologies, making them capable of offering improved efficiency, predictive maintenance and remote monitoring. This not only enhances the customer experience but also opens up new revenue streams through subscription services, data monetization and affiliate marketing.
3. Smart Manufacturing: Generative AI technology, when integrated with the Internet of Things (IoT) devices and sensors, offers a game-changing approach to manufacturing. It enables real-time monitoring of equipment conditions, allowing for proactive maintenance and minimizing unplanned downtime. By analyzing data from these sources, AI can predict equipment failures and suggest maintenance activities, reducing unplanned downtime and improving overall equipment efficiency. To illustrate this, consider a recent project with a private-equity firm looking to purchase a manufacturing automation-services company. As part of their due diligence, we assessed the potential for AI, which estimated a 30% programming efficiency gain for PLC/manufacturing-line software programmers.
4. Supply Chain Management: AI has the potential to optimize supply chain processes in manufacturing by predicting demand, managing inventory and streamlining logistics. Generative AI technology can also facilitate communication between suppliers, manufacturers and customers, fostering better coordination and issue resolution. This enhanced supply chain management can lead to cost savings and improved efficiency.
5. Sales and Service: Leveraging AI, manufacturers can offer personalized and customized products at scale. Generative AI, including tools like ChatGPT, can assist in understanding customer requirements and generating custom designs based on buyer preferences. This not only enhances customer satisfaction but also opens up new market opportunities.

The impact on the manufacturing workforce

AI's influence in manufacturing is not limited to processes and products; it also has significant implications for the workforce. Specifically, it is expected to impact the workforce in two major ways: increasing the productivity of certain jobs and creating opportunities for new ones. Jobs that involve repetitive tasks and require minimal training or education (e.g., assembly line work) are prime candidates for automation by AI. AI can perform these tasks more efficiently and accurately than humans, leading to improved operational efficiency.

For example, a prominent beverage manufacturer recently partnered with AI to address the increasing customer demand for their hard seltzers. By implementing a roadmap for data-driven manufacturing operations, the company is projecting a 5-10% improvement in labor productivity and an 18% increase in capacity utilization. This transformation is expected to result in an annual revenue increase of $36 million over a span of five years, showcasing the real-world impact of AI on the manufacturing workforce.

Conclusion

In conclusion, AI is poised to bring transformative change to the manufacturing industry. Its impact is substantial and multifaceted, touching every aspect from design to production to sales and customer service. While there are challenges to overcome and adjustments to be made, the potential benefits are immense. AI has the power to make manufacturing more efficient, innovative and customer-centric.

As we stand on the cusp of an AI-driven manufacturing revolution, the industry's leaders must carefully navigate this transformative journey. While AI will automate certain tasks, it will also create new opportunities and improve overall productivity. With its vast potential to streamline operations and enhance customer experiences, AI's role in manufacturing is bound to be a pivotal one. The manufacturing world is on the brink of a significant AI-led transformation, and the future looks promising.

Dive Brief:

• Verge Genomics, a biotechnology company using artificial intelligence to develop therapies for diseases of the nervous system, announced Sept. 8 a four-year partnership with AstraZeneca's rare disease unit Alexion to identify new drug targets.
• As part of the agreement, Verge will receive up to $42 million in upfront, equity and near-term payments. The company also could receive as much as $840 million in milestone payments over the deal, along with royalties on any resulting products.
• The partnership seeks to find and validate new drug targets for uncommon neurodegenerative and neuromuscular diseases. The companies plan to do this by using Verge’s technology, which uses both AI and human tissue data to predict what drug targets are most likely to succeed in clinical testing.

Dive Insight:

Artificial intelligence tools have drawn the interest of many drugmakers hoping to use it to unearth new therapeutic targets.

Launched in 2015, Verge has raised over $100 million from a variety of investors, including the asset management firm Blackrock and large pharmaceutical companies like Eli Lilly and Merck & Co. Rather than follow the typical path for identifying new drug targets, which often involves animal or cell models, Verge uses machine learning to analyze genomic data from human tissue samples. The biotech claims this method allows it to “rapidly advance new data insights into clinical candidates.”

The technology has attracted partners in the past. In 2021, Verge formed a collaboration with Lilly to find and develop treatments for ALS, or amyotrophic lateral sclerosis.

In 2022, the company’s board of directors also grew with the addition of former Biogen executive Al Sandrock.

Verge is currently developing a drug for ALS known as VRG50635. The small molecule drug is meant to restore degenerated neurons by inhibiting PIKfyve — an enzyme that, research indicates, performs various important cellular functions. It advanced into human testing last year. Data from a Phase 1 trial were released this June and showed positive signs with regard to safety and tolerability.

As part of the new collaboration, Alexion will use Verge’s technology to select novel targets with “high-potential” for other indications. Alexion will have the option to license potential targets to advance them through the development and commercialization.

AstraZeneca now has a stake in Verge as well.

Injuries are an ever-present threat in the manufacturing world. In 2021, the manufacturing industry was preceded only by the healthcare and retail industries in its rate of nonfatal work injuries and illnesses. 

AI is poised as a way to address many of the hazardous elements of manufacturing workplaces. The technology can help limit employees’ exposure to loud environments, unwieldy machinery and dangerous tasks by streamlining processes and helping workers focus on less physically risky activities. 

In manufacturing, many of AI’s potential benefits are concentrated in replacing the cause of the most common workplace injuries. These include “musculoskeletal disorders, mainly from overexertion in lifting and lowering, and being struck by powered industrial trucks and other materials handling equipment,” according to an OSHA spokesperson. 

There are several ways to reduce those points of risk, with the most dangerous manufacturing tasks standing to benefit the most. 

In industrial settings, autonomous vehicles such as drones and robots can be used to perform dangerous tasks, including inspecting pipelines, tanks, and other structures, said AI startup Alpamayo co-founder Christoph Netsch. Here, hazards due to worker fatigue, forgetfulness or other human error are significantly reduced.

Maintenance activities often bring an increased risk because it can require workers to go outside of standard safety protocol and “improvising,” Netsch said. These processes can be supported by predictive AI, which can monitor asset conditions in real time and reduce the amount of maintenance needed.

For memory chip maker Micron, early detection of a machinery malfunction via thermal imaging can mean the difference between “making a simple repair and replacing an entire piece of equipment,” which besides being costlier, can mean more risks to the worker. 

With some AI tools, a workplace that’s already digitized or is in the practice of collecting data from various sources is necessary to implement these tools, Netsch said. 

He gave the example of one gas turbine factory that remotely operated gas and oil platforms. In such a setting, establishing a predictive maintenance diagnostic service required workers to first “implement a uniform data structure,” so that data coming in from different sources was standardized.

Some of the easiest ways to implement AI in the workplace are systems employees can bring into the field without significant digitization of the organization. Computer vision, for instance, is a standalone tool that can detect if workers aren’t wearing protective equipment, or even if there are anomalies in production environments. It works because automated systems can detect hazards at a rate that humans cannot replicate.

Virtual training is another area in which AI has the capacity to make the process easier to integrate into operations. While AI cannot serve as a true substitute for real-life scenarios, it can still help prepare workers, according to Muilenburg Manufacturing Consulting founder Michael Muilenburg.

“[If you’re a firefighter], you don't want to light a building on fire every single day,” Muilenburg said. “You can use virtual reality to simulate that.” 

While the potential benefits of this technology can translate to a reduction in injuries and improved workplace safety, introducing any new element into a manufacturing site introduces a new set of risks that managers should account for.

Whether it’s an industrial robot or automated pallet wrapper, workers should be made aware of the unique hazards associated with working around these machines, said an OSHA spokesperson. Otherwise, they simply risk replacing one source of hazards with another.

Muilenburg said he’s an advocate of a hybrid approach, in which AI and workers can each use their strengths in tandem. He cites one company which opted to have people, not robots, working the assembly line, “frankly because humans were better at it,” while AI-powered robots were responsible for the final inspection of the parts. 

“That approach was really good because the robot was more consistent than the human eye at catching these these little deviations,” Muilenburg said. “So I thought that was a great application of the humans adding the value and the robot simply inspecting and qualifying the final result.”

The industrial metaverse might still sound far-off, but manufacturers are already exploring how the technology could produce new ways to cut costs and improve operations.

The idea is to use virtual technologies and artificial intelligence to enable both employees and executives to try new processes and ideas — without the risk or expense associated with experimenting in the physical world. 

Some companies are already deploying the technology. Earlier this year, auto manufacturer BMW started using industrial metaverse software created by Nvidia, called the Nvidia Omniverse. The omniverse combines Nvidia graphics, accelerated computing, simulation and AI technologies onto one platform, and will allow BMW to model logistics and robots at plants before they enter production.

“[The metaverse is] not very prevalent yet, but it certainly has vast potential,” said Lisa Anderson, president of LMA Consulting, a supply chain and manufacturing consultancy. “It has so many applications. I think we haven't even discovered or thought through all of the ways this can be utilized.”

Right now, one of the simplest applications of the technology is employee training. Surgeons already practice procedures using virtual reality, while firefighters rehearse navigating dangerous fires with the technology. Now, manufacturers are exploring using the same approach to prepare manufacturing employees for work on the shop floor.

“The metaverse — as people are popularly calling it — gives the ability to conduct training in sensitive environments in a manner that the people that are being trained are safe, as is the infrastructure,” explained David Crawley, a professor of practice at the University of Houston who has previously worked on industrial manufacturing projects. 

Nokia, meanwhile, is working with firms, including the aircraft manufacturer Dassault, on industrial metaverse applications, including digital twin technology, the company said. The technology could also reduce the need for training equipment, said Tom Richter, the global head of discrete and process manufacturing verticals for digital industries at Nokia.

Virtual reality-based training may be especially appealing to younger people who are more familiar with using smartphones and virtual reality headsets, said Kevin Krot, head of mobility and industrial products at the operations and supply chain consulting firm SGS Maine Pointe.

“If you look at the manufacturing factories, they're having an aging issue right now,” Krot added.  “Your workforce will need it because you won't have the 20 years of experience and you're going to have to train people up and this will be a way to increase their training and make the throughput efficiency and quality that is needed.”

More ambitious uses of the technology could involve improving manufacturing operations, added Anderson, of LMA Consulting. Manufacturers can use the technology to model new products digitally, without, for example, having to waste materials.

The metaverse can also help with improving overall factory operations, including by allowing remote technicians to virtually explore problems and provide remotely provide guidance to equipment maintenance employees on the ground — which saves costs. In addition, the technology can help with exploring and optimizing different workflows, Richter said.  

“The ability to virtualize more and more aspects of the physical world will in future not only allow for answers to rather simple questions like ‘does this size of the machine fit into this space’ based on a 3D-model, but to simulate the behavior of the complex environment of shopfloors or even full-scale plants,” he added. 

Investing in metaverse technologies does come with caveats. The technology is costly and can require purchasing software and hardware, including sensors and headsets.

And while this technology can help companies save money as they attempt new innovations, many are also hesitant to invest in technology that’s “proactive,” rather than “reactive,” Krot said. Generally, industrial metaverse applications are still in their infancy, which means they may not be affordable or viable for many companies right now, even if they will be advantageous down the line. 

Setting up one of these services can also require skilled technicians some manufacturing companies don’t necessarily have. In addition, these technologies might be harder to deploy if there’s a technology leadership gap, a problem many companies are facing amid labor constraints in the manufacturing industry. 

Still, it’s possible that industrial metaverse applications could save manufacturers from making a range of expensive or time-intensive mistakes — by learning from them in the metaverse first. 

“You have to have that commitment to have that long-term vision to make the capex change, sometimes it's going to be maybe a five-year or more payback,” added Krot. “When we get into a wave where there's more capital expenditures, larger programs that justify this, it will be the future.”

As food and beverage companies look to artificial intelligence to modernize and streamline their plants, Jonathan Darling believes the technology can be used to bring CPGs closer to their consumers.

Darling, the industrial automation market segment leader for Schneider Electric, told Food Dive, the ultimate goal of his company is to bring tools like AI to companies to streamline their supply chains in an effective way.

One way AI can be effectively utilized by food and beverage companies, he said, is contextualizing data points to center their consumers in the products they create and what consumer groups they should serve — for example, using data that shows if a consumer is more likely to buy Oreos since 70% of their social media followers bought them.

“It can help to analyze data to see if decisions can be made on new equipment investments or, if they need to shift how they're making a product to reduce overuse of ingredients or loss of ingredients through manufacturing issues or efficiencies,” Darling said.

Several food and beverage companies have leveraged and increased their reliance on AI in recent years for new product development, often in burgeoning categories like better-for-you foods. In 2022, Mars entered a collaboration with life sciences company PIPA to advance the development of plant-based ingredients for new products. And in April 2023, beverage giant AB InBev announced the development of a “futuristic beer” — Beck’s Autonomous — which was selected by artificial intelligence after developing millions of flavor combinations. And ADM is partnering with food tech company Brightseed to develop immunity-boosting ingredients.

Darling said that while AI is a game changer, its outputs are only made up of its inputs.

“We understand that the tool is not the answer, but it is the conduit that helps companies move towards the achievement and accomplishment of their goals,” Darling said.

Darling said Schneider Electric works with a variety of CPGs — large breweries, coffee manufacturers and water bottlers — to verify that the machinery in its facilities is not at risk of obsolescence. It then helps companies create a roadmap to implement new equipment into its plants, in terms of budgets and resource allocation.

While there are fears among some observers about the potential for increasing artificial intelligence leading to mass labor displacement, Darling said food and beverage manufacturers can use AI to improve their plants and keep them running. Specifically with the recruitment process, he said, the technology can help ensure candidates are qualified for the positions.

“I think a lot more comfort is going to come in learning how to use it as a tool versus as a solution,” Darling said.

How tech could drive down emissions

Companies looking to lower their carbon footprint over the next decade — particularly indirect scope 3 emissions from ingredient transportation and waste, which make up 87% of industry emissions — will turn to AI to help discover where proper investments can be made.

Darling said the biggest issue automation companies run into when attempting to make food and beverage supply chains more sustainable is working to implement new software and data without any major disruptions to their production.

While food companies may make lofty expectations for their own abilities, Darling said they must be tethered to tangible outcomes. He quoted Bill Gates, who once said people often “overestimate what they can do in one year and underestimate what they can do in ten years.”

But Darling doesn’t think the food industry’s emissions goals are unachievable. He said companies will primarily be concerned about showing a positive return on investment into sustainable practices — something they will need to continue working towards. This includes tailoring strategies to specific locations of facilities and having the right people in charge to course correct.

“What’s needed is an internal audit of where we are at at our current state, and what is actually achievable and actionable that's going to help us move the needle towards that five- or 10-year goal when it comes to reducing emissions,” Darling said.

When Schneider Electric works with food companies, he said, it details risks that currently exist in its supply chain and examines whether new technologies can help mitigate them.

Dive Brief:

• Chipmaker NVIDIA reported record revenue of $13.51 billion for its second quarter of fiscal 2024, up 88% from the previous quarter and a 101% jump year-over-year, according to its earnings report released Aug. 23.
• The results — which come as the demand for and some would say frenzy over generative AI technology continues to rise — follow a less frothy first quarter in which the company reported revenue was down 13% year-over-year but rose 19% from the previous period to $7.19 billion,
• There is “tremendous demand for NVIDIA accelerated computing and AI platforms,” CFO Colette Kress said Wednesday during the company’s earnings call, with major companies — including Amazon Web Services, Microsoft Azure, and Oracle Cloud — already deploying “in volume” systems which use the company’s chips.

Dive Insight:

The Santa Clara, California-based company’s revenue shattered Wall Street expectations of $11.04 billion, and comes as “a new computing era has begun,” founder and CEO Jensen Huang said in a statement included in the release. “The race is on to adopt generative AI,” he said.

The hype around generative AI technology’s potential has not slowed down since the launch of OpenAI’s ChatGPT tool in November 2022, with a report last week from Gartner showing that the technology is at the “peak of inflated expectations” especially when it comes to revenue and sales.

Chipmakers including NVIDIA and Broadcom have moved swiftly to dominate the nascent generative AI space, with Broadcom anticipating revenues generated by the technology will account for over 25% of semiconductor revenue in fiscal 2024, CFO Dive reported in June 2023.

NVIDIA, meanwhile, has been investing in AI for over a decade, Malcolm DeMayo, global VP, financial services industry for the chipmaker told CFO Dive in a previous interview — and the dawn of ChatGPT, which was trained on NVIDIA chips, has pushed the company’s market capitalization to over $1 trillion.

The company’s strong results over the past several quarters has since turned it into a bellwether for generative AI demand, with the technology driving “exponential growth” in the computing space, Kress said in May 2023 during the company’s first quarter earnings results.

Its second quarter revenue spike has only solidified its position as a key player in the nascent generative AI space, with CFRA Research Senior Equity Analyst Angelo Zino noting that NVIDIA “is probably going to be the most important company to civilization over, we think, the next five to 10 years” in an interview Aug. 23 with Yahoo Finance Live.

NVIDIA also reported record revenue for its data center sector of $10.32 billion for the quarter ended July 30, 2023, an increase of 141% from the previous quarter and a 171% rise year-over-year.

Demand for its data center offerings was particularly high in the U.S. for the company’s second quarter, “as customers direct their capital investments to AI and accelerated computing,” Kress said, who also cited steady demand in China with data center revenue falling in the company’s historical range for the quarter.

However, Kress also warned against the long-term effects of potential restrictions that have been floated by the U.S. government regarding the same of the company’s chips in China. The Biden administration is considering new restrictions on the export of AI chips to China, which could halt the ability of customers in the country and others to buy them without a license, the Wall Street Journal reported in June 2023.

The company expects to see revenue climb to approximately $16 billion in the third quarter of fiscal 2024.