Economic Potential of Generative AI in Chip Design

While the specificity offers enhanced performance and efficiency, it also diminishes the flexibility of an AI chip. The lack of versatility prevents it from performing a wide variety of tasks or applications. Its optimized chips for generative AI applications are different from the generally developed GPUs. • Establish an AI-enabled https://chat.openai.com/ digital core by enabling a modern data platform, rearchitecting applications to be AI-ready and adopting a flexible architecture that allows the use of multiple models across your ecosystem. According to our research, most high-tech executives believe GenAI will lead to organizations modernizing their tech infrastructure.

The large language model (LLM) released by OpenAI is the first program to make generative artificial intelligence (AI) easily accessible to the public. Now, the generative AI market is expected to grow from $40 billion in 2022 to $1.3 trillion over the next 10 years. In this article, I aim to demystify how generative AI constitutes a distinct revolution and explore the prospective economic impacts of deploying this technology across diverse sectors.

Applications of Generative AI

The potential benefits to the global economy from increased GenAI productivity could also be substantial. With the US market likely to remain at the forefront of GenAI investment, closely followed by Europe, Japan and China, global GDP could get a boost worth $1.2t (in our baseline scenario) and $2.4t (in the optimistic case) over the next decade. The adoption of generative AI is expected to significantly impact various industries and job markets, including manufacturing, healthcare, retail, transportation, and finance. While it is likely to lead to increased efficiency and productivity, it is also expected to lead to job displacement for some workers. While AI will automate some portion of jobs, it will also create entirely new occupations and sectors.

This gap can be attributed to a lack of understanding of GenAI and how to integrate the technology for revenue growth. Helpfully, too, many generative AI tools will be easier to access than previous technologies. This is not like the advent of personal computers or smartphones, where employers needed to buy lots of hardware, or even e-commerce, where retailers needed to set up physical infrastructure before they could open an online storefront. Many businesses may find that they can work with AI specialists to design bespoke tools.

While other generative design techniques have already unlocked some of the potential to apply AI in R&D, their cost and data requirements, such as the use of “traditional” machine learning, can limit their application. Pretrained foundation models that underpin generative AI, or models that have been enhanced with fine-tuning, have much broader areas of application than models optimized for a single task. They can therefore accelerate time to market and broaden the types of products to which generative design can be applied.

The firm also hopes to vastly expand their access to information and sharpen insights about both target companies and the macro conditions in which they operate. The MVP accelerator put as many as 30 initiatives in motion and institutionalized the company’s ability to innovate. It not only buttressed Multiversity against competitive incursion but will also burnish the company’s exit story.

With its new approach, Groq can boost the economic potential of generative AI within the chip industry. They used the labs to design a Gaudi series of AI processors that specialize in the training of large language models (LLMs). Compared to established giants like NVIDIA, Intel is a fairly new player in the AI chip industry. However, with the right innovations, it can contribute to the economic potential of generative AI.

One example was using generative AI modules to answer routine questions from students about class content or administrative issues that take an inordinate amount of a professor’s time. The initiative removed 80% of those questions from professors’ plates, allowing them to redistribute that time to more value-added activities like course planning and one-on-one interactions with students. Over 95,000 individuals trust our LinkedIn newsletter for the latest insights in data science, generative AI, and large language models. These are custom-built AI chips that specialize in handling neural network computations, like image recognition and NLP. The parallel processing architecture enables the AI chips to process multiple operations simultaneously.

Companies and business leaders

It is one of the well-established tech giants, holding a dominant position within the AI chip industry. It is estimated to hold almost 80% of the global market for GPUs (Graphics Processing Units). Its robust software ecosystem includes frameworks like CUDA and TensorRT, simplifying generative AI development. As per McKinsey’s research, generative AI is set to potentially unlock 10 to 15 percent of the overall R&D costs in productivity value, raising its stakes in the economic impact. Since the economic potential of generative AI can create staggering changes and unprecedented opportunities, let’s explore it.

AI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness. Tim Cook, Apple’s chief executive, has promised investors that the company will introduce new generative A.I. The company’s smartphone rivals, Samsung and Google, have already added Gemini to their newest devices to edit videos and summarize audio recordings. A partnership would extend the long relationship between the companies that has helped deliver everything from maps to search on Apple’s devices.

The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation. Retailers can create applications that give shoppers a next-generation experience, creating a significant competitive advantage in an era when customers expect to have a single natural-language interface help them select products. For example, generative AI can improve the process of choosing and ordering ingredients for a meal or preparing food—imagine a chatbot that could pull up the most popular tips from the comments attached to a recipe.

Discriminative models excel at making predictions from existing data and identifying anomalies. These models power everything from social media content recommendation engines to financial fraud detection platforms. All of us are at the beginning of a journey to understand this technology’s power, reach, and capabilities.

Very quickly, however, the diligence team demonstrated that the tool faced a serious threat in the marketplace. In a matter of days, the team built a series of prototypes using OpenAI’s GPT-4 API and other open-source models. They then tested these “competitors” against the target’s solution and found that all of them performed significantly better in a number of ways.

The technology has been heralded for its potential to disrupt businesses and create trillions of dollars in economic value. While LPUs are still in their early stage of development, they have the potential to redefine the economic landscape of the AI chip industry. The performance of LPUs in further developmental stages can greatly influence the future and economic potential of generative AI in the chip industry.

• The speed at which generative AI technology is developing isn’t making this task any easier.
• Even when such a solution is developed, it might not be economically feasible to use if its costs exceed those of human labor.
• The potential economic benefits of generative AI include increased productivity, cost savings, new job creation, improved decision making, personalization, and enhanced safety.
• The report modelled scenarios to estimate when generative AI could perform each of more than 2,100 “detailed work activities” that make up those occupations across the world economy.

Generative AI helps them pinpoint the market research and competitive analysis needed to underwrite specific opportunities. Generative AI is a critical reasoning engine capable of having an open-ended conversation with a customer, producing rich marketing content, and scanning vast stores of data to provide deeper insights. From our knowledge of different players and the types of chip designs, we can conclude that both factors are important in determining the economic potential of generative AI in chip design. Each factor adds to the competitiveness of the market, fostering growth and innovation.

These tools have the potential to create enormous value for the global economy at a time when it is pondering the huge costs of adapting and mitigating climate change. At the same time, they also have the potential to be more destabilizing than previous generations of artificial intelligence. Our previously modeled adoption scenarios suggested that 50 percent of time spent on 2016 work activities would be automated sometime between 2035 and 2070, with a midpoint scenario around 2053. For example, our analysis estimates generative AI could contribute roughly $310 billion in additional value for the retail industry (including auto dealerships) by boosting performance in functions such as marketing and customer interactions. By comparison, the bulk of potential value in high tech comes from generative AI’s ability to increase the speed and efficiency of software development (Exhibit 5). Our analysis captures only the direct impact generative AI might have on the productivity of customer operations.

EY-Parthenon is a brand under which a number of EY member firms across the globe provide strategy consulting services. We focus on strategies to originate, build, and scale corporate ventures and reimagine your core business for growth. One European bank has leveraged generative AI to develop an environmental, social, and governance (ESG) virtual expert by synthesizing and extracting from long documents with unstructured information.

Artificial intelligence can solve many problems that humans can’t, such as traffic congestion, parking shortages, and long commutes. Gen AI is expected to play a role in improving the quality, safety, efficiency, and sustainability of future transportation systems that do not exist today. In the transportation industry, self-driving vehicles are powered by generative AI, enabling them to navigate roads and make real-time decisions.

This is because AI assistance helped less-experienced agents communicate using techniques similar to those of their higher-skilled counterparts. The deployment of generative AI and other technologies could help accelerate productivity growth, partially compensating for declining employment growth and enabling overall economic growth. In some cases, workers will stay in the same occupations, but their mix of activities will shift; in others, workers will need to shift occupations. The analyses in this paper incorporate the potential impact of generative AI on today’s work activities.

The goal here isn’t to fill seats with less expensive robo investors but to make investment professionals smarter and faster at what they do. One large investor at the forefront of thinking through these issues is backing generative AI initiatives that cut across the investment cycle. The most advanced is a project to help investment professionals become more productive by speeding up (and improving) the bread-and-butter busywork that is critical to sourcing and evaluating deals.

Using an off-the-shelf foundation model, researchers can cluster similar images more precisely than they can with traditional models, enabling them to select the most promising chemicals for further analysis during lead optimization. Banks have started to grasp the potential of generative AI in their front lines and in their software activities. Early adopters are harnessing solutions such as ChatGPT as well as industry-specific solutions, primarily for software and knowledge applications.

Now that we recognize some leading players focused on exploring the economic potential of generative AI in the chip industry, it is time to understand some of the major types of AI chip products. • Focus on talent and reinvent the way your people work by adapting operating models fit for the gen AI era, with a strong focus on talent development and continuous learning and skilling. Building competencies across functions to fully understand the impact of generative AI on people as well as developing the capabilities to provide them with the continuous learning needed to embrace generative AI will play a key role in how the technology is received.

Ensure all AI actions—from design to deployment and use within the organization—drive value while being aware of and protecting against the risks of AI, such as bias or infringement of intellectual property and data privacy. This means taking a close look at the data being used by your models and doing extensive testing before deploying solutions. Syed is Accenture’s High Tech global lead, helping clients with growth strategy, reinvent their business and optimize supply chain.

AI forecaster can predict the future better than humans

The company had trained it extensively on proprietary data, and the selling point was that it could process this complex technical information with a standard of accuracy critical to the company’s customers. Also called linear processing units, these are a specific chip design developed by Groq. These are designed to handle specific generative AI tasks, like training LLMs and generating images. Groq claims its superior performance due to the custom architecture and hardware-software co-design. Each of these players brings a unique perspective to the economic landscape of generative AI within the AI chip industry.

In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences. Unlocking the productivity potential of GenAI will likely require the deployment of both tangible (infrastructure) and intangible (technology, software, skills, new business models and practices) investments. And, as we saw in the first installment of our article series, it could also take time for the productivity benefits of GenAI to materialize. There has generally been a delay between the inception of paradigm-shifting technologies and their diffusion across the economy. But the faster speed of GenAI diffusion could mean that the boost to economic activity could be felt more quickly – that is, in the next three to five years.

To streamline processes, generative AI could automate key functions such as customer service, marketing and sales, and inventory and supply chain management. You can foun additiona information about ai customer service and artificial intelligence and NLP. Technology has played an essential role in the retail and CPG industries for decades. Traditional AI and advanced analytics solutions have helped companies manage Chat PG vast pools of data across large numbers of SKUs, expansive supply chain and warehousing networks, and complex product categories such as consumables. In addition, the industries are heavily customer facing, which offers opportunities for generative AI to complement previously existing artificial intelligence.

Hence, our adoption scenarios, which consider these factors together with the technical automation potential, provide a sense of the pace and scale at which workers’ activities could shift over time. Generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs. Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities. Combining generative AI with all other technologies, work automation could add 0.5 to 3.4 percentage points annually to productivity growth. However, workers will need support in learning new skills, and some will change occupations. If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world.

For one thing, mathematical models trained on publicly available data without sufficient safeguards against plagiarism, copyright violations, and branding recognition risks infringing on intellectual property rights. A virtual try-on application may produce biased representations of certain demographics because of limited or biased training data. Thus, significant human oversight is required for conceptual and strategic thinking specific the economic potential of generative ai to each company’s needs. Generative AI has taken hold rapidly in marketing and sales functions, in which text-based communications and personalization at scale are driving forces. The technology can create personalized messages tailored to individual customer interests, preferences, and behaviors, as well as do tasks such as producing first drafts of brand advertising, headlines, slogans, social media posts, and product descriptions.

Tools — which exploded onto the tech scene late last year — accelerated the company’s forecast. The report from McKinsey comes as a debate rages over the potential economic effects of A.I.-powered chatbots on labor and the economy. Global economic growth was slower from 2012 to 2022 than in the two preceding decades.8Global economic prospects, World Bank, January 2023.

As generative AI gains speed, it will become increasingly critical for firms to institutionalize this kind of scrutiny. Deal teams should be doing a fast analysis of any target company, asking whether generative AI is likely to have an impact—positive or negative—in the years ahead. Anyone with an internet connection now has access to tools that can answer almost every question under the sun, write everything from university essays to computer code and produce art or photorealistic images. Build the workforce capabilities needed to realize organizational strategy, with help from our data and AI-driven platforms. Gen AI is expected to help address this shortage through increased efficiency, allowing fewer workers to serve more patients.

Today, approximately 60% of the workforce holds positions that did not exist in 1940. Nearly 85% of employment growth since that time is due to new occupations created through technological advances. We hope this research has contributed to a better understanding of generative AI’s capacity to add value to company operations and fuel economic growth and prosperity as well as its potential to dramatically transform how we work and our purpose in society. Companies, policy makers, consumers, and citizens can work together to ensure that generative AI delivers on its promise to create significant value while limiting its potential to upset lives and livelihoods.