The economic potential of generative AI : The next productivity frontier by Intelligent Enterprise Institute Intelligent Enterprise Institute

Google is excited about the economic potential this new technology can offer Canadians and Canadian businesses, as well as the improvements to the country’s sustainability, productivity and quality of living. We are just beginning to see what this next wave of technology is capable of and there is no country better positioned to embrace this new AI frontier. Previous generations of automation technology often had the most impact on occupations with wages falling in the middle of the income distribution. For lower-wage occupations, making a case for work automation is more difficult because the potential benefits of automation compete against a lower cost of human labor. Additionally, some of the tasks performed in lower-wage occupations are technically difficult to automate—for example, manipulating fabric or picking delicate fruits. Some labor economists have observed a “hollowing out of the middle,” and our previous models have suggested that work automation would likely have the biggest midterm impact on lower-middle-income quintiles.

Enterprises will likely look to derive more value out of their existing data assets, pushing up the consumption of pipelining tools, databases, and more. As AI matures, we expect cloud computing, cybersecurity, and digital media to receive strong boosts, allowing leading platforms in these arenas to solidify their positioning and compound the addressable market opportunities at their disposal. Hardware is evolving alongside software, particularly the hardware needed for compute infrastructure, including high-end semiconductors. Two emerging trajectories in AI compute infrastructure are likely to drive significant commercial impact, including today’s GPU shortages and resulting market asymmetries, as well as and shifts in AI workloads from training to inference. In our view, the most important and clear emerging capabilities for generative artificial intelligence are multimodality, value engineering, and generative agency.

United States Government Accountability Office — Artificial Intelligence in Health Care

Input from the entire C-suite is essential, as a variety of factors across core business functions must be addressed. For example, concerns over how people’s jobs may change, whether they will be automated, and opportunities for reskilling should be addressed with care. An otherwise bottom-up approach, driven by individual project teams with a proof-of-concept (POC) mindset, is apt to result in implementation fatigue, risk proliferation, and disillusionment. To effectively and safely scale a GenAI POC for enterprise use, guardrails must be established (learn more). Now consider a scenario where a shop floor mechanic is tasked with troubleshooting a turbine with a vibration problem arising from a multitude of potential causes. IoT sensors installed on the turbine could help automatically detect and convey real-time, contextual information about the problem.

At the recently hosted India Mobile Congress in October this year, technology providers showcased multiple 5G solutions for industrial and home use cases, signing multiple deals with telecom operators that seek to further expand their 5G coverage. Sunil Bharti Mittal, chairman of Airtel’s holding firm Bharti Enterprises, said at the event that the telco will cover all of India with 5G services by March 2024. This is one of the things she thinks provides enterprises with a new level of agility and productivity, which is especially relevant in regions with significant human skills gaps, like Japan, Australia, and Singapore. We also expect that natural language will come to a variety of functions around the blocking and tackling involved with the data lifecycle, from cataloging data to discovering, managing, governing and securing it. Atlan, a data catalog provider focusing on DataOps provides glimpses of what we expect to see more of this year. Atlan starts with a common natural language search function that is quite similar to the natural language query capabilities that a growing array of BI tools are offering.

AI and Biometrics Privacy: Trends and Developments

Further, the LLMs really do understand natural language, and therefore are being pushed into service as a new consumption layer for programs. We’re also seeing broad adoption in areas of professional pairwise interaction such as therapy, legal, education, programming, and coaching. Many of the use cases for generative AI are not within domains that have a formal notion of correctness. In fact, the two most common use cases currently are creative generation of content (images, stories, etc.) and companionship (virtual friend, coworker, brainstorming partner, etc.). They can guide the model toward the answer they’re seeking, rather than requiring the company to shoulder a pool of humans to ensure immediate correctness. For example, the release of the large image model Stable Diffusion blew away some of the most successful open-source developer projects in recent history with regard to speed and prevalence of adoption.

Empowering your workforce with the latest knowledge of AI technologies and the positive impact they can have within your business. A second limitation is that we assume that the incumbent cannot acquire the entrepreneurial firm. A particular case would be if a potential acquisition by the incumbent serves the sole purpose of shutting down the invention. While this feature is sometimes relevant, there is also evidence that many (leading) firms, such as Microsoft, Google, and Ericsson, acquire startups to incorporate them highly efficiently into their businesses. In fact, entrepreneurs must still employ a sufficiently creative (risky) strategy to become interesting enough to be a target.

However, a recent Council on Foreign Relations report argued that TSMC’s position may make potential aggression by Beijing more likely, as Beijing could have an interest in gaining control of the company and using it for geopolitical leverage. While these US-led export controls have marked a clear turning point in US-China technology competition, there have been challenges to enforcement, including due to the need to coordinate with international partners over complex, global supply chains. Over the last 20 years, “traditional AI” has become pervasive, powering advertising algorithms, content recommendations, and social-media targeting.

The technology could also monitor industries and clients and send alerts on semantic queries from public sources. The model combines search and content creation so wealth managers can find and tailor information for any client at any moment. Banking, a knowledge and technology-enabled industry, has already benefited significantly from previously existing applications of artificial intelligence in areas such as marketing and customer operations.1“Building the AI bank of the future,” McKinsey, May 2021. 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.

Broadly speaking, the insurance provider’s incentive is to pay the least amount possible per patient relative to their insurance costs. And the medical provider is (financially) incentivized to bill insurance as much and as often as possible, regardless of what’s truly best for the health of the patient. The factor common to all the industries portrayed in red is that they’re often insulated from price competition due to misaligned incentives, often as a result of regulation or industry structure. The timing of Founders looking to start marketplaces incorporating fintech components is ripe, not only because of the catalysts mentioned above, but also because of the massive untapped opportunities presented by market inefficiencies stemming from misaligned incentives. Pioneering in adaptable, anticipatory leadership with a forte in interdisciplinary innovation impacting sectors like aviation, energy, healthcare & other. Dance, linguistics, drama, law—the power and potential of AI is reshaping nearly every academic discipline at USC.

From virtual influencers themselves that have added novelty to a crowded influencer market, to the proliferation of AI in the content that influencers make for themselves, AI will be at the centrepiece of this entire conversation. The proliferation of increasing intelligence will also lead to the creation of an alternate creator economy in the coming year, which will exist alongside human influencers with an already significant following. 2024 is the year when an increasing number of companies are likely to experiment with such technology, including in India. Tony Baer is principal at dbInsight LLC, which provides an independent view on the database and analytics technology ecosystem. Baer is an industry expert in extending data management practices, governance and advanced analytics to address the desire of enterprises to generate meaningful value from data-driven transformation. In the near term, we expect to see gen AI database innovation to focus on structuring the data.

Read more about The Economic Potential of Generative Next Frontier For Business Innovation here.

• In the U.S., recent waves of tech innovation and adoption have greatly altered the nation’s economic landscape.
• Overall, semiconductor export controls’ importance will increase as future AI-enabled capabilities require greater computing power than legacy chips are able to provide.
• 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.
• As a result of these reassessments of technology capabilities due to generative AI, the total percentage of hours that could theoretically be automated by integrating technologies that exist today has increased from about 50 percent to 60–70 percent.
• Along with so-called embeddings—which map the relationships between words and use a unique neural architecture—the Transformer makes it possible for the model to learn in a self-supervised way.
• So far, a combined investment of nearly $500 million over five years has established 19 of these institutes, with links to 37 states.