{"id":54491,"date":"2023-08-29T15:57:43","date_gmt":"2023-08-29T19:57:43","guid":{"rendered":"https:\/\/ifintechworld.com\/markets\/why-generative-ai-could-grow-even-after-a-set-of-bad-nvidia-earnings\/"},"modified":"2023-08-29T15:57:46","modified_gmt":"2023-08-29T19:57:46","slug":"why-generative-ai-could-grow-even-after-a-set-of-bad-nvidia-earnings","status":"publish","type":"post","link":"https:\/\/ifintechworld.com\/?p=54491","title":{"rendered":"Why Generative AI Could Grow Even After A Set Of Bad Nvidia Earnings"},"content":{"rendered":"<div>\n<p>Does the rush of capital into Generative Artificial Intelligence depend too heavily on one company?<\/p>\n<p>After all, the Generative AI shot heard round the world was fired May 24 when chip designer Nvidia forecast much-faster-than-expected second quarter growth \u2014 sending its market capitalization up $250 billion.<\/p>\n<p>That left the investment world on edge in the days leading up to Nvidia\u2019s actual Q2 results. But the chip designer did not disappoint \u2014 instead it grew faster than expected and raised its guidance.<\/p>\n<p>How so? On August 24, Nvidia reported its revenue soared 88% from the previous quarter ($2.51 billion more than forecast) while the chip designer guided investors to expect 170% revenue growth in the third quarter ($3.4 billion above estimates).<\/p>\n<p>Can Nvidia keep blasting through investor expectations? What would happen to capital flows into the Generative AI ecosystem if Nvidia delivered disappointing earnings or a more conservative forecast?<\/p>\n<p><fbs-ad position=\"inread\" progressive=\"\" ad-id=\"article-0-inread\" aria-hidden=\"true\" role=\"presentation\"><\/fbs-ad><\/p>\n<p>I do not know the answer to the first question, but it partially depends on the answer to the second one: Capital will keep flowing into Generative AI as long as companies earn high payoffs from using the technology to solve pressing business problems.<\/p>\n<p>Based on my interviews with five experts \u2014 from KPMG, EY, Forrester Research, PwC, and MIT\u2019s Sloan School \u2014 I see strong evidence of business demand for Generative AI. Here are the reasons why:<\/p>\n<ul>\n<li>Companies are afraid of missing out on the productivity-boosting properties of Generative AI.<\/li>\n<li>Generative AI\u2019s ease of use has caused it to become widely adopted by the general public.<\/li>\n<li>Companies in many industries are finding beneficial end-uses of Generative AI.<\/li>\n<li>Companies see Generative AI\u2019s risks as manageable.<\/li>\n<\/ul>\n<h2 class=\"subhead-embed color-accent bg-base font-accent font-size text-align\">Why Companies Are Adopting Generative AI<\/h2>\n<p>Companies are adopting Generative AI because they fear missing out on its benefits. As Sreekar Krishna, KPMG\u2019s national leader artificial intelligence &amp; head of data engineering, told me in an August 21 interview, \u201cPeople are seeing value across the board. KPMG started experimenting five months back with 15 to 20 people. A couple weeks later thousands of people were using it. Now there are 15,000 to 16,000 people at KPMG whose are using it.\u201d<\/p>\n<p>To be sure, some executives are skeptical. As Joe Atkinson \u2014 PwC\u2019s vice chair, U.S. chief products and technology officer \u2014 said in an August 25 interview, \u201cI have been on a roadshow meeting with clients and partners. I went to 15 markets and met with 100 clients. There was peer-to-peer sharing. Overall, there is a sense of excitement and some skepticism. While people see some hype, we think Generative AI will have a massive impact.\u201d<\/p>\n<p>Yet many companies are either using Generative AI or experimenting with it. In an August 22 interview, Forrester Research Senior Analyst Rowan Curran said, \u201cThere is a huge amount of excitement and hype regarding Generative AI.\u201d<\/p>\n<p>\u201cCompanies are building and using large language models and generative adversarial networks [in which a Generator creates fake data and a Discriminator tries to distinguish fake from real],\u201d Curran added. \u201cThey are experimenting with full production Generative AI \u2014 building and buying the needed capabilities. Others are doing small explorations. A vanishingly small number of companies are doing nothing with Generative AI.\u201d<\/p>\n<p>Companies are in a hurry to capture what they see as Generative AI\u2019s productivity and efficiency improvements. Dan Diasio, EY\u2019s global consulting AI and automation leader, told me in an August 23 interview that he spoke with 1,300 CEOs in July. Sixty-five percent said they \u201cwere clear AI would have a significant role in their business.\u201d<\/p>\n<p>\u201cEverybody is dipping their toe in the water,\u201d Diasio said. \u201cWhat is different about Generative AI is they don\u2019t just want to learn about it; they want to push it into production. It is already creating value. It is augmenting work \u2014 resulting in more productivity and greater efficiency.\u201d<\/p>\n<h2 class=\"subhead-embed color-accent bg-base font-accent font-size text-align\">Why Generative AI Has Grown So Much Faster Than Other Technologies<\/h2>\n<p>Generative AI has been around for decades, however, experts agree its ease of use compared to other technologies \u2014 such as blockchain, the metaverse, and NFTs \u2014 resulted in very rapid scaling.<\/p>\n<p>Unlike these other technologies, companies are afraid of being left behind. \u201cAcross the board there is interest. Nobody wants to be late the way they were with mobile,\u201d Krishna said. \u201cWhy is Generative AI taking off faster than blockchain technology did? Blockchain is an engineering achievement. But the realization of blockchain was cryptocurrency and other applications. Blockchain has made improvements in cybersecurity \u2014 enabling the sharing of data across industries.\u201d<\/p>\n<p>Yet Krishna sees Generative AI as different. \u201cIt enables the democratization of access to data \u2014 you no longer need a data scientist to get access,\u201d he said. \u201cCompanies see Generative AI as easy to test and worth putting the energy into it. When something becomes widely adopted; if you delay, your customers will push you into adopting it.\u201d<\/p>\n<p>PwC sees Generative AI as a user-friendly front end to a powerful technology\u2014 analogous to the effect of the Web browser for the internet in the 1990s. \u201cMost people are not engaging with blockchain and augmented reality.\u201d Atkinson said. \u201cBy contrast, it is easy for most people to see how Generative AI applies to what they do everyday.\u201d<\/p>\n<p>Chatbots are doing for AI what the graphical user interface on Web browsers did for the internet. \u201cFor decades, a small number of people in academic and military settings used the internet, Atkinson explained. \u201c[When Netscape (and other) browsers were launched], internet business cases emerged. Generative AI makes AI accessible. People don\u2019t understand a black box.\u201d<\/p>\n<p>Forrester sees Generative AI as far more appealing to society. As Curran said, \u201cThe rate of adoption is extremely high. It is different from previous technologies like NFTs and the metaverse. Generative AI models are very visceral with real world applications. There is a huge explosion of ideation.\u201d<\/p>\n<p>EY sees Generative AI as a consumer-friendly technology. \u201cUnlike many previous technological waves, Generative AI is widely democratized,\u201d Diasio said. \u201cIt is accessible in every application. The challenge is to find the areas with the most value balanced against the risk.\u201d<\/p>\n<h2 class=\"subhead-embed color-accent bg-base font-accent font-size text-align\">Specific Corporate End-Uses For Generative AI<\/h2>\n<p>Companies are finding valuable applications of Generative AI for various business functions and to solve industry-specific business challenges.<\/p>\n<p>EY\u2019s Diasio provided the following list of common Generative AI functional solutions:<\/p>\n<ul>\n<li><strong>Knowledge management: <\/strong>Getting answers to questions from employees, customers and consumers.<\/li>\n<li><strong>Customer service: <\/strong>With high turnover in contact centers, onboarding new professionals fast, finding answers, and doing more.<\/li>\n<li><strong>Sales and marketing:<\/strong> Writing product descriptions and marketing content, and designing new packaging.<\/li>\n<li><strong>Information technology: <\/strong>Writing code, going faster and getting through hands-on keyboard work.<\/li>\n<li><strong>Procurement:<\/strong> Summarizing contracts and complying with contract provisions.<\/li>\n<\/ul>\n<p>He also provided examples of industry-specific Generative AI applications:<\/p>\n<ul>\n<li><strong>Technology: <\/strong>Less about productivity improvement and more about Generative AI as a source of new revenue-generating products.<\/li>\n<li><strong>Consumer products:<\/strong> Used for sales and marketing, building better product descriptions or marketing content.<\/li>\n<li><strong>Wealth and asset management<\/strong>: Empowering the sales force with knowledge about products and offerings.<\/li>\n<li><strong>Private equity:<\/strong> Deploying Generative AI in their portfolio companies.<\/li>\n<\/ul>\n<p>Many KPMG clients are using Generative AI. \u201cBetween 200 and 300 of our clients are investing in Generative AI,\u201d Krishna said. \u201cCompanies investing the most are Microsoft, Google, Meta. They are investing billions in the technology.\u201d<\/p>\n<p>Financial services and manufacturing firms are also investing. \u201cThese are industries driven by manual processes,\u201d he added. \u201cGenerative AI can reduce the workload on everybody\u2019s hands. For example, 30 days is the average time it takes to close a mortgage. People don\u2019t want to wait that long. Manufacturers are starting to use Generative AI because it can interpret images and videos.\u201d<\/p>\n<p>KPMG is using the technology internally. Krishna explained that KPMG has four use cases that \u201ccut the time for complying with documentation and work paper requirements.\u201d<\/p>\n<p>\u201cFor example, every three months a chief information security officer must certify the company is in compliance, that everyone has been trained, and so on,\u201d he said. \u201cWe have mini-chatbots enabling users to ask a few questions to automate a service.\u201d<\/p>\n<p>PwC has an ambitious forecast for how much Generative AI will add to the global economy. Its estimate is $15.7 trillion by 2030.<\/p>\n<p>\u201cThis will come from a productivity gain of at least 25% \u2014 unlocking capacity to build value,\u201d Atkinson said. \u201cGenerative AI will create new business models and make existing models better.\u201d<\/p>\n<p>PwC is among many firms offering consulting services to help companies with Generative AI. \u201cCompanies are seeking consulting help on everything from Generative AI strategy to implementation,\u201d Atkinson added. \u201cWe set up an AI factory with use cases, AI specialists and data scientists. We are helping with specific value chain applications of Generative AI such as customer care, financial reporting, and training employee.\u201d<\/p>\n<p>Forrester sees many companies using Generative AI for marketing and communications. \u201cPeople are using it for marketing and design \u2014 coming up with ideas, drafting, and developing content for tag lines, white papers, blogs, and social media posts,\u201d Curran said. \u201cPeople are also using if for inter and intra-office communications \u2014 including sales prospecting letters and memos.\u201d<\/p>\n<p>Forrester also envisions Generative AI saving time in customer-focused business processes and observes some companies struggling to turn prototype solutions into widely used applications.<\/p>\n<p>\u201cGenerative AI can take information and summarize and translate it in comprehensible ways,\u201d Curran added. \u201cIt can speed up existing processes such as call centers by retrieving knowledge and summarizing transcripts of customer calls \u2014 saving time and money. People are not having much trouble getting started, but they do have challenges in going from a prototype to enterprise-wide deployment with low latency and tight access controls.\u201d<\/p>\n<p>Startups are developing industry-specific Generative AI solutions. As George Westerman, MIT Sloan School senior lecturer, told me in an August 23 interview, \u201cSome startups are selling Generative AI services for law, healthcare, and finance. Now Generative AI is part of what we do.\u201d<\/p>\n<p>Westerman urges business leaders to get started with Generative AI. \u201cYou should get started doing something, put your toe in the water,\u201d he told me. \u201cThere is fear of being left behind. Customer facing messaging is taking off fast. 60% of companies are using Generative AI \u2014 sending emails to customers. For customer service, summarizing everything we have done before takes it to a new level.\u201d<\/p>\n<h2 class=\"subhead-embed color-accent bg-base font-accent font-size text-align\">How Companies Are Managing Generative AI\u2019s Risks<\/h2>\n<p>Companies are handling this in different ways. They are adopting policies to control how their employees use ChatGPT, piloting potential high value applications before rolling them out companywide, adopting frameworks for responsible AI, and building company-specific Generative AI applications to protect proprietary information and limit hallucinations.<\/p>\n<p>KPMG observes some companies using Generative AI tools that give them greater control. \u201cMicrosoft\u2019s<fbs-ticker data-name=\"MSFT\" data-href=\"https:\/\/www.forbes.com\/companies\/microsoft\" data-type=\"stock\"><br \/>\n  MSFT<br \/>\n <\/fbs-ticker> OpenAI Studios \u2014 which has been operating for the last six or seven months \u2014 provides companies a semi-controlled environment,\u201d Krishna said. \u201cThey can use OpenAI without giving it proprietary information, they can choose what data they will not share with OpenAI, retain full rights to the data, and require OpenAI to delete their data.\u201d<\/p>\n<p>PwC is reducing risk by testing applications on a small scale before rolling them out. Atkinson said the company is piloting Generative AI across multiple teams and will eventually make it more widely available.<\/p>\n<p>\u201cWe want to deploy it in a responsible way in a safe environment with help from our employees,\u201d he added. \u201cIt will be used for customer care, help desk, analysis of data, reading and summarizing lots of data. We will train people firm-wide in responsible uses of Generative AI.\u201d<\/p>\n<p>PwC observes companies taking different approaches to balancing Generative AI\u2019s opportunities against its risks. \u201cSome are shutting down employee access to third-party chatbots; some are allowing the use of chatbots with guardrails; others \u2014 like PwC are deploying Generative AI chatbots trained with company-only data,\u201d Atkinson said.<\/p>\n<p>PwC sees an opportunity to limit the likelihood of hallucinations and protecting proprietary data by following responsible AI principles \u2014 such as infrastructure, governance, training, and human review. Atkinson said companies can use specific data sets \u2014 he calls micro-models \u2014 so they \u201ccan secure proprietary data.\u201d<\/p>\n<p>Forrester advises companies to avoid free, online chatbots. As Curran said, \u201cUsing free, online chatbots is risky and dangerous because you don\u2019t have control of inputs or outputs.\u201d To limit such risks, he advises people to use applications such as Writer, Jasper.ai, Grammarly, or Microsoft 365 Copilot.<\/p>\n<h2 class=\"subhead-embed color-accent bg-base font-accent font-size text-align\">Envisioning Generative AI\u2019s Future<\/h2>\n<p>Since Generative AI\u2019s widespread adoption is in its early days, it is unclear how it will evolve. In the next few years, I am guessing companies will implement many of the Generative AI applications discussed above that save on costs, boost efficiency, and raise productivity fairly quickly.<\/p>\n<p>After that, companies may invest more heavily in Generative AI applications that create more customer value than competing ones and are difficult for rivals to copy. While it could be many years before all such applications are built, at some point \u2014 unless it reinvents itself \u2014 Generative AI will become a mature industry.<\/p>\n<p>Until then, capital will pour into the Generative AI ecosystem \u2014 benefiting suppliers from chip makers to management consultants.<\/p>\n<\/div>\n<p>Read the full article <a href=\"https:\/\/www.forbes.com\/sites\/petercohan\/2023\/08\/27\/why-generative-ai-could-grow-even-after-a-set-of-bad-nvidia-earnings\/\" target=\"_blank\" rel=\"noopener\">here<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Does the rush of capital into Generative Artificial Intelligence depend too heavily on one company? After all, the Generative AI shot heard round the world was fired May 24 when chip designer Nvidia forecast much-faster-than-expected second quarter growth \u2014 sending its market capitalization up $250 billion. That left the investment world on edge in the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":53617,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[241],"tags":[83],"class_list":["post-54491","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-markets","tag-featured"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Why Generative AI Could Grow Even After A Set Of Bad Nvidia Earnings | iFintechWorld<\/title>\n<meta name=\"description\" content=\"Does the rush of capital into Generative Artificial Intelligence depend too heavily on one company? 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