The Next Phase of Generative AI Adoption
00:00Today on the AI Daily Brief, we are discussing the next phase in generative AI adoption,and before that on the brief, OpenAI shuts down a set of Iranian chatGBT accounts.The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.To join the conversation, follow the Discord link in our show notes.Welcome back to the AI Daily Brief Headlines edition,all the daily AI news you need in around five minutes.In our coverage of SB 1047, the controversial California AI regulatory bill that'sbeen in the discussion recently, one of the big complaints from people who are opposed to the bill
00:41is that it prioritizes existential risk factors versus those that are more here and now.This is the basis, for example, of a lot of the current congressional Democrats from Californiasaying that SB 1047 lacks an evidentiary basis. Whether you agree with that or not in termsof SB 1047, if you watch the news, it's definitely clear that there are challenges here and now withthat we're starting to have to deal with. Case in point, OpenAI, recentlydeactivated a chat GPT account that was linked to an Iranian disinformation campaign.One of the big concerns that people have had is AI impacting the current presidential election cycle.And while this wasn't the first time that OpenAI has had to shut down an account from a foreign
01:23adversary, it does appear to be the first time that OpenAI had removed an account that wasfocused on disinformation around the U.S. election. Axios writes that OpenAI actually identified,removed, and banned an unspecified number of chat GPT accounts.this week that were being used to create content about the U.S. presidential elections.OpenAI writes, this week, we identified and took down a cluster of chat chip ETT accounts that weregenerating content for a covert Iranian influence operation identified as Storm 2035. We have bannedthese accounts from our services and we continue to monitor any further attempts to violate our policies.The operation used chat chipt to generate content focused on a number of topics, including
01:58commentary on candidates on both sides of the U.S. presidential election, which it then shared viasocial media accounts and website. That said, OpenAI said that the operation didn't appear to haveachieved meaningful audience engagement, and that the majority of social media posts that they identifiedreceived few or no likes, shares, or comments. They referenced something called the Brookingsbreakout scale, which assesses the impact of covert intelligence operations on a scale from one,which is the lowest to six, which is the highest, and considered this to be the low end of categorytwo, which meant activity on multiple platforms but no evidence that real people picked up orwidely shared their content. Storm 2035 was using Chatsyby,
02:32both to generate long-form articles as well as to generate shorter social media comments.The content that was created was published to both progressive and conservative news outlets,and the social media accounts that were created posed as both progressives and conservatives.And interestingly, OpenAI says,they interspersed their political content with comments about fashion and beauty,possibly to appear more authentic or in an attempt to build a following.Said Ben Nemo, principal investigator from OpenAI's intelligence and investigations team,we all need to stay alert but stay calm.
03:00There's a big difference between an influence operation postingonline and actually becoming influential by reaching an audience. Meanwhile, that wasn't the story that wasgetting a lot of attention when it comes to AI and the elections. Former President Donald Trumptook to his Truth Social Network and shared several screenshots of ex-posts showing women wearingSwifties for Trump T-shirts. Another screenshot was Taylor Swift, created to look like Uncle Sam,with the message Taylor wants you to vote for Donald Trump. Trump himself captioned the post Iaccept. And there have been approximately 100,000 different media outlets writing about this.Now, to me, this actually brings up a question from Professor Ethan Malick, who wrote
03:36the first predictable bad effect of AI, easy deepfakes, is already here thanks to open flux,and there has been remarkably little wide outcry, either in the press or among policymakers.I wonder if it hasn't sunk in yet or if it ends up being less disruptive than anticipated.He continues, it looks like we're all going to just live through the end of reliable imagesand see how it works out.The interesting question to me, which we'll just have to wait and see, is what actual impactthings like Trump sharing those AI photos actually has.In that case, it strikes me that they're pretty obviously not real
04:06and that it doesn't seem like there was an actual attempt here to convince anyone that they were.Does that have the impact of us just sort of assuming things are AI,so to not take anything too seriously?Or does it lull us into a false sense of security,where when a politician shares an image that's actually been created by AIand attempts to convince us it's real, we actually still take it seriously?It's a pretty significant and fascinating sociological questionthat I'm not sure anyone's going to know until it actually happens.
04:32Meanwhile, over an AI on Wall Street, AMD is making a $4.9 billion AI acquisitionin its attempt to catch up to Nvidia.The company is planning to buy ZT systems to bolster its ability to provide AI infrastructure.According to a release, AMD says the deal will bring about, quote,the next major step in AMD's AI strategy to deliver leadership AI training and inferencingsolutions based on innovating across silicon software and systems.writes MarketWatch, the company will be tapping into ZT's systems expertise in both the design andoptimization of cloud computing offerings.
05:04Said Mizuho analyst, the deal validates Nvidia's integrated strategy of self-contained full-racksystem hardware designs, and the deal could make investors, quote, a wee bit more confidentabout AMD's ability to hit Wall Street's target for $9 to $10 billion in AIGPU revenue next year.Meanwhile, the information is arguing that another AI-related stock in Arm is priced too highly.They write,Since chip design firm Arm Holdings went public last September, excitement around generative AI hassent its stock soaring to a high last month of $188 more than triple its IPO price.While the stock has since fallen back a bit, investors are still valuing Arm at a big premium
05:38to Nvidia both on a multiple of future revenue and profits.The valuation gulf makes little sense.By way of example, they say that AI is a much smaller part of Arm's business than of Nvidia's.They compare Nvidia's 126% revenue increase as compotes to Arms 21%.Ultimately, this author suggests,Arm stock looks severely overvaluedtrading at 33 times estimated next 12-month salescompared to 23 times for Nvidia.
06:01They also point out that AMDis trading at just 8.5x.I share this only because I continue to thinkthat Wall Street is in a potential repricing momentwhen it comes to AI stocks,which, as we will discuss again a little bit in the main part of the episode,I believe has exactly nothing to dowith what AI means as a technology.
06:18Lastly today, yet another lawsuitaround copyright infringementwith a group of authors suing Anthropic this time,alleging that it committed, quote,large-scale theft in training clawed on pirated copies of copyright books.At some point soon here, we're going to have to start declaring a moratorium on covering news of new suitsin favor only of actual updates as these things work their way through the legal system.For now, though, that's going to do it for today's AI Daily Brief Headlines edition.
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07:22AID Daily Brief listeners receive a 20% discount on Venice Pro.Visit venice.aI slash NLW and enter the discount code NLW Daily Brief.That's NLW Daily Brief, all one word.Today's episode is brought to you by Super Intelligent, which is of course our platformthat helps you learn how to use AI tools and perhaps even more importantly, gives you ideason the best use cases that are actually going to help you achieve whatever it is you want toachieve. To recognize the end of summer and back to school slash back to work, we are running our bestpromotion ever when you sign up for Super Intelligent, between now and the end of August, using code
08:02so back, your first month will be 100% free. The platform features over 600 fun, highly practicalAI tutorials that get you using AI fast and with an eye to actually transforming how you get things done.We've just launched Super for Teams, so if you have a group of people at your company that want to figure outhow to use AI together, I highly suggest you check it out. But for those of you who are usingsuperintelligent as an individual, once again, if you sign up for Superintelligent between now andthe end of the month using code so back, you will get your first month 100% free. Go toB-supert.a.i and check it out today. You have heard it endlessly throughout this summer.AI is losing its hype. It's losing its luster. I have also hopefully explained a number of times
08:48why I think there is a massive, massive difference between the way that Wall Street prices AIcompanies and artificial intelligence in general. Seemingly, no journalists are able to make thisdistinction, but here we are. Today, though, instead of ragging on all of that, I'm going to focus onwhat might be coming next, because there's no doubt that we are in a transitional moment in termsof how people think about AI adoption. Particularly in firms who have quietly been experimenting thiswhole time, there is, I think, a sense of a shift, or at least a desire for a shift. And interestingly,McKinsey really captured this in a recent survey. This came out on August 7th, and the blog postsumming it up, they called Gen AI's Next Infliction Point, from employee experimentation to
09:31organizational transformation. This informs a huge amount about how we're thinking about AIat Superintelligent, and so today we're going to dig into some of these numbers and what I thinkthe implications are. To set this up, I want to come back to a study which I frequently referencehere on the show, which is the 2024 Work Trend Index from Microsoft and LinkedIn.This came from a survey of around 31,000 people across 31 countries.One of the really notable parts of this survey was the speed at which employees are adoptingAI even though their bosses are getting hung up.The study found that 75% of knowledge workers were using AI, and among those, 46% had started
10:05in the last six months.While, meanwhile, 79% of leaders thought that AI was critical to remain competitive, but59% were worried about quantifying productivity, and 60% said that their company lacked a vision and a planto implement AI. The net result was that of those 75% who are using AI, 78% were just doing it on theirown, not telling their bosses about it, bringing their own personal uses of AI to the office.The net impact of this is that right now, all of the benefits of AI are accruing to individualemployees, not to companies. In other words, when AI is invisible, it's great if employees aredoing things much faster than they might otherwise. But unless they are proactively deciding to use
10:45that extra time to move farther or faster with their work for the company, the company isn'tnecessarily realizing any of that benefit. This has created a prerogative for enterprises to figure outhow to scale individual employee benefit from AI to broad organizational benefit. And so thissets up for this McKinsey survey. The kickoff line is, as many employees adopt generative AI at work,companies struggle to follow suit, to capture value fromcurrent momentum, businesses must transform their processes, structures, and approach to talent.So as you can see, sounds familiar. Now again, especially because we've got all these sort ofarticles like this one from the economist, artificial intelligence is losing hype, it's important
11:22to counteract this with the results of these surveys. After nearly two years of debate, McKinseywrites, the verdict is in. Gen A.I is here to stay and its business potential is massive.But, as I said, employees are far ahead of their organizations in using Gen AI, and companies have beenslow to adopt in ways that could realize Gen AIs trillion-dollar opportunity.So let's talk about some of the study's findings.First of all, this study found even more usage than that Microsoft and LinkedIn survey.This had 91% of employees using generative AI split between 21% being heavy users and70% being light users.
11:55All of those folks, as well as the non-users, anticipate that generative AI will positivelyimpact their work experience.Of those who are using it the most, 98% say they believe it will positively impact their workexperience, among light users 91%, and even among non-users, 80% believe that GenAI willpositively affect their work experience. In terms of what they think will be improved,communication, creativity, critical thinking, and decision-making, and ability to collaborateall score highly. To take the light users as a benchmark, 81% of light users think thatGen AI will improve their communication, and 75% think it will improve their creativity.
12:29The biggest gap between light users and heavy users was in attitudes around critical thinkingand decision-making and the ability to collaborate.65% of light users thought that Gen.A.I.would positively impact their ability to collaborate, as opposed to 84% of heavy users.84% of heavy users also thought that it would impact their ability to think critically andmake decisions, while 67% of light users thought the same.In terms of this idea that organizations are lagging behind, McKinsey points out that only13% of respondents' companies have implemented multiple use cases.
12:59Not surprisingly, the organizations that had implemented two or more use cases tended to have a higherconcentration of those heavy users. Organizations that had multiple use cases, a group that McKinsey calledearly adopters, saw 49% of their employees as light users and 43% of their employees as heavy users.They write, the CIO of a global heavy industry company sees these trends at his own organization.Employees are experimenting with Gen.A.I. through publicly available and embedded tools,which is increasing curiosity and encouraging greater openness to experimentation. Yet he notes thatthere's no easy-to-proof business case for employee-driven adoption and the piecemeal implementationof use cases. And that leads McKinsey to what they believe is the next inflection point,
13:38moving from individual experimentation to strategic value capture. So what are their suggestionsfor how organizations can make this transition? They argue that there are three key steps.The first is reinvent domains by translating vision into value. The second is reimaginedtalent and skilling by putting people at the center. And the third is reinforce changesthrough formal and informal mechanisms that ensure continuous adaptation.So basically, the word salad of reinvent domains by translating vision into valueis that companies should take a domain-based approach.Basically let units like product development, marketing, and customer service think holistically
14:13about solutions and implementations and new workflows that work for their domain.Effectively thinking about Gen.A.I. adoption at only the individual level is too small,but at the cross-organization level might be too big, whereas within a specific domain ordepartment, that might be a better starting point.Now, when it comes to the second bucket, putting people at the center, the companies that are moreadept at using AI right now, what McKinsey calls early adopters, also, quote, prioritize talent and thehuman side of Gen AI more than other companies. Two-thirds of them have a clear view of their talentgaps and strategy to close them compared to just 25% of the organizations that are just experimenting
14:47with AI. McKinsey also writes that these early adopter firms focus heavily on upskilling andreskilling as a critical part of their talent strategies, as quote, hiring alone isn't enough to closegaps and outsourcing can hinder strategic skills development. This is really, really important.Yes, new hiring processes are absolutely going to prioritize AI skills as part of it, but you're goingto have to work with your existing workforce as well. Lastly, and this one is really interesting,40% of early adopter organizations provide extensive support to encourage employee adoption.In other words, these firms are encouraging people to not keep their AI usage secret and insteadshare with their learning. This is pretty much central to the way that superintelligent approaches
15:25unlocking organizational AI value. While nominally, we are a platform where people can learn how touse AI tools, in point of fact, the much more valuable aspect of it is getting people to share whatthey're using AI for. And that happens both between and within organizations. Our Super for Teams product,for example, is basically entirely focused on getting people to share with their colleaguesthe high-value AI use cases they're finding that could actually be driving value inside theirorganizations. Now, this last idea that you have to reinforce the changes to continue transformingis on the one hand obvious, but also even more important in AI than basically any other domain.The speed with which the technology is changing effectively demands that organizations need to build
16:05infrastructure that assumes change. McKinsey suggests that governance is a right part of this,and that, quote, a centralized model with a Gen AI dedicated center of excellence helps alignAI vision with execution. A second part, they say, is treating the changes like a true transformation.That means defining its infrastructural roles and measurement criteria and ensuring accountabilitywithin business units. Ultimately, though, they say the big thing is mindset. For Gen. AI, that meansthat leaders should visibly adopt generative AI in their own ways of working, that organizationsshould communicate the reasons behind implementing Gen AI, that there need to be comprehensive andongoing training programs, and that companies should start to integrate AI goals into performance
16:42metrics and evaluation processes. So that is this study from the front lines. To reiterate why Ithink this is so important, is that as the headlines and the news outlets debate, AI hype or not,every organization in the world, every enterprise, every company, every small business is basicallygoing through this process that McKinsey is describing. Employees are experimenting and iterating withoutbeing told to do so, in fact, sometimes in defiance of what they've been told, and organizationsare now finally racing to catch up with them in order to actually translate the benefits thatthey see their employees getting to organization level and business level benefit as well.It is actually, despite these headlines, an incredibly exciting time when it comes to how
17:21this technology is finding its way into the workplace.And I hope you now have a better sense of that.That, however, is going to do it for today's AI Daily Brief.Until next time, peace.
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