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What podcasters are saying about Enterprise AI adoption & AI software
Summary
In the past 30 days, 63 speakers on 35 finance podcasts discussed Enterprise AI adoption & AI software in 95 views. Chorus has followed Enterprise AI adoption & AI software since September 22, 2026: 98 views from 64 speakers on 35 shows.
Who talks about it most
- Aaron Levie · 5
- Naveen Rao · 5
- Josh Brown · 4
- Ethan Mollick · 4
- Kai Wu · 3
- Robert Kaplan · 3
- Gabe Stengel · 3
- Ev Randle · 2
- Jack Altman · 2
- Steve Hou · 2
The latest views
Newest first. Every quote is checked against the episode's transcript.
Josh Brown · Ritholtz Wealth Management
Enterprises are rationing AI token spend and can swap providers, a real bear case for AI vendors.
Josh describes the 'real bear case' for AI vendors. Companies pushed AI on employees with little control, and token use spiralled: agents ran for days, and Uber blew through its annual token budget in the first quarter. Companies will now impose company-wide AI budgets and rationalise token use, which Josh calls running a business rather than experiments. Open-weight and free models mean there are essentially no switching costs. When quarterly results miss, Fortune 500 finance teams will trace the shortfall back to the money paid to Anthropic and look for a cheaper harness.
“Number one, there's gonna be some sort of budget on AI use company wide, and they're gonna have to make decisions like, okay, this department gets x dollars in budget for AI.”at 35:19
Prof G Markets: Josh Brown: If This Is A Bubble, It’s Pathetic ·
Jeetu Patel · Cisco
Enterprise AI is still very early for agents, though chatbot use is already at scale.
Patel separates two stages of enterprise use. Employees now use chatbots at scale, much as they use search engines. Agents are still in the very early days, largely because enterprises fear trust failures. He ties the slow agent adoption to that fear, which is the same trust problem he describes elsewhere in the interview.
“When you start thinking about agents, we are still in the very, very early days. And I think part of the reason for that is just people being fearful of trust.”at 5:04
Bloomberg Talks: Cisco Systems President & Chief Product Officer Jeetu Patel Talks AI Industry ·
Christine Lagarde · European Central Bank
AI investment supports euro area growth; firms' new-tech adoption could lift growth above expectations.
Over the medium term, the recovery in confidence and real incomes plus a robust labour market should support domestic demand. Investment in AI-related activities, along with government spending on infrastructure and defence, should continue to support growth. Growth could also surprise on the upside if euro area firms adopt new technologies faster, together with reforms to raise productivity and complete the single market. The ECB sees AI investment as one of the drivers of euro area growth and as a potential source of upside.
“Investment in AI related activities ... should also continue to support growth. ... the adoption of new technologies by Euro Area firms ... may drive up growth by more than expected.”at 3:32
ECB press conferences and Euro Matters (ECB podcast): President Lagarde presents the latest monetary policy decisions – 29 October 2026 ·
Brien Lundin · Jefferson Financial (Gold Newsletter / New Orleans Investment Conference)
AI shows great potential in mineral exploration but its value is still unproven.
Lundin agrees AI could raise the odds of finding deposits, but says it is still unproven. Earlier machine-learning efforts, including one backed by big tech with hundreds of millions of dollars, have not yet found a deposit. Newer efforts look more promising. He cites Kodiak Copper, which used AI on its data to generate targets, some of which the company's geologists had not identified, while others overlapped with geologists' targets.
“[AI] shows great potential. I think you're right there. It's still unproven.”at 28:48
Thoughtful Money: Gold & Silver Weakness Now Offers A "Generational Opportunity" | Brien Lundin ·
Kai Wu · Sparkline Capital
AI will become table stakes, so today's laggard firms will shrink toward zero over time.
Kai uses the classic S-curve model of technology diffusion. Infrastructure is built first, then early adopters, then the early and late majority, with laggards last. He argues that AI will become table stakes, as the internet and calculators did. Companies that lean into AI now will hire, build products and streamline operations to gain share. Laggards will either go out of business or be forced to adapt. He cites e-commerce, where many retailers failed while Walmart adapted. He expects laggards to decline toward zero, though he says this may take a long time. He describes the current period as one of wide dispersion between adopters and laggards.
“in ten years, there will be no AI laggards. In ten years or twenty years, at some point, we'll all be using AI. In the same way we all use the Internet today, we all use the calculator.”at 9:41
Excess Returns: The S&P 500 Is Just 50 Stocks. The Nasdaq Is a 67% AI Bet. Kai Wu on What Else Is Hiding in Your Funds ·
Kai Wu · Sparkline Capital
AI adopters trade at no premium to laggards, and leadership should shift to adopters in the next phase.
Kai's data shows infrastructure firms have done well while early adopters and laggards have gone roughly flat. Early adopters trade at parity with laggards, so investors pay no premium for AI adoption. He draws on the history of technology cycles: after the infrastructure build-out, leadership usually transitions to the adoption phase. He argues that if AI generates ROI for users, early adopters should benefit through multiple expansion and business improvements. That makes identifying them the key question. He thinks the market is currently in the adoption phase, though he qualifies that the transition has not yet happened. The argument rests on the historical pattern and the flat relative performance of adopters, and he doesn't put a timeframe on it.
“the infrastructure firms have done really well. And the laggards and early adopters are basically just kind of sitting there... the valuations of early adopters are at parity with laggards... There's no premium for those guys.”at 20:35
Excess Returns: The S&P 500 Is Just 50 Stocks. The Nasdaq Is a 67% AI Bet. Kai Wu on What Else Is Hiding in Your Funds ·
Kai Wu · Sparkline Capital
Early AI adopters across sectors and markets offer long-term AI upside without infrastructure's capital intensity.
Kai argues that if AI is truly transformative, the real risk is owning companies that early adopters put out of business. Holding early adopters lets investors capture the long-term upside of AI while avoiding the high valuations and capital intensity of infrastructure stocks. He finds these adopters outside US tech, in German and Japanese companies, small caps, industrials and pharmaceuticals, so the thesis diversifies across markets and industries too.
“Being put out of business by the early adopters. So I do think of early adopters as being kind of... threading the needle... where you can get exposure to the long term upside of AI while... not having the same exposure to high valuations or the capital intensity of these... infrastructure stocks.”at 40:16
Excess Returns: The S&P 500 Is Just 50 Stocks. The Nasdaq Is a 67% AI Bet. Kai Wu on What Else Is Hiding in Your Funds ·
Jack Altman · Benchmark
Token buyers are not yet proven more productive in all cases; broad enterprise diffusion still has a long way to go.
Token sellers are clearly doing well, but Jack doubts buyers are proving much more productive across the board. Coding was unproductive a year ago and now looks productive, but much of the economy has not yet adopted AI. He says there is still a long way to go before ordinary businesses can buy AI productively. He still believes it will get there, so this is a cautious view about the pace of diffusion rather than a rejection of it.
“I don't think that in all cases, the buyers of tokens are proving to be that much more productive in all cases.”at 9:04
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
Jack Altman · Benchmark
AI coding adoption is near saturation among likely users, so 10x growth will fade; the runway is elsewhere in the economy.
Jack compares AI coding to Zoom during the pandemic. Everyone who wanted the product got it in a burst, growth was extreme, and then it saturated. Anyone in the Benchmark world who might use AI programming already does, so from here growth comes from market-share shifts, not 10x years. The larger opportunity is in the many places in the economy where adoption is far behind, so coding growth should slow even as AI broadens.
“everybody who might want to use AI programming has been using AI programming for a while... you don't get 10x year over year from that forever.”at 10:38
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
Ev Randle · Benchmark
AI code generation is the most impactful product-market-fit moment in tech history and could keep doubling yearly.
The breakout moment came when Opus 4.5 in November 2025 supercharged Claude Code. That created a wave of derivative companies (Supabase, Clickhouse, Vercel, Modal, E2B, Exa, Chainguard, Temporal) growing from 10 to 100 to 300. As code volume rises, derivative products must grow with it. Ev sees agent-run software factories spreading to the rest of the economy, so durable code-gen demand could keep doubling each year even if the 10x years end. He calls it on the order of the iPhone moment.
“It's probably the most impactful product market fit moment in the history of technology.”at 17:36
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
Ev Randle · Benchmark
AI companies buying tokens from labs is acceptable; they will become the large companies, as internet natives did.
Ev concedes that if only AI companies buy AI tokens, that is more leverage in the system, and that buyers in banks or health systems would be healthier. But his view is that AI-native companies are at the frontier and will become massive by out-competing firms that do not build software factories. He compares this to Stripe, which got big by serving DoorDash, Shopify and Instacart before old-economy firms adopted it. He says he has no problem with circularity.
“I don't really have any problem with AI companies buying AI tokens, like the circularity, because I'm like, look, these are companies.”at 27:06
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
Alexander 'Alty' Altmann · Barclays
Enterprise AI revenue will not close the gap within the time frame the market wants to see ROI.
Altman says enterprise AI revenue will probably get there eventually, but it cannot close the revenue gap on the timeline the market wants to see returns. Because of this, he argues AI needs to break into the consumer layer. He sees this as the main revenue question for the sector and ties it to his view that consumer adoption is the missing piece.
“To the point about revenues, like, enterprise revenues, I just don't think is gonna plug that gap.”at 31:52
Odd Lots: What Wall Street Really Thinks About the AI Trade Now ·
Steve Hou · Silicon Data
Companies actively adopting AI show faster growth and stronger profitability, per Ramp data, though the pace of payoff is uncertain.
Steve says the macro-level ROI from AI would show up as higher productivity, profitability and revenue growth. He says some evidence of this exists. Ramp data show companies actively adopting AI are growing faster and are more profitable. He concedes that these effects may take a while to play out, and the speed of the payoff is the open question.
“we do see some signs of that, right? Ramp has seen, has shown data, for example, on how companies that are actually actively adopting AI are also seeing faster growth and, you know, stronger profitability.”at 16:31
Monetary Matters: Why Compute Prices Are Still Rising | Steve Hou of Silicon Data on GPU Rental Indices, Fed Meeting, and Anthropic S-1 (LIVE) ·
Randy Cohen · Harvard Business School
LLMs make previously infeasible quant research ideas practical, which is game-changing for finance research.
Cohen points to Lauren Cohen's pre-generative-AI paper. It used machine learning to compare each company's 10-K risk factor section year over year. When the wording changed, the stock on average performed badly afterwards, since lawyers only change risk language when they see a serious risk. He argues that LLMs such as Claude now make this kind of research cheap enough to generate around 50 candidate ideas and select the six that hold up, reducing the hours of researcher time needed to check them. Quant funds are already using this internally, and a friend at a top quant fund now asks Claude for work that once required junior analysts. He calls it game-changing.
“it's obvious the kinds of things that you can now do that would have just been too hard back in the day. And it's super exciting... And it is absolutely game changing.”at 49:59
Alpha Exchange: Randy Cohen, Professor of Entrepreneurship & Finance Harvard Business School Co-Founder, PEO Partners ·
Dominic Konstam · Mizuho Americas
Business take-up of language models is just beginning; companies aren't spending much yet.
Based on the clients he interacts with and his own bank, take-up of language models is still only just beginning. Early use was mostly a substitute for search. He uses Claude a lot now, but the business side is only starting and customers are not yet spending much on it.
“the take up of these language models is really still only just beginning. ... So I think from the business side, this is thing is really just beginning. And we're not spending that much money on it yet in terms of a customer.”at 18:49
Ethan Mollick · Wharton School, University of Pennsylvania
Nearly all large companies report AI ROI; the question is now transformation, not whether gains exist.
Mollick cites his Wharton colleagues' survey of about 1,000 large companies, where 85% of firms with over $1 billion in revenue reported positive ROI from AI as of last year. He says CEOs he talks to are no longer asking whether AI pays off. Individual examples he cites include a Fortune 100 company using AI as a super-analyst for its senior team and PE funds seeing portfolio companies grow faster. He notes the returns are at the transformation stage and many firms are still in experimentation.
“There is almost nobody at this point in the data from my colleagues at Wharton back this up who isn't seeing some ROI from AI. Like, it's not... This is not an issue anymore that people aren't seeing ROI.”at 7:19
Big Technology Podcast: This AI Expert Argues We're Underestimating AI's Productivity Impact — With Ethan Mollick ·
Ethan Mollick · Wharton School, University of Pennsylvania
The MIT study showing 95% of AI pilots fail is a bad study; high pilot failure is healthy.
Mollick calls the MIT NANDA 95% pilot failure study a famously bad study, saying it was based on interviews at a conference that were coded by the author himself. He notes more reasonable reports put the failure rate closer to one in eight. He says he hopes most pilots fail because companies need to run experiments that fail to learn how to use AI, given there is no playbook. He discloses that he takes no money from the AI labs.
“That is a famously bad study. ... Somebody went out and just interviewed people at a conference and coded that himself.”at 13:33
Big Technology Podcast: This AI Expert Argues We're Underestimating AI's Productivity Impact — With Ethan Mollick ·
Ethan Mollick · Wharton School, University of Pennsylvania
AI agents became practical only around late 2025, when models reached the Opus 4.5/4.6 level.
Mollick argues the shift from the chatbot era to the agentic era depends on model capability crossing a threshold. Practical agents were not possible until models reached roughly the Opus 4.5 or 4.6 level around November or December of last year. This explains why product formats keep changing, since each capability step makes new product categories possible.
“Practical agents were not possible until we reached Opus four six level or four five level about November, December of last year. Like, that was when agents became practical.”at 27:15
Big Technology Podcast: This AI Expert Argues We're Underestimating AI's Productivity Impact — With Ethan Mollick ·
Ethan Mollick · Wharton School, University of Pennsylvania
Companies locking in AI vendors and solutions based on today's models will be left behind as models improve.
Mollick gives an example: someone who built an elaborate RAG system around a Llama 3 or Llama 4 model in 2024 saw it become obsolete quickly. He worries companies are choosing vendors and solutions based on current technology and will be blown away as capabilities keep rising. He argues firms need to be ambidextrous, exploiting current technology while exploring for the future.
“I worry a lot that companies are picking vendors or solutions based on today's technology, and then they are going to be blown away as the technology continues to increase.”at 1:06:09
Big Technology Podcast: This AI Expert Argues We're Underestimating AI's Productivity Impact — With Ethan Mollick ·
Robert Kaplan · Goldman Sachs
AI adoption is early; he is optimistic many use cases will work, with lessons coming over the next year.
Kaplan separates the AI build-out, which is already under way, from the adoption wave, which he describes as early. He says he is very optimistic about how many adoption use cases will work, but expects to learn a lot over the next year about which ones succeed.
“And then you've got the adoption wave, but we're early in the adoption wave. I'm very optimistic about how many of these adoption use cases are gonna work, but we're gonna learn a lot over the next year.”at 6:56
Bloomberg Talks: Goldman Sachs Vice Chair Robert Kaplan Talks Fed Meeting ·
Andrew McDermott · Mission Value Partners
Japan's machine learning in manufacturing process improvement is world-leading and economically useful.
He says Japan's real AI is machine learning applied to continuous improvement in manufacturing, an area where the Japanese are uncontested. He checked his two largest holdings and found they started discussing AI in 2015 and 2016, when this applied work began. He cites Anduril's drone project, which could only source fully non-Chinese parts from Japan. He contrasts this productive use with the speculative AI spending in the US.
“So I'm incredibly bullish on Japan's participation in the guts of this machine learning for the same reason that I was bullish on it fifteen years ago.”at 29:56
Top Traders Unplugged: GM109: Why Buffett Finally Went All In on Japan ft. Andrew McDermott ·
Jim Cramer · CNBC
AI chatbots speed up research and help organize problems, but they are not reliable for solving them.
Cramer says his show does not use AI in ways that could lead to a mistake. Bots are prone to errors, often do not use primary sources, and should not be treated as serious research tools. He still used ChatGPT and Claude to build a drug-pipeline comparison. The AI-assisted process took about three hours, against roughly three days by his usual method. He says he sticks with ChatGPT for helping organize a problem, but not for solving it. He still checked primary sources and analyst estimates himself, so the AI was a productivity aid, not a source of answers.
“Do I stick with ChatGeePity seven because of that? Yes. When it comes to help organizing a problem, but no when it comes to solving it.”at 46:53
Mad Money with Jim Cramer: Mad Money w/ Jim Cramer 10/27/26 ·
Tim Urbanowicz · Innovator Capital Management (Goldman Sachs Asset Management)
Agent deployment will reward picks-and-shovels such as security, identity and commerce providers.
Urbanowicz argues the market is entering a new phase of AI adoption, agent deployment. As in the first phase, when the picks-and-shovels suppliers were the biggest beneficiaries, he expects the next phase to reward infrastructure providers, though the beneficiaries will look different. He names security providers, identity and commerce as areas that will matter.
“We think this next phase of agent deployment is really going to be about those picks and shovels.”at 15:21
CNBC Closing Bell: Markets Brace for the Fed as Earnings Roll In 10/27/26 ·
David Solomon · Goldman Sachs
Falling AI costs make starting businesses cheaper, so small businesses need AI training to adopt the tools.
Solomon argues small businesses matter greatly to the economy. They are about 99% of US companies by count and drive 60% to 65% of annual US job creation. He says AI lowers the expense of building a business or platform, which gives more entrepreneurs the chance to start one. Those businesses still need coaching, education and experience deploying technology, even when small. To meet that need, Goldman committed $500 million to a Small Business AI Institute with leading AI labs. It extends a 17-year small business program that has served about 20,000 companies, adding AI training and more capital for participants.
“There's an expense to building a business, building a platform, setting it up. That expense is going down with this technology, so that gives more entrepreneurs the opportunity to start businesses.”at 12:28
Goldman Sachs Exchanges: Goldman Sachs Chairman and CEO David Solomon on AI: Implementation, Accountability, and Jobs ·
Robert Kaplan · Goldman Sachs
Companies have learned from AI use cases; more tangible benefits will show up over the next year.
Kaplan says that most companies started trying 15 to 20 AI use cases in earnest about a year ago. Some work and some don't, and firms are learning how to use and segment tokens. He concedes many companies say they have not yet seen tangible benefits. But his view, based on Goldman's experience with clients, is that more and more benefits will be realized over the next year as companies learn.
“And I think even though many companies say we haven't yet seen tangible benefits, I think actually, you're gonna see more and more benefits realized over the next year as companies learn, and that's been our experience.”at 28:18
Bloomberg Surveillance: Bloomberg Surveillance TV: October 27th, 2026 ·
Robert Kaplan · Goldman Sachs
Enterprise AI adoption is early; Kaplan is optimistic many use cases will work, learned over the next year.
Kaplan distinguishes the current AI push, which is narrow and about building data centers, power and chips, from the coming adoption wave. He says the adoption wave is early, and he is very optimistic about how many adoption use cases will work, though the economy will learn a lot over the next year.
“And then you've got the adoption wave, but we're early in the adoption wave. I'm very optimistic about how many of these adoption use cases are gonna work, but we're gonna learn a lot over the next year.”at 7:33
Bloomberg Surveillance: Previewing the Fed Meeting and AI IPOs ·
Lyn Alden · Lyn Alden Investment Strategy
Companies adopting AI more heavily win share and can grow headcount.
Alden cites many studies showing that companies adopting AI more heavily tend to win market share from peers that adopt less. She argues the zero-sum framing is wrong. AI can make each employee more productive, which lets firms run on better margins and pricing, take share, and grow headcount despite using more AI.
“there are tons of studies that show, for example, that just companies that are just higher adopters of AI, tend to be winning and taking market share versus ones in their field that are not, and ironically, to increase headcount.”at 15:53
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Willie Lee · Neostellar Capital
Coding agents drove AI's breakout; personal agents and cybersecurity are the next value pools.
Lee says Anthropic has shown the revenue potential of coding agents, which drove the breakout growth this year. He points to other areas where AI is unlocking large value, including personal agents, cybersecurity, and research. He says these are the next areas where companies are trying to work out AI's potential and how much value they can capture.
“I think the main piece that Anthropic has really shown is the potential for revenue opportunity within coding agents.”at 15:27
CNBC Fast Money: Anthropic’s long-awaited IPO filing... And next Fed decision on deck 10/26/26 ·
Matt Reed · Fidelity Investments
AI adoption in financial firms is still early, with efficiency gains and customer-facing uses just starting.
Financial firms use AI in two ways: to help customers use their data and products more effectively, and to gain efficiency that lets them add value faster. Matt says both are still early despite how long AI has been discussed. He looks for firms that can use AI to improve their product's value in ways that set them apart from peers.
“I would say in both of those cases, it's pretty early days still despite how long it feels like we've been talking about this.”at 15:13
Fidelity Viewpoints: 10.27.26 - Why a recession may be farther away than you think ·
Steve Weiss · Short Hills Capital Partners
AI usage is rising, notably in healthcare, sustaining the AI trade despite shallow-use reports.
Weiss says there are many conflicting reports, including Apollo's and Alphabet's, that describe AI use as shallow. That does not trouble him. He expects a major uptick in how AI is used, and says it is already happening predominantly in healthcare, where companies are using it. He thinks this usage sustains the AI trade.
“There are so many conflicting reports... They're saying, oh, it's so... You know, it's very shallow. That doesn't trouble me. You know, you're going to see a major uptick in how AI is used, and you're seeing it in health care predominantly. They're using it. So I think that sustains it.”at 24:50
CNBC Halftime Report: The Investment Committee Prepares for a Pivotal Week 10/26/26 ·
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