This Small Startup’s AI Video Model Just Put Sora 2 to Shame

The battle to win the burgeoning AI-generated-video market is heating up, thanks to a new model from a small but mighty player. Runway, a startup that develops AI models for video generation, has released its new flagship model, named Gen-4.5. The company said in a blog post this new model is a major step up for AI-generated video, especially when it comes to realistic physics and exact instruction following. The model claimed the top spot on independent benchmarking organization Artificial Analysis’s text-to-video leaderboard. Founded in 2018 by students of New York University’s Tisch School for the Arts, Runway has been laser-focused on AI video and has been steadily growing since releasing its first model in 2023. According to The Information, this strategy has paid off; the company hit $80 million in annualized recurring revenue in December 2024, and hopes to hit $300 million in ARR by the end of 2025. But Runway is going up against some of the biggest tech companies in the world, most notably Google and OpenAI, which have developed and commercialized their own AI video models. Runway’s plan to beat these mega-funded foes seems pretty simple: make better models. Runway wrote that Gen-4.5 represents “a new frontier for video generation.” Objects in Gen-4.5 videos “move with realistic weight, momentum, and force,” the company says, with better water and surface rendering. The company also says that details like hair will remain more consistent, and that the model will be able to generate more varied art styles. Altogether, Runway says, these upgrades enable the platform’s users to be much more exacting and detailed about their video generations. The new model is already being used commercially by enterprises, Runway says. Video game distributor Ubisoft, ad agency Wieden + Kennedy, Allstate Insurance, and Target were given early access to the tool. The model is available to paid subscribers and through Runway’s API. Gen-4.5 was both built on Nvidia GPUs and uses that company’s hardware to run, according to Runway. The company wrote that it “collaborated extensively” with Nvidia on the model’s creation. Runway creative principal Nicolas Neubert celebrated the model’s release on X, posting that “Gen-4.5 was built by a team that fits onto two school buses and decided to take on the largest companies in the world. We are David and we’ve brought one hell of a slingshot.” BY BEN SHERRY @BENLUCASSHERRY

Black Friday Broke Records. The Real Story Is How AI Changed the Way We Shop

If you only looked at the numbers, you’d think Black Friday was business as usual—just bigger. And, to be clear, it was definitely bigger. Adobe, which tracks more than a trillion retail site visits across 18 categories, says consumers spent a record $11.8 billion online yesterday, up 9.1 percent from last year and even above the company’s own forecast. Between 10 a.m. and 2 p.m., Adobe says shoppers spent $12.5 million every minute. By any metric, that’s a massive number of people shopping for deals. It’s a record for Black Friday sales online, but if you look a little closer, you realize it’s also a massive number of people shopping in very different ways than they used to. Black Friday has already changed quite a bit in the past few years. What was once a single day defined by incredible deals and lines outside big-box stores has stretched into a weeks-long digital shopping season. And, let’s be honest, people aren’t camping outside a Target anymore; they’re sitting on their couch, scrolling their phones. The AI holiday The most interesting part of the story is how things have shifted even more this year. Adobe’s data shows that AI-generated traffic to retail sites jumped 805 percent year-over-year. Not only are people using AI tools to find deals and compare products, but also shoppers who landed on a site from an AI assistant were 38 percent more likely to convert than everyone else. That’s surprising, and yet it makes perfect sense. One of the things AI chatbots like ChatGPT, Claude, and Gemini are good at is instantly surfacing the best price across half a dozen retailers. This year, there were plenty of headline features: Electronics, toys, apparel, TVs, and appliances were discounted between 24 and 30 percent. AI tools just made it easier to find them. And those deals didn’t just convince people to buy more. Adobe says that people spent more on higher-end items. The share of units sold from the most expensive tier of products spiked: 64 percent in electronics, 55 percent in sporting goods, 48 percent in appliances. With the right combination of discounts and AI-assisted shopping comparison, people weren’t just looking for deals—they were looking for the best value. Mobile continued to dominate Depending on the hour, around 55 percent of online Black Friday sales happened on a phone—$6.5 billion worth. That’s up 10 percent from last year and represents billions of dollars processed through screens smaller than a wallet. Mobile phones reward frictionless experiences. And it turns out, AI is very good at removing friction. When the easiest way to shop is to ask ChatGPT for a recommendation and the best deal, it changes the way retailers have to think about Black Friday. Not only that, but the timeline seems to have shifted. Adobe says one of the biggest spikes happened from 10 a.m. to 2 p.m. Shopping habits shifted toward the times when people are already using their phones. You don’t need to wait for a sale to “start” when an AI assistant can surface the best price the moment it exists. AI shopping is here to stay Adobe expects U.S. consumers to spend more than $250 billion online this holiday season, with Cyber Monday alone projected to hit $14.2 billion. But the part worth paying attention to isn’t the total—it’s how we got there. Shoppers are trusting AI to do the busywork and find them the best value. For a shopping event that used to be all about physical stores, that’s a significant shift that retailers have to pay attention to. The challenge is that they no longer control the narrative—the AI assistant does. The lesson here may not seem obvious, but the reality is that retailers need to redefine what loyalty means when more shoppers start their journey with an AI prompt instead of walking into a store or pulling up your website. When an assistant compares every retailer at once, being “top of mind” matters far less than being the lowest-friction, highest-confidence option in that moment. That means loyalty isn’t something you win with flashy ads or homepage banners—it’s something you earn through the operational details an AI actually cares about. Black Friday broke spending records. But the more interesting record is the one you might overlook: how many of those purchases started with someone typing a question into an AI instead of typing a URL into a browser. EXPERT OPINION BY JASON ATEN, TECH COLUMNIST @JASONATEN

Gen-Zers Are Using AI to Boost Their Side Hustles and Grow Them Into Full-Time Businesses

As more Gen-Zers embrace side hustles, they’re increasingly leaning on artificial intelligence to help them get ahead. A new survey by Canva finds that 80 percent of the people who have side hustles have used AI to fuel the growth of those enterprises, with 74 percent calling it their secret weapon. The tools, including ChatGPT and Canva’s own online graphic design offerings, are being used for everything from video creation and logo/brand design to data analysis and copywriting. And some of those side hustles are becoming full-time businesses. Side hustles, on the whole, have never been hotter. Data from the U.S. Bureau of Labor Statistics shows that 8.9 million Americans are currently working multiple jobs. That’s 5.4 percent of the country’s workforce. And a SurveyMonkey study published earlier this month found “72 percent of workers either already have or are considering a side gig—37 percent already have a side hustle, and 35 percent are considering pursuing one.” Some 22 percent of the people surveyed by Canva said they were inspired to start their own company after launching a side gig and 17 percent said the work led to a consulting or freelancing job. Additionally, 33 percent said they had gained new clients or customers, while 29 percent said their side gig had helped build their professional brand. Gen-Z was the generation most likely to start passive income side hustles: Of those with side hustles, 48 percent of Gen-Zers are currently earning passive income. All totaled, two-thirds of the 300 “5-9 influencers,” as Canva calls them, said they would consider quitting their full-time jobs if they believed their side projects could sustain them. They wouldn’t be the first. Some very familiar tech companies got their start as side hustles or side projects, including Groupon, Twitter, Craigslist, and Instagram (which began as Burbn, a location-based app for whiskey lovers). And thousands of other, smaller businesses began as a part-time side gig for the founder, eventually growing to multimillion-dollar businesses. Today’s side hustle community is made up of a mix of generations. Canva’s survey found that just under half of Gen-Zers, Millennials, Gen-Xers, and Baby Boomers were making money from side gigs today, with the actual percentages ranging from 40 to 48. Increasingly, the side hustles they’re choosing are digitally focused. The most popular jobs were social media creator (35 percent), e-commerce (27 percent), gaming and streaming (24 percent), and graphic design (14 percent). Extra income is the biggest motivator for people who have side gigs, Canva found, but it wasn’t the only one. Some 36 percent of the respondents said they were running their side hustle because they enjoyed the creative expression it gave them. And just under one-third said they wanted to turn a passion into a business. Even people with side hustles who aren’t looking to launch a business of their own are seeing advantages from the work. The skills they’ve learned as part of that work, including the AI expertise they’re building, are helping people advance. Some 14 percent of the people surveyed said their side hustle had helped them get a promotion at their day job. BY CHRIS MORRIS @MORRISATLARGE

The hottest new AI company is…Google?

Google just threw another twist in the fast-changing AI race. And its biggest competitors are taking notice. “We’re delighted by Google’s success — they’ve made great advances in AI and we continue to supply to Google,” Nvidia wrote in a November 25 post on X, before adding that “NVIDIA offers greater performance, versatility, and fungibility than ASICs,” (the application-specific integrated circuits) like those made by Google. “Congrats to Google on Gemini 3! Looks like a great model,” OpenAI CEO Sam Altman also wrote on X. The posts came just days after mounting buzz about Google’s Gemini 3 model — and the Google-made chips that help to power it. Salesforce CEO Marc Benioff wrote on X that he’s not going back to ChatGPT after trying Google’s new model. “The leap is insane — reasoning, speed, images, video… everything is sharper and faster. It feels like the world just changed, again,” he wrote. Now Meta is said to be in talks with Google about buying its Tensor chips, according to The Information, coming after Anthropic said in October that it plans to significantly expand its own use of Google’s technology. Shares of Google were up nearly 8% last week, while Nvidia’s were down a little over 2%. At stake is more than just bragging rights or a few sales contracts. As the tech industry claims AI will reshape the world — including investment portfolios belonging to everyone from billionaires to 401k-holding retirees — what company and what vision comes out on top could affect nearly every American. At face value, Nvidia’s post says the company isn’t worried about Google encroaching on its territory. And for good reason — Google’s chips are fundamentally different from Nvidia’s offerings, meaning they aren’t a match-for-match alternative. But that OpenAI and Nvidia felt the need to acknowledge Google at all is telling. “They’re in the lead for now, let’s call it, until somebody else comes up with the next model,” Angelo Zino, senior vice president and technology lead at CFRA, told CNN. Google and Meta did not immediately respond to a request for comment. Nvidia declined to comment. The leader for now Google is hardly an AI underdog. Along with ChatGPT, Gemini is one of the world’s most popular AI chatbots, and Google is one of the few cloud providers large enough to be known as a “hyperscaler,” a term for the handful of tech giants that rent out cloud-based computing resources to other companies on a large scale. Google services like Search and Translate have used AI as far back as the early 2000s. Even so, Google was largely caught flat-footed by OpenAI’s ChatGPT when it arrived in 2022. Google management reportedly issued a “code red” in December 2022 following ChatGPT’s seemingly overnight success, according to The New York Times. ChatGPT now has at least 800 million weekly active users, according to its maker, OpenAI, while Google’s Gemini app has 650 million monthly active users. But Gemini 3, which debuted on November 18, now sits at the top of benchmark leaderboards for tasks like text generation, image editing, image processing and turning text into images, putting it ahead of rivals like ChatGPT, xAI’s Grok and Anthropic’s Claude in those categories. Google said over one million users tried Gemini 3 in its first 24 hours through both the company’s AI coding program and the tools that allow digital services to connect to other apps. But people tend to use different AI models for different purposes, says Ben Barringer, the global head of technology research at investment firm Quilter Cheviot. For example, models from xAI and Perplexity are ranked higher than Gemini 3 search performance in benchmark tests. “It doesn’t necessarily mean (Google parent) Alphabet is going to be … the end-all when it comes to AI,” said Zino. “They’re just kind of another piece to this AI ecosystem that continues to get bigger.” More chip competition Google began making its Tensor chips long before the recent AI boom. But Nvidia still dominates in AI chips with the company reporting 62% year-over-year sales growth in the October quarter and profits up 65% compared to a year ago. That’s largely because Nvidia’s chips are powerful and can be used more broadly. Nvidia and its chief rival, AMD, specialize in chips known as graphics processing units, or GPUs, which can perform vast amounts of complex calculations quickly. Google’s Tensor chips are ASICs, or chips that are custom-made for specific purposes. While GPUs and Google’s chips can both be used for training and running AI models, ASICs are usually designed for “narrower workloads” than GPUs are designed for, Jacob Feldgoise, senior data research analyst at Georgetown’s Center for Security and Emerging Technology, told CNN in an email. Beyond the differences in the types of chips themselves, Nvidia provides full technology packages to be used in data centers that include not just GPUs, but other critical components like networking chips. It also offers a software platform that allows developers to tailor their code so that their apps can make better use of Nvidia’s chips, a key selling point for hooking in long-term customers. Even Google is an Nvidia client. “If you look at the magnitude of Nvidia’s offerings, nobody really can touch them,” said Ted Mortonson, technology desk sector strategist at Baird. Chips like Google’s won’t replace Nvidia anytime soon. But increased adoption of ASICs, combined with more competition from AMD, could suggest companies are looking to reduce their reliance on Nvidia. And Google won’t be the only AI chip competitor, said Barringer of Quilter Cheviot, and it’s doubtful it will achieve Nvidia’s dominance. “I think it’s a part of a balance,” he said. Analysis by Lisa Eadicicco

Demystifying Private GenAI solutions

Investments in the AI industry reached astronomical highs with the $300bn deal between OpenAI and Oracle. Competition reached the governmental level with the announcements of investments: US $500 bn, EU over $200 bn and China aims to reach $98 billion by the end of the year. On the technology side, GPT-5 was recently released. Unlike prior versions, it did not represent a paradigm shift but rather an incremental update with improved test results. This development deviates from the scaling-performance trend and echoes Yann LeCun's (MetaAI) statement, suggesting that artificial general intelligence (AGI) will not be achieved merely by scaling large language models (LLMs). Furthermore, Apple's latest research highlighted LLMs' limitations in mathematical reasoning. Another study by University College London acknowledges the LLM’s "scaling wall" issue, leading to a significant increase in computational costs for error correction. Therefore, the question remains: can LLMs innovate and acquire new skills as a "PhD in a Pocket"? Let’s take a moment to explore how current AI technologies can benefit project managers. While off-the-shelf generative AI solutions, which we discussed in the previous report, are quietly making their way into our office suites and smartphones, today, we will focus on private generative AI solutions. These solutions include not only data preparation and training, but also hosting infrastructure and development or customisation of the GenAI model. Gartner are sceptical about whether this way would be selected by the majority, and shortlists those who can choose it: ·Corporates; ·Software Product Development companies, including Startups; ·Niche businesses that haven’t found the right off-the-shelf solution and are willing to develop their own solution. Private GenAI solutions require strong expertise in software development, data, testing, hosting infrastructure, implementation, training and, as always, support. Benefits: Private GenAI enables exploration, innovation, and modification of nearly any use case in project management, resulting in solutions that can surpass off-the-shelf options. As we fine-tune the model itself, it also allows combining multiple models. And offers the next-level security with full control over its data infrastructure, data flows, and models. Trade-offs: Across different sources, the failure rate of GenAI PoCs in business is 80-90%, and it can reach a shocking 95% for solo implementations, according to recent research by MIT. So companies should be very selective and carefully evaluate the outcomes and future-proofing of their PoC’s use cases. Responsibility for ensuring compliance with the EU AI Act and GDPR regulations. If the organisation seeks a solution integrated with internet search or one that would work with multiple output modalities, it is essential to consider RAG and Agentic-AI solutions closely. By Denis Makarov, who is the IT Solutions Program Manager at Sanbra Group Ltd

Google’s New Gemini 3 AI Crushed OpenAI and Anthropic in a Benchmark Test for Business Operations

Google has released Gemini 3, the latest in its line of advanced AI models. As most AI companies do when announcing a new flagship model, Google boasted that Gemini 3 is its most intelligent model yet, and tops several benchmarks, including one that judges an AI’s ability to run a business. Google has also released a new application to supplement Gemini 3’s coding power. After months of teasing, Google CEO Sundar Pichai finally announced Gemini 3 in a blog post, saying that it enables anyone to “bring any idea to life.” The model is now integrated throughout much of Google’s ecosystem, including its search engine’s AI Mode, Google AI Studio, and the Gemini App. Pichai said that Gemini 3 is “much better at figuring out the context and intent behind your request, so you get what you need with less prompting.” Gemini 3 will be a family of models that vary in size and price. For now, the only model available is Gemini 3 Pro, which is the largest and most expensive version. Over time, smaller and cheaper versions of the model will be released. Gemini 3 Pro also includes a “Deep Think” mode, which has become standard across AI platforms. By activating this mode, Gemini can think even longer and harder about how to solve complex problems. Demis Hassabis, CEO of Google DeepMind, wrote that Gemini 3 is “the best model in the world for multimodal understanding and our most powerful agentic and vibe coding model yet, delivering richer visualizations and deeper interactivity.” By multimodal, he’s referring to the capability of AI models to process and generate content across a variety of mediums, including text, images, and video. Vibe coding refers to the practice of directing AI agents to write and execute code on your behalf, and has been a major AI topic in 2025. In its blog post, Google also claimed that Gemini 3 Pro is significantly less sycophantic than other AI models. “Its responses are smart, concise and direct, trading cliché and flattery for genuine insight,” the company wrote, “telling you what you need to hear, not just what you want to hear.” According to Google’s own testing, Gemini 3 Pro tops several widely-used AI benchmarks, including MMMU, which gauges multimodal understanding, and Terminal-Bench, which judges a model’s ability to code within a computer terminal. One notable leaderboard that Gemini 3 Pro topped was Vending-Bench 2, a benchmark that measures an AI model’s ability to run a business (in this case a vending machine) over a long period of time. After a full simulated year of operation, Gemini 3’s bank account balance was $5,478.16, much higher than second place finisher Claude Sonnet 4.5, which ended the virtual year with $3,838.74. Google clearly has high hopes for Gemini 3 in the coding domain. Along with the new model, the company has released Google Antigravity, a new agentic development platform that will likely compete with fast-growing startup Cursor, which sells its own AI-powered integrated development environment (IDE). Google Antigravity gives AI agents access to a code editor, terminal, and browser. In addition to Gemini 3, Google Antigravity users will also be able to select Anthropic’s Claude models and OpenAI’s open-weights model. Google says that Antigravity also comes “tightly coupled” with Nano Banana, the company’s popular image-editing model. For nontechnical founders who might be intimated by the technical details of Antigravity but want to try their hand at AI coding, Google has brought Gemini 3 Pro to Google AI Studio, a web-based application designed specifically for those without coding experience. In a blog post, Google AI Studio product lead Logan Kilpatrick wrote that Gemini 3 Pro “can translate a high-level idea into a fully interactive app with a single prompt. It handles the heavy lifting of multi-step planning and coding details delivering richer visuals and deeper interactivity, allowing you to focus on the creative vision.” Gemini 3 Pro is currently available for enterprise use for members of Google’s Gemini Enterprise platform. Google says that several businesses are already using Gemini 3 Pro, including Box, Cursor, Harvey, Replit, Thomson Reuters, and Shopify. Gemini 3 Pro costs $2 per million tokens on input prompts that are smaller than 200,000 tokens, and $12 for per million tokens generated. Tokens are units of data that are processed and generated by AI models. BY BEN SHERRY @BENLUCASSHERRY

10 AI Tools Marketers Are Using Right Now

Who doesn’t want to be more efficient? That’s why millions of Americans are turning to AI at the office. Over the past year, the share of U.S. workers using AI tools as part of their job doubled to 40 percent, according to a Gallup poll published in June. That number gets even higher for the marketing industry. By one measure, 61 percent of creative and marketing professionals use AI for their work, including analytics, content creation, strategy, and planning. Another poll found that 76 percent of marketers employ AI tools. For those marketers searching for the most effective AI tools to incorporate into their daily work flow, Inc. surveyed marketing-focused founders and their chief marketing officers. Here are the models and platforms they cannot live without. 1. ChatGPT Unsurprisingly, one of the most frequently cited tools was ChatGPT. Founders and chief marketing officers rely on the LLM for daily tasks, including brainstorming, market research, data analysis, strategy development, and content creation. One chief marketing officer trained a custom GPT to become chief of staff. Another says they use ChatGPT to formulate step-by-step guides when learning how to use other AI tools. With a team of fewer than 10 people, Sophie Mann, chief marketing officer of Furnished Finder, an online marketplace for furnished rental properties, uses ChatGPT as both her executive assistant and copywriter. Mann taps the LLM to help her draft board updates, structure meeting agendas, write performance reviews, and negotiate partner contracts. Without a full-time copywriter on staff, Mann also built a custom GPT, which is trained on Furnished Finder’s brand voice and customer personas, to write content. “It’s now the starting point for nearly every marketing asset. From email campaigns, social captions, blog posts, and paid ads, to event collateral and even voice-over scripts for our phone lines. You name it. We’re likely starting our drafts in Chat,” says Mann. “These tools help us scale our output without adding headcount. Reading through 1,000+ customer survey responses, for example, used to take hours. Now, I can have AI summarize key themes and insights in under a minute.” 2. Descript For creators and founders building in public through video, whether it’s TikTok or podcasts, Descript has become a go-to tool to make editing easier and faster. The AI-powered video editing platform, which landed on Fast Company’s list of Most Innovative Companies this year, creates transcripts from raw video and lets users edit through a text document by deleting words, phrases, or entire chunks. Last year, Descript added new AI capabilities, which automatically remove filler words, repeated words, bad takes, and background noise. Overall, the company claims its tool enables users to make “130 percent more videos in 27 percent less time” and “first-time users were 25 percent more likely to complete their project,” Fast Company reported earlier this year. 3. OpusClip Many of the founders who use Descript also use OpusClip, an AI-powered video editing software that helps users cut down longer videos, such as hour-long podcast interviews, into a series of shorter clips for social media. Within two years of launching, the company has scaled to more than 12 million users and become a favorite of social media managers. Shana Ayabe, founder of the marketing company Grace Digital Media and co-host of the podcast The Exit Interview, calls the tool a game changer. Opus Clip “allows us to quickly edit, reformat from landscape to vertical, and add dynamic captions while maintaining high production quality,” says Ayabe, who uses the paid version so her team can collaborate in real-time on the platform, “hearting” and commenting on different clips. “The built-in virality scoring system helps us understand why a clip is likely to perform well, so we can strategically schedule posts around traffic patterns and trends.” 4. Perplexity ChatGPT is not the only LLM that marketers use. In fact, most of the founders and CMOs who spoke with Inc. use multiple different models, depending on the task. Perplexity was the tool of choice when it came to research, including analyzing documents and data sets. Denise Aguilar, a global marketing strategist and founder of her eponymous Seattle-based company, Denise Aguilar Consulting, used the paid version of Perplexity and says the LLM has streamlined her workflow, allowing her to take on more ambitious projects for her clients. “The upgraded features, such as advanced file handling, faster processing, and priority support, have enabled me to work with large sets of PDFs and rapidly search, organize, and synthesize information,” says Aguilar, who has worked with companies, including Microsoft, Amazon, General Motors, and Vogue. “Investing in the Pro version has definitely raised my efficiency, especially when refining communications strategies, building personas, and editing across multiple marketing campaigns.” 5. Midjourney Midjourney, an AI-powered image generation tool, is being sued by a collection of visual artists and the major Hollywood studio Warner Bros. for copyright infringement, but marketers, especially those who work in the creative side of advertising, still say the tool is helpful for developing concept art and mock ups. To avoid any legal issues, be careful to restrict any images to internal and exploratory use only. 6. Lovable Marketers have joined the vibe-coding trend and started developing their own software by telling AI tools what they want to create, rather than writing code themselves. Many founders and CMOs prefer to use Lovable. The platform has become so popular that it became a unicorn within eight months of launching and has attracted nearly eight million users. Inc. AI reporter Ben Sherry used the free version to create an entire website in an hour. Marketers tend to opt for the paid version and say the tool is especially helpful for creating prototypes of client websites and apps. Maria Pergolino, chief marketing officer of SPS Commerce, a Minneapolis-based software company that helps retail partners optimize supply chain operations, says Lovable has been “transformative” for her work flow. “No longer do you need to awkwardly describe your vision for an app, ad, slide, or campaign. Instead you can describe your vision to an LLM and pop the directions into Lovable to bring your ideas to life,” says Pergolino. “This saves me hours every week.” 7. Claude If Perplexity has become the researcher and ChatGPT has become the catch-all for marketers, Claude has become the go-to LLM for writing. Founders and CMOs say the model excels as a place for brainstorming, storytelling, and testing out ideas or phrases. Patrick Finan, the co-founder and CEO of Block Club, a branding, strategy, and content agency for B2B technology companies based in Brooklyn, says Claude is the main LLM that he and his team use. “It’s fully integrated into Slack, Gmail, Google Workspace, Google Calendar,” he says. 8. Gamma For help making presentations, marketers have turned to Gamma as the PowerPoint of the AI era. The AI-powered platform takes text, such as documents or outlines, and transforms them into a slide presentation with one prompt. Using this same method, Gamma also lets users create polished-looking PDF documents, social media assets, and websites. Within two years of launching the San Francisco-based startup has attracted 50 million users, Fast Company reported earlier this year. Founders who spoke with Inc. recommended the paid version. 9. AirOps “Marketers are losing their minds” trying to optimize their existing SEO strategy for the new era of AI search, Andy Crestodina, co-founder and chief marketing officer of Orbit Media Studios, a Chicago-based digital agency that focuses on web development and website optimization, told Inc. recently. AirOps has helped streamline that process, founders and CMOs say. The company, which secured a $40 million Series B fundraising round earlier this month, calls itself the first content engineering platform for AI search. Marketers say the platform makes their AI optimization strategy more efficient. Leah Taylor, who runs communications for the AI sales platform Apollo, says Apollo has embedded the AirOps AI infrastructure into its core marketing operations to automate performance reporting and identify new revenue opportunities. “AirOps ingests our first-party data across notes, OKRs, experiment logs, and Slack, then uses enterprise LLMs to analyze and publish insights,” says Taylor. Since WebFlow, a no-code website experience platform, started using AirOps, the company increased its visibility on AI search with more than 330 new citations and a 24 percent uptick in SEO impressions, says chief marketing officer Dave Steer. AirOps has also increased revenue, turbocharging AI-attributed signups jumping from the low single digits to nearly 10 percent. 10. Gemini Marketers who are incorporating LLMs into their daily workflow also name-checked Gemini. While Doug Straton, chief marketing officer at Bazaarvoice, an Austin-based software platform that helps brands harness user-generated content, ratings, and reviews, calls ChatGPT the “easiest and most fun” LLM to use, he usually turns to Gemini instead for its repeatability and reliability. “I find Gemini, while harder to brief, creates more uniform, consistent results. It’s my company’s default,” says Straton, who uses the paid version. “It seems less eager to please you with a result you want to see, versus what you need to see.” BY ALI DONALDSON @ALICDONALDSON

What’s Next for AI? Andreessen Horowitz Founders Share Their Thoughts

Stocks of companies tied to artificial intelligence have been hitting stratospheric levels for over a year now, thrilling investors, but also causing concerns about a potential AI bubble. As startups close breathtaking funding rounds, like the $40 billion OpenAI collected in March of this year, fears of an AI bubble are growing — and some say a burst could be even bigger than the dot-com bubble of the late 1990s. The bubble theory is hotly debated. Some within the industry say they agree that the investment landscape is bloated, including OpenAI co-founded Sam Altman. Other experts, like Goldman Sachs, however, say we’re not in one (yet) — and Fed chair Jerome Powell has been skeptical of the bubble calls. As that debate rages, investors continue to fund AI startups. Few investors are in as deep as Marc Andreessen and Ben Horowitz. Their venture firm, Andreessen Horowitz (commonly called a16z), has sunk billions into the AI space. In April, it was reported the company was in early talks to raise a massive $20 billion AI-focused fund. The two investors recently came together at a16z’s Runtime conferences to talk about where AI can go beyond chatbots. Neither was willing to make any specific predictions about AI’s forthcoming capabilities, saying it’s too early to even imagine that. Andreessen likened AI to the personal computer in 1975, noting there was no way at that time to imagine what PCs would be capable of today. However, he expects similar levels of advancement — from a stronger starting point. AI, he said, is already approaching levels of human creativity — and while Andreessen would love to see humans continue to have superiority in that area, he thinks it’s unlikely. Tools like OpenAI’s Sora 2 video, for instance, are already capable of creating realistic scenes, animations, and special effects — and the introduction of AI actress Tilly Norwood has caused an outcry and prompted debate in Hollywood. “I wanna like hold out hope that there is still something special about human creativity,” he said. “And I certainly believe that, and I very much want to believe that. But, I don’t know. When I use these things, I’m like, wow, they seem to be awfully smart and awfully creative. So I’m pretty convinced that they’re gonna clear the bar.” Horowitz agreed, saying that while AI might not currently create at the same level as human artists, whether painters or hip-hop performers, that’s largely due to how little it has learned so far. It’s just a matter of time before it has an equal or superior level of talent. And some artists are already looking to use AI to collaborate, he said. “With the current state of the technology, kind of the pre-training doesn’t have quite the right data to get to what you really wanna see, but, you know, it’s pretty good,” he said. “Hip-hop guys are interested because it’s almost like a replay of what they did — they took other music and built new music out of it. AI is a fantastic creative tool. It way opens up the palette.” While AI can devour as many data sets as programmers throw at it, that doesn’t give the technology situational awareness. It is, in essence, book smarts versus street smarts. But the robotics field is expanding quickly. Elon Musk and Tesla are working on humanoid robots and Robotics company 1X has already started to take preorders for a $20,000 humanoid robot that will ‘live’ and work around your home. Once that technology and AI are blended, Andreessen said, AI will see a significant jump in actionable intelligence. “When we put AI in physical objects that move around the world, you’re gonna be able to get closer to having that integrated intellectual, physical experience,” he said. “Robots that are gonna be able to gather a lot more real-world data. And so, maybe you can start to actually think about synthesizing a more advanced model of cognition.” While there are plenty of experts who warn the AI market could be in a bubble right now, including OpenAI CEO and co-founder Sam Altman, Horowitz dismisses the idea, saying bubbles occur when supply outstrips demand — and that’s not the case with AI. “We don’t have a demand problem right now,” he said. “The idea that we’re going to have a demand problem five years from now, to me, seems quite absurd. Could there be weird bottlenecks that appear, like we don’t have enough cooling or something like that? Maybe. But, right now, if you look at demand and supply and what’s going on and multiples against growth, it doesn’t look like a bubble at all to me.” BY CHRIS MORRIS @MORRISATLARGE

What Adobe Knows About AI That Most Tech Companies Don’t

Last week, I was talking with a graphic designer about Adobe MAX, and they shared with me the most unexpected review of an AI feature I’ve ever heard. “Photoshop will rename your layers for you!” he said, without hesitating. The feature he was referring to was that Photoshop can now look at the content on each of your layers and rename them for you. Since most people don’t give a lot of thought to naming layers as they create them, this might be one of the most useful features Adobe has ever created. It’s certainly one of the most useful AI features that any company has come up with so far, mostly because it does something very helpful but that no one wants to do. Helpful over hype And, that’s the point. In fact, that reaction explains more about Adobe’s AI strategy than anything the company demoed during its keynote. It’s not the kind of feature that gets a lot of hype, but I don’t know anyone who regularly uses Photoshop who wouldn’t prefer to have AI handle one of the most universally hated chores in design: cleaning up a pile of unnamed layers. I think you can make the case that Adobe just made the loudest, clearest argument yet that AI isn’t a side feature. In many ways, it is the product now. Almost every announcement touched Firefly, assistants that operate the apps for you, “bring your own model” integrations, or Firefly Foundry—the infrastructure layer that lets enterprises build their own private models. What Adobe understands But beneath it all, Adobe is doing something most tech companies still aren’t. Instead of looking for ways to bolt AI onto its products, Adobe is building AI into the jobs customers already hired Adobe to help them do. When I sat down with Eric Snowden, Adobe’s SVP of Design, at WebSummit this past week, he used a phrase that stuck with me: “utilitarian AI.” Sure, there were plenty of shiny new AI features that Adobe announced like Firefly Image Model 5, AI music and speech generation, podcast editing features in Audition, and even partner models like Google’s Gemini and Topaz’s super-resolution built directly into the UI. But Snowden lit up talking about auto-culling in Lightroom. “You’re a wedding photographer. You shoot 1,000 photos; you have to get to the 10 you want to edit. I don’t think there’s anybody who loves that process,” he told me. Auto-culling uses AI to identify misfires, blinks, bad exposures, and the frames you might actually want. Ultilitarian AI is underrated That’s what he means by utilitarian AI—AI that makes the stuff you already have to do dramatically less painful. They force you into an “AI mode,” but instead save you time while you go about the tasks you already do. Snowden describes Photoshop’s assistant like a self-driving car: you can tell it where to go, but you can grab the wheel at any time—and the entire stack of non-destructive layers is still there. You’re not outsourcing your creative judgment—you’re outsourcing the tedious tasks so. you can work on the creative process.. That’s Adobe’s first insight–that AI should improve the actual job, not invent a new one. The second insight came out of a conversation we had about who AI helps most. I told Snowden I have a theory: AI is most useful right now to people who either already know how to do a thing, or don’t know how to use the steps but know what the result should be. For both of those people AI helps save them meaningful time. That’s how I use ChatGPT for research. I could do 30 Google searches for something, but ChatGPT will just do them all at the same time and give me a summary of the results. I know what the results should be, and I’m able to evaluate whether they are accurate. The same is true for people using Lightroom, Photoshop, or Premiere. You know what “right” looks like, so you know whether the tool got you closer or not. AI can do many of the tasks, but it’s still up to humans to have taste. AI has no taste Which is why Snowden didn’t hesitate: designers and creative pros are actually better positioned in an AI world—not worse. “You need to know what good looks like,” he told me. “You need to know what done looks like. You need to know why you’re making something.” Put the same AI tool in front of an engineer and a designer and, according to Snowden, “90 times out of 100, you can guess which is which,” even if both are typing prompts into the same tool. That means taste becomes the differentiator. Snowden told me he spent years as a professional retoucher. “I think about the hours I spent retouching photos, and I’m like, I would have liked to go outside,” he said. Being able to do that skill was important, but it wasn’t the work. The finished product was the work, and AI can compress everything between the idea and the result. Trust has never mattered more The third thing Adobe understands—and frankly, most companies haven’t even started wrestling with—is trust. I have, many times, said that trust is your most valuable asset. If you’re Adobe, you’ve built up that trust over decades with all kinds of creative professionals. There is a lot riding on whether these AI tools are useful or harmful to creatives, as well as to their audiences. So, Adobe didn’t just ship AI features; it is building guardrails around them. For example, the Content Authenticity Initiative will tag AI-edited or AI-generated content with verifiable metadata. Snowden’s framing is simple: “We’re not saying whether you should consume it or not. We just think you deserve to know how it was made so you can make an informed choice.” Then there’s the part most people never see—the structure that lets a company Adobe’s size move this fast. Understanding how customers want to use AI Snowden’s team actually uses the products they design. He edits photos in Lightroom outside of work. Adobe runs a sort of internal incubator where anyone can pitch new product ideas directly to a board. Two of the most important new tools—Firefly Boards and Project Graph—came out of that program. When AI arrived, Adobe already had the mechanism to act on it. It didn’t need to reinvent itself or reorganize. It just needed to point an existing innovation engine at a new set of problems. That’s the lesson here: Adobe isn’t chasing AI because it’s suddenly trendy with features no one is sure how anyone will use. It saw AI as a powerful way to improve the jobs its customers already do. That’s the thing so many tech companies still miss. AI is not a strategy. It’s not even the product. It’s a utility—one that works only if you know what your customers are trying to accomplish in the first place. So far, it seems like Adobe does. And that’s why its AI push feels less like a pivot and more like a product finally catching up to the way creative work actually happens. EXPERT OPINION BY JASON ATEN, TECH COLUMNIST @JASONATEN

How to Grow Your Social Following as a Founder—and Which Platforms to Use

So you want to build in public—documenting the process of founding, launching, and growing your business online—but you’re not sure which platform to use. You could use Substack or Beehiiv to send newsletters, Medium to write blog posts, TikTok or YouTube to post videos, LinkedIn, X, or Bluesky to share text-based posts, or Instagram to post photos. There’s no right answer. Founders of all kinds have grown their businesses by posting on each of these platforms—and many use more than one. Plus, there’s plenty of overlap: You can post TikTok-like videos on Instagram and share X-like text posts on Substack. Still, if you’re at the very beginning of your building in public journey, it’s a good idea to focus your efforts on just one. Here’s a guide to help you pick between some of the most popular platforms right now: Substack, Beehiiv, TikTok, LinkedIn, and X. Choose Substack if… You’re a founder in the politics, media, fashion, or beauty space who enjoys storytelling. Substack, which launched as a newsletter platform in 2017 but now bills itself as a subscription network, reports hosting more than 50 million active subscriptions and 5 million paid subscriptions. The platform recently added video and livestream features in order to court creators who use other paid subscription platforms, but the majority of its content is still long-form and text-based. If you’re considering building in public on Substack, you need to have a love for writing—or at the very least, storytelling. Newsletters on politics, fashion, and beauty seem to do especially well on Substack, which makes it a solid choice of platform if your company is in any of these industries. Many new-age media organizations including The Ankler and The Free Press publish on Substack, which means it’s also a great pick for media entrepreneurs and founders in adjacent industries like public relations. “Substack is where founders can reach audiences who genuinely value a direct, personal connection,” Christina Loff, the platform’s head of lifestyle partnerships tells Inc. over email. “The publications that perform best all share a common thread: a strong, human voice.” Examples of founders whose publications do this well, she adds, include Rebecca Minkoff, who has more than 6,000 subscribers; Dianna Cohen of Crown Affair, who has more than 13,000; and Rachelle Hruska MacPherson of GuestofaGuest.com and Lingua Franca, who has more than 260,000. Choose TikTok if… Your business is targeting Gen Z. It’s no secret that TikTok dominates in attracting young users—and keeping them engaged. The video sharing app rose to fame in 2020 and now has an estimated 170 million American users, many of whom are 28 years old and under. In fact, according to TikTok, 91 percent of Gen Z internet users “have discovered something” on the platform in the past month. So if you’re a young founder, or if you’re starting a business that’s targeting Gen Z customers, TikTok is probably your best bet. All you really need to get started on TikTok is a smartphone and basic video-editing skills. Nadya Okamoto, the co-founder of sustainable period care brand August, for one, has grown her audience to 4.4 million in just four years by filming her daily routine, answering product questions, and posting get-ready-with-me videos. Boutique candy brand Lil Sweet Treat’s founder Elly Ross has gained more than 36,300 followers by documenting her experience of opening four storefronts and launching a line of candy. Before you fully commit to building in public on TikTok, remember that there’s still a minute possibility that the platform will get banned in the U.S. on December 16. Choose LinkedIn if… You’re a founder in the business-to-business space. As a work-centric social media platform, LinkedIn is a great place for you to build in public if your company makes products for or provides services to other businesses. Still, there’s a lot of competition on the platform. More than 69 million companies and 243 million American professionals use LinkedIn, according to the company—and almost all of them are posting about their own careers. BY ANNABEL BURBA @ANNIEBURBA

Why Some AI Leaders Say Artificial General Intelligence Is Already Here

Artificial intelligence is still a relatively new technology, but one that has been seeing seemingly exponential jumps in its capabilities. The next big milestone many founders in the industry have discussed is artificial general intelligence (AGI), the ability for these machines to think at the same level as a human being. Now, some of AI’s biggest names say they believe we could already be at that point. The recent Financial Times Future of AI summit gathered Nvidia CEO Jensen Huang, Meta AI’s Yann LeCun, Canadian computer scientist Yoshua Bengio, World Labs founder Fei-Fei Li, Nvidia chief scientist Bill Dally, and Geoffrey Hinton (often referred to as the “Godfather of AI“) together to discuss the state of the technology. And some of those leaders in the field said they felt AI was already topping or close to topping human intelligence. “We are already there … and it doesn’t matter, because at this point it’s a bit of an academic question,” said Huang. “We have enough general intelligence to translate the technology into an enormous amount of society-useful applications in the coming years. We are doing it today.” Others said we may not even realize that it has happened. While most forecasts for the arrival of AGI still put it at several years down the road, LeCun said he didn’t expect it would be an event, like the release of ChatGPT. Instead, it’s something that will happen gradually over time—and some of it has already started. AI companies are generally less bullish on the subject of AGI than the panelists. OpenAI has said if it chooses to IPO in the future, that will help it work toward the AGI milestone. Elon Musk, last year, predicted AGI would be achieved by the end of 2025 (updating his previous prediction of 2029). Last month, he wrote in a social media post that the “probability of Grok 5 achieving AGI is now at 10 percent and rising.” Not all of the AI leaders said they felt AGI was here. Bengio, who was awarded the Turing Award in 2019 for achievements in AI, said it was certainly possible, but the technology wasn’t quite there yet. “I do not see any reason why, at some point, we wouldn’t be able to build machines that can do pretty much everything we can do,” said Bengio. “Of course, for now … it’s lacking, but there’s no conceptual reason you couldn’t.” AI, he continued, was a technology that had “a lot of possible futures,” however. And that makes it hard to forecast. Basing decisions today on where you think the technology will go is a bad strategy, he said. World Labs founder Li straddled the question, saying there were parts of AI that would supersede human intelligence and parts that would never be the same. “They’re built for different purposes,” she said. “How many of us can recognize 22,000 objects? How many humans can translate 100 languages? Airplanes fly, but they don’t fly like birds. … There is a profound place for human intelligence to always be critical in our human society.” Hinton, meanwhile, opted to look beyond AGI to superintelligence, an AI milestone where the technology is considerably smarter than humans. There are several startups exploring this space now, including Ilya Sutskever’s Safe Superintelligence and Mira Murati’s Thinking Machines Lab. “How long before if you have a debate with a machine, it will always win?” Hinton posited. “I think that is definitely coming within 20 years.” BY CHRIS MORRIS @MORRISATLARGE

AI Isn’t Replacing Jobs. AI Spending Is

For decades now, we have been told that artificial intelligence systems will soon replace human workers. Sixty years ago, for example, Herbert Simon, who received a Nobel Prize in economics and a Turing Award in computing, predicted that “machines will be capable, within 20 years, of doing any work a man can do.” More recently, we have Daniel Susskind’s 2020 award-winning book with the title that says it all: A World Without Work. Are these bleak predictions finally coming true? ChatGPT turns 3 years old this month, and many think large language models will finally deliver on the promise of AI replacing human workers. LLMs can be used to write emails and reports, summarize documents, and otherwise do many of the tasks that managers are supposed to do. Other forms of generative AI can create images and videos for advertising or code for software. From Amazon to General Motors to Booz Allen Hamilton, layoffs are being announced and blamed on AI. Amazon said it would cut 14,000 corporate jobs. United Parcel Service (UPS) said it had reduced its management workforce by about 14,000 positions over the past 22 months. And Target said it would cut 1,800 corporate roles. Some academic economists have also chimed in: The St. Louis Federal Reserve found a (weak) correlation between theoretical AI exposure and actual AI adoption in 12 occupational categories. Yet we remain skeptical of the claim that AI is responsible for these layoffs. A recent MIT Media Lab study found that 95% of generative AI pilot business projects were failing. Another survey by Atlassian concluded that 96% of businesses “have not seen dramatic improvements in organizational efficiency, innovation, or work quality.” Still another study found that 40% of the business people surveyed have received “AI slop” at work in the last month and that it takes nearly two hours, on average, to fix each instance of slop. In addition, they “no longer trust their AI-enabled peers, find them less creative, and find them less intelligent or capable.” If AI isn’t doing much, it’s unlikely to be responsible for the layoffs. Some have pointed to the rapid hiring in the tech sector during and after the pandemic when the U.S. Federal Reserve set interest rates near zero, reports the BBC’s Danielle Kaye. The resulting “hiring set these firms up for eventual workforce reductions, experts said—a dynamic separate from the generative AI boom over the last three years,” Kaye wrote. Others have pointed to fears that an impending recession may be starting due to higher tariffs, fewer foreign-worker visas, the government shutdown, a backlash against DEI and clean energy spending, ballooning federal government debt, and the presence of federal troops in U.S. cities. For layoffs in the tech sector, a likely culprit is the financial stress that companies are experiencing because of their huge spending on AI infrastructure. Companies that are spending a lot with no significant increases in revenue can try to sustain profitability by cutting costs. Amazon increased its total CapEx from $54 billion in 2023 to $84 billion in 2024, and an estimated $118 billion in 2025. Meta is securing a $27 billion credit line to fund its data centers. Oracle plans to borrow $25 billion annually over the next few years to fulfill its AI contracts. “We’re running out of simple ways to secure more funding, so cost-cutting will follow,” Pratik Ratadiya, head of product at AI startup Narravance, wrote on X. “I maintain that companies have overspent on LLMs before establishing a sustainable financial model for these expenses.” We’ve seen this act before. When companies are financially stressed, a relatively easy solution is to lay off workers and ask those who are not laid off to work harder and be thankful that they still have jobs. AI is just a convenient excuse for this cost-cutting. Last week, when Amazon slashed 14,000 corporate jobs and hinted that more cuts could be coming, a top executive noted the current generation of AI is “enabling companies to innovate much faster than ever before.” Shortly thereafter, another Amazon rep anonymously admitted to NBC News that “AI is not the reason behind the vast majority of reductions.” On an investor call, Amazon CEO Andy Jassy admitted that the layoffs were “not even really AI driven.” We have been following the slow growth in revenues for generative AI over the last few years, and the revenues are neither big enough to support the number of layoffs attributed to AI, nor to justify the capital expenditures on AI cloud infrastructure. Those expenditures may be approaching $1 trillion for 2025, while AI revenue—which would be used to pay for the use of AI infrastructure to run the software—will not exceed $30 billion this year. Are we to believe that such a small amount of revenue is driving economy-wide layoffs? Investors can’t decide whether to cheer or fear these investments. The revenue is minuscule for AI-platform companies like OpenAI that are buyers, but is magnificent for companies like Nvidia that are sellers. Nvidia’s market capitalization recently topped $5 trillion, while OpenAI admits that it will have $115 billion in cumulative losses by 2029. (Based on Sam Altman’s history of overly optimistic predictions, we suspect the losses will be even larger.) The lack of transparency doesn’t help. OpenAI, Anthropic, and other AI creators are not public companies that are required to release audited figures each quarter. And most Big Tech companies do not separate AI from other revenues. (Microsoft is the only one.) Thus, we are flying in the dark. Meanwhile, college graduates are having trouble finding jobs, and many young people are convinced by the end-of-work narrative that there is no point in preparing for jobs. Ironically, surrendering to this narrative makes them even less employable. The wild exaggerations from LLM promoters certainly help them raise funds for their quixotic quest for artificial general intelligence. But it brings us no closer to that goal, all while diverting valuable physical, financial, and human resources from more promising pursuits. By Gary N. Smith and Jeffrey Funk

A New AI Agent Wants to Schedule Your Life—Should You Let It?

Have you ever thought your working life would be easier with an executive assistant? A suite of new AI agents are cropping up, promising to take on the work and deliver all the benefits of having an EA without you actually having to hire anyone for the job. And, ostensibly, all for a far lower price tag. To find out if technology could do a better job than I could at making my schedule work for me, I tested out a free trial of Blockit, a new AI-powered agent that integrates with a user’s calendars and email. When signing up for the tool, Blockit promised me that in as little as five minutes it could learn the same amount of information about my schedule, habits, and preferences as a human EA might over the course of several months. Here’s how Blockit works: The AI agent learns your preferences for taking meetings, including when and where you like to conduct certain kinds of business. Then, you can copy the Blockit bot into emails or Slack messages with your contacts and give it instructions to set up a meeting at your chosen time and place. It sounded fantastically simple, but after using the tool, I realized that letting Blockit’s AI into my schedule required more than a little work on my part, too. Here are my three biggest takeaways from letting AI into my schedule for a week. You need to work to make it work for you Blockit’s onboarding process involves answering multiple questions about your habits and schedule, some of which got me thinking a little more about where, in fact, I like to work. So if you like to take certain meetings in a coffee shop near your office, you need to tell Blockit the exact address and the AI will make a note of it for future reference. Similarly, if you have an office or work from home on certain days, Blockit will log that, too. Doing this means that when you copy Blockit’s bot into an email with a contact that you want to get a coffee with, the bot will schedule a meeting at your preferred spot, invite the other person to it, and block off the time on your calendar that it will take you to get there from wherever you told it you would be working that day. That’s extremely helpful! But it also requires you to make some concrete decisions about where and when you will be working—and that’s not always totally obvious if you are in an industry that regularly puts you in many different locations on short notice. Blockit, to its credit, can keep up—it will even ask you to confirm if you are traveling if you tell it to set a meeting in an unfamiliar city. But if you are a busy CEO, keeping your AI agent up to date on your schedule might not always be top of mind. Another interesting Blockit feature is its codewords function. Users can teach the AI codewords that trigger certain actions: For example, say I sign off an email agreeing to a meeting with “best wishes” and copy Blockit to set something up. I could have already set “best wishes” as a codeword meaning that this meeting is not high priority, can be set sometime three or four weeks away, and can be canceled if I get another, higher priority request for the same time between now and then. It’s a clever idea, but again, I had to go through the work of teaching Blockit my codewords, a process that the desktop app doesn’t make particularly intuitive. Overall, I had to spend a solid chunk of time training Blockit—it definitely took more than five minutes of work to get value from this tool. If you’re already feeling stretched, taking those hours to invest in the AI might not be your top priority. But if you do, it may be worth it. Blockit needs access to everything An obstacle I ran into early with Blockit was that it didn’t want to work with just one Google calendar—it wanted access to every calendar app I had access to. That would be fine if the people who owned those other calendars were also Blockit users, which they were not. Blockit only works if you share all your calendar data with it, and if you are an entrepreneur or contractor who works regularly with other companies and are copied into their calendar, you likely don’t have the authority to give Blockit permission to see everything you can see. You might also have some personal privacy concerns that would prevent you from sharing certain information with Blockit. As a result, you might end up letting the app see only half the picture—which could make it less adept at sorting your schedule out for you. Another hurdle for the AI was the fact that I don’t schedule everything in my calendar. I don’t block time-off for certain kinds of work, or log when I’m taking free time. I also often block off a day in my calendar with reminders like “parents arriving today,” and it looks like I’m busy all day—but I’m not really. I tried to clean up my calendar and make it more faithful to what my days actually look like, but I gave up after spending an hour on planning out just two weeks into the future. In that sense, Blockit might be better suited to someone who is starting from scratch—say, joining a new company—or whose company has a calendar system that has become overwrought. Advantages of large-scale integration Blockit is supercharged when other people in your contacts list have Blockit too. Your AI agent can directly communicate with their AI agent and set a meeting up for you with minimal human engagement required. Unfortunately, none of my regular contacts have Blockit. The company behind it has put nothing into marketing it, so its customer base is word-of-mouth only. This brings me back to a realization I raised earlier: Blockit may work best on a company-wide scale rather than on an individual level. The app is genuinely helpful for individuals, but if it were integrated across a team or a company, I can see it taking on some of the core functions of a secretary or EA with little effort. (What the final pricing would be in my case, should I continue to use it past the free trial, is unclear.) That would also get over another potential hurdle with Blockit: Not everyone is used to having an AI agent ask them for their availability. If you’re trying to book a coffee date with your elderly relative, for example, or set up an intro call with a first-time contact, they might be a little skeptical. On a company-wide scale, however, Blockit may be just as intuitive as other AI-powered productivity tools, whether they be schedulers like Sunsama, Structured, or Todoist; note-takers like Fireflies.ai or Otter.ai; or management systems like Airtable or Jira. And, importantly, if your company invests in a tool like Blockit, it would likely become just as big a part of employee workflow as any other software-as-a-service product. BY CLAIRE CAMERON, FREELANCE WRITER

Small Businesses Aren’t Seeing the Same AI Gains as Big Corporations. Here’s Why

Companies of all sizes and sectors are moving swiftly to boost productivity by integrating artificial intelligence applications to automate tasks previously performed by employees. But recent reports clash significantly in calculating AI’s effects on humans, while also diverging on whether larger corporations seem to benefit from AI more than smaller businesses. AI should go in replacing humans in a given workplace, and how beneficial machines taking over from employees is to the results sought in using the tech. The first of those inquiries came from Wells Fargo chief equity strategist Ohsung Kwon, who compared changes in revenue generated per each worker on the staffs of big S&P 500 firms. He then made the same calculation for companies on the small-cap Russell 2000 index. Using the 2022 release of OpenAI’s ChatGPT AI bot as the starting point, Kwon’s team determined that the increased scaling abilities of larger corporations allowed them to benefit from the tech’s automating capabilities to boost the output—and with it, revenue—of workers they employed. During the same period, by contrast, it found productivity in the modest-size businesses fell. ″While productivity for the S&P 500 has soared 5.5 [percent] since ChatGPT, it’s down 12.3 [percent] for the Russell 2000,” Kwon wrote in a recent note to clients that was featured in a CNBC report on the differing results of AI adoption in business. “We see other examples of diverging trends in consumer, industrial, and financial markets.” But much like today’s big news that Amazon is laying off 14,000 corporate employees as it expands its use of AI across the business, Kwon’s measurement of productivity gains appears to depend mainly on human workers losing their jobs to the tech. Even if overall output remains the same or even dips following tech-driven head count reductions, the lower number of total workers—and payroll savings added back into the bottom line—mechanically boosts per employee claims of revenue generated. In addition to Amazon, the CNBC report lists big companies—including Meta, UPS, Starbucks, Oracle, Microsoft, and Google—that have announced big staff cuts this year. Those were undertaken to streamline their structures, but primarily to make way for use of AI to automate many of the tasks eliminated that employees previously performed. That willingness of big companies to sacrifice employees, cut labor costs, and boost revenue by scaling their use of AI appears to explain why they’ve benefited more from the tech than small-business owners under Kwon’s analysis. After all, even entrepreneurs responsive to investor demands for increasing returns tend to be more hesitant about laying off people they’re often working in close contact with than corporate managers. Any aversion to company founders cutting staff as an integral part of AI adoption may well also explain another of Kwon’s findings. While the S&P 500 rose 74 percent since use of AI took off in 2022, the Russell 2000 increased by just 39 percent—probably reflecting investor views about where the biggest, fastest potential boosts to share prices are. Still, none of that means smaller companies are holding back on introducing the tech to their workplaces or missing out on the productivity gains it can offer. A recent survey of small-business owners in the U.S., Australia, Canada, and the U.K. by Intuit QuickBooks Small Business Insights found nearly 70 percent of respondents used AI on a daily basis, with 75 percent reporting increased productivity as a result. Around 15 percent of participants said adoption of the tech had allowed them to create jobs, with only 5 percent saying they’d cut head counts instead. Results of a recent study by business consultancy Deloitte also measured successful adoption of AI in ways other than merely reducing head counts and costs. Its Humans x Machines report argues that both big corporations and small businesses that focus primarily on the tech rather than the employees who use it end up with disappointing results. Its survey found that nearly 60 percent of responding companies that deployed AI first and asked workplace questions about its use and effectiveness later are 1.6 times more likely to report lower return on their investment than other businesses. Companies with the best outcomes, the report said, are organizations that allowed human relations and other managers to work with staff to identify the most useful kinds of AI applications, train workers to adopt them, and then encourage continued deployment of those tools across the business. The report concluded that the tech will never meet its effectiveness potential unless business leaders prepare employees to enable that beforehand. “[M]ost organizations are investing heavily in AI, but not enough in the work design needed to unlock its value,” said Deloitte U.S. human capital head of research and chief futurist David Mallon in comments about the study. “This shouldn’t be an ‘either/or’ approach—it should be a ‘both/and’ strategy to maximize value. Organizations that take a technology-first approach struggle to scale, while those that intentionally design roles, workflows, and decision-making to integrate humans and machines are more likely to exceed their ROI expectations.” BY BRUCE CRUMLEY @BRUCEC_INC

This 1 Skill Is the Most Important for the AI Era, Say Leaders From LinkedIn, Meta, and Box

Artificial intelligence is already redefining the workplace. That was driven home this week by Amazon and Chegg, which both announced substantial layoffs. Amazon plans to cut 14,000 jobs as it invests more in AI, while Chegg said it was laying off 45 percent of its workforce as it confronts what it calls “the new realities of AI.” For workers and business owners, that’s a pair of warning shots highlighting the uncertainty and volatility of the years to come. Box CEO Aaron Levie, LinkedIn chief economic opportunity officer Aneesh Raman, and Clara Shih, Meta’s head of business AI, were recent guests at the Masters of Scale Summit in San Francisco to discuss the rise of AI and the changes it will bring. Entrepreneurs, said Shih, could be the people who are best suited to maximize AI’s productivity, thanks to their ability to pivot quickly and their near-obsessive tracking of what’s up and coming. Asked which skill will be most needed in the AI era, Shih said, “I think entrepreneurship, which is defined by pursuit of opportunity without regard to resource constraint, because the underlying substrate of our resources is continually shifting. And so we have to be constantly… on our toes, literally, just to pay attention to these trends and continually reinvent ourselves and our companies.” Raman expanded on that thinking, saying he believed curiosity would be the most valuable skill, while still namechecking the rest of the five Cs that represent critical soft skills in business leadership: curiosity, compassion, creativity, courage, and communication. “We all have to get better at all of those,” he said. “And then you have the sort of habits of resilience, adaptability, learn how to learn quick, learn how to fail fast.” While this week’s news of layoffs was discouraging, Levie said he’s optimistic about the long-term employment outlook as AI expands. Ultimately, he believes, AI will result in more hiring, since businesses can get a higher return on investment from each worker. He points to the evolution of the advertising agency as an analogy. In the 1980s, he said, it could take weeks to draw, print, and scale an ad. The rise of Photoshop slashed that turnaround time, though. Had people in the ’80s known what it could do, they would have feared massive layoffs in their field. The industry, of course, continues to thrive—and companies that previously couldn’t afford to advertise found themselves able to, thanks to lower costs. Levie says he suspects the impact of AI will be much the same, only on a broader scale. The technology, he says, will broaden the playing field, giving companies that don’t have the budget for many of the experts and tools that larger businesses do a chance to compete at the same level. “Each organization will look different, but… just imagine, let’s say, the small businesses that always have a structural disadvantage versus large companies because of their lack of access to talent and resources,” he said. “If you imagine them all being weaponized with the same expert lawyer and expert marketing and expert product development and software engineer as any kind of mid- or large-size company, what’s going to happen next is you’re going to have just a tremendous amount of growth of new organizations emerge with lots more productivity in a number of categories.” Additionally, he said, AI is still a work-in-progress. Despite the AI evangelists heralding the changes it will bring, it has few functional uses at the moment for many businesses. That gives owners and workers time to prepare for changes to come, learn complimentary skills, and become familiar with AI’s abilities—and how to maximize its potential. “The reality is that if you drop AI into today’s business process, it’s going to actually do very little,” Levie said. “It’s not world-changing. […] We thought AI would work how we do. It turns out it might be the case that we have to work how AI does, and we have to be actually in service of the agent to make it most productive.” BY CHRIS MORRIS @MORRISATLARGE

Technologists to AI Cheerleaders: Stop Being So Creepy

These days, AI is definitely near the peak of the hype cycle, when pronouncements about a new technology reach their most fevered pitch. But even given that reality, CEOs of AI companies and other assorted AI boosters have been saying a lot of creepy and extreme stuff lately. Some of these comments are just overenthusiastic salesmanship. Earlier this year, Anthropic CEO Dario Amodei, for example, predicted that AI would be writing 90 percent of all code in six months. That’s clearly not coming true. But other predictions are just scary. Amodei also warned that AI would eliminate 50 percent of white-collar jobs within five years. Fortune has a whole roundup of terrifying remarks from OpenAI boss Sam Altman. Sample comment: “Mitigating the risk of extinction from A.I. should be a global priority.” There are also the peculiar cases of AI diehards who see the technology in spiritual terms. One former Google AI engineer started an AI-worshipping church. Elon Musk is admittedly not the most reliable narrator, but according to him, Google co-founder Larry Page wants to create “basically digital god.” For everyday nontechnical people, these comments are creepy but also confusing. How much of this should we take seriously? A handful of fascinating recent blog posts and newsletters offer some reassuring answers. The vast majority of those in the trenches building AI view such comments as deeply unhelpful and inaccurate. Those who know it best wish everyone would talk about AI like a “normal technology.” What AI technologists think The first of these reports comes from seasoned entrepreneur and tech industry executive Anil Dash. On his blog recently, he highlighted the huge gap between how AI company bosses and influencers talk about AI and how those building the technology talk about it. Those with technical roles but lower public profiles share “an extraordinary degree of consistency in their feelings about AI,” claims Dash. He sums up their stance like this: “Technologies like LLMs have utility, but the absurd way they’ve been over-hyped, the fact they’re being forced on everyone, and the insistence on ignoring the many valid critiques about them make it very difficult to focus on legitimate uses where they might add value.” As evidence, Dash cites his conversations with his many friends and contacts within the industry. He also links to this lengthy paper by Princeton AI experts Arvind Naryanan and Sayash Kapoor. It argues we should treat AI as a normal, if revolutionary, technology like electricity or the internet, not some “potentially superintelligent entity.” Is not being an AI booster bad for your career? AI engineers know, it is possible to build AI that is not centralized in the hands of a few big companies, that treats the creators of content used to train these models fairly, and that isn’t terrible for the environment. They also understand that a reasonable public discussion about AI is necessary to achieve these aims. But many are afraid to speak out publicly, Dash claims. “Mid-level managers and individual workers who know this is the common-sense view on AI are concerned that simply saying that they think AI is a normal technology like any other, and should be subject to the same critiques and controls, and be viewed with the same skepticism and care, fear for their careers,” he writes. On her blog, programmer Gina Trapani seconds this view. She too says that a more reasonable discussion of AI can be bad for career advancement. Her most AI literate friends are also the people with the most sober view of AI’s potential and pitfalls. “The majority of people who work with and in technology hold a moderate view of AI, as any other normal technology with valid use cases and real problems that need to be fixed,” she writes. But, she continues, “tech people don’t talk about measured AI enough (probably because they want to keep their job).” Stop being creepy about AI! Both Trapani and Dash’s take home message is directed at those tasked with explaining AI to the general public. You can feel Dash virtually screaming through his keyboard at the Altmans and Amodeis of the world and their many imitators when he writes: “Stop being so goddamn creepy and weird about the technology! It’s just tech, everything doesn’t have to become some weird religion that you beat people over the head with, or gamble the entire stock market on.” “It’s creepy to tell people they’ll lose their jobs if they don’t use AI. It’s weird to assume AI critics hate progress and are resisting some inevitable future,” Trapani admonishes. But there is a takeaway here for everyday entrepreneurs too. If you worry that AI hype is badly overblown and discussions of the technology would be more helpful if everyone just calmed down, you are far from alone. The vast majority of AI engineers apparently agree with you. Hopefully, that will empower you to push back against overheated hype and have more level-headed conversations about AI with those in your professional circle. EXPERT OPINION BY JESSICA STILLMAN @ENTRYLEVELREBEL

Everyone Said AI Would Kill Google. Its First-Ever $100 Billion Quarter Just Proved Them Wrong

Every story about Google starts and ends with search. It makes sense—Google is a search engine. It’s not only the company’s most important and profitable business, but it’s the thing that has defined the internet for two decades. But for most of the past two years, the biggest story about Google has been that artificial intelligence would, inevitably, make search obsolete. People would stop “Googling” things because AI chatbots could just tell them the answers. Search—the company’s $200-billion-a-year cash cow—was supposed to be doomed. On the one hand, the idea that people would no longer type queries into Google’s search box and then click on the blue links that show up on results pages was a doomsday scenario. And AI chatbots certainly made that look increasingly likely. Then again, that story always assumed Google would sit still while the world around it changed. It assumed the company that practically invented the modern internet—or at least the way most of us experience it—wouldn’t figure out how to adapt. On Wednesday, Alphabet, Google’s parent company, reported its first-ever $100 billion quarter. Revenue rose 16 percent to $102.3 billion. Net income jumped 33 percent to $34.98 billion. Those are not the numbers of a company whose main business is being disrupted. It’s more like the numbers of a company that’s quietly figuring out how to change with the behavior of its users. Google Search and YouTube each grew at a double-digit pace. “Google Search & other” revenue climbed 15 percent to $56.6 billion. YouTube ads rose 15 percent to $10.3 billion. Combined, Google’s advertising machine brought in more than $74 billion for the quarter. Not only that, but its cloud business grew by 35 percent over the previous year. That leads to the most interesting part of this story, which is the part about how Google is spending all that money. As it announced its earnings, Google said it would raise its capital expenditures, specifically as it invests in infrastructure to serve its cloud businesses. That’s the part of the business that powers its AI ambitions. Google made more money than ever from search, and it’s spending that money on AI. Training and running massive models requires staggering amounts of computing power. But that’s exactly where Google’s advantage lies—it already owns what is probably the largest global computing infrastructure ever built. Now, it’s doubling down. Alphabet expects to spend $91 billion to $93 billion in capital expenditures this year—mostly on data centers, networking, and custom chips designed for AI workloads. That’s up sharply from last year and puts Google in the same spending league as Amazon and Microsoft. And even with those huge investments, Alphabet’s operating margin—excluding a $3.5 billion European Commission fine—rose to 33.9 percent. In other words, it’s spending tens of billions to expand AI capacity while remaining one of the most profitable companies on the planet. Google’s strategy isn’t just about protecting search ads. It’s about using the strength of that business to fund a transformation into something bigger: the dominant AI platform. That’s still a big lift. Yes, Google is a household name, but it’s still behind in AI—at least in terms of consumer mindshare. OpenAI’s ChatGPT is the front-runner in terms of customer adoption, but Google has almost every other advantage. It has the technology, the infrastructure, and a built-in user base that already trusts it as the default source of information. And because Google controls so many layers of the stack—hardware, data centers, models, and consumer products—it can absorb the cost of AI adoption in a way startups and rivals can’t. It doesn’t have to rent the future on someone else’s platform; it’s already building it. Now, Google is doing something very few companies have ever pulled off: funding its own disruption without losing momentum. Search and YouTube are still massive profit engines, generating the cash Google needs to build the infrastructure for AI. Basically, Google doesn’t really care whether you type your queries into a search box or a chatbot window, as long as you keep asking it your questions. For all the hype about AI replacing search, this quarter makes one thing clear: Google’s biggest business isn’t dying. It’s evolving into something that could be far more lucrative. If the company’s $93 billion AI spending spree pays off the way Pichai expects, Google might have just figured out a better end of the story than search. EXPERT OPINION BY JASON ATEN, TECH COLUMNIST @JASONATEN

AI ‘Consulting’ Services Can Help Smaller Businesses, but Risks Persist

Consultancy firms can be very useful for growing businesses, giving new companies guidance or financial or management advice when needed, backed by experience and expertise. But for smaller enterprises with narrow margins, the cost of hiring top-rank consulting firms can be financially out of reach. Enter AI, according to a new report on Business Insider. In much the same way that generative AI tools promise to add, say, coding expertise to a small team or free up workers from mundane tasks to engage in more productive work, AI-powered “consultant” apps are emerging from a suite of Silicon Valley startups, with the goal of helping small firms carry out market research, analyze data, or smooth and optimize their business operations. Business Insider quotes Thomson Nguyen, co-founder and managing partner of Wyoming-based venture capital outfit Saga, on the phenomenon. These new AI consultancy players won’t be challenging big consulting firms any time soon, he thinks, simply because if you’re a “Fortune 500 company building AI infrastructure for your call center, you’ll still hire the Big Four,” because you’ll have the budget set aside and experience in working with third-party consultancies. So the real target for these startups is smaller companies, making under $100 million a year, which are too small to hire a McKinsey or Deloitte. Business Insider notes that AI apps like PromptQL, from Bangalore-based AI unicorn Hasura, are directly set up to tackle typical consultant roles—including analyzing a company’s internal data, and continually adapting over time. PromptQL even has a team of engineers that’ll help craft an AI analyst agent specifically to meet the needs of a client company. Hasura co-founder and CEO Tanmai Gopal admitted to Business Insider that it’s “not as good as a McKinsey consultant,” but it has the benefits of being “instant.” That’s the very opposite of the sometimes protracted process during which a consultant learns about their client company before tackling an analysis, since an AI can just be switched on and immediately wrestle with data. Among the top tasks being assigned to AI consultancy startups are starting and managing call centers and customer service automation, integrating software and AI into the client company’s operations, and building management and operational AI systems. There are even AI tools targeting executive coaching. This may not be a surprise, considering that big tech names like Salesforce are already selling their own agent-based AI services aimed at automating the sales process and call center operations. AI startups offering similar options and targeting smaller companies as clients is natural. Gopal told Business Insider that, for now, these AI consultancy tools aren’t really replacing human workers—echoing many an AI evangelist’s arguments about the role of AI in the workplace. Human workers have more diverse skills, and for the moment such services depend as much on the “network” of colleagues that a human worker can access as on their advice. What’s the takeaway for your company? If you find yourself struggling with an expertise gap, you may find that there’s an AI-powered consulting tool out there that will fit your needs. But as with most AI tools, perhaps the thing to remember is that (just as with human consultants, though perhaps less obviously) AIs are not infallible. AI systems regularly make mistakes, and can hallucinate analysis and advice that they then pass off just as if it was real advice. You’ll have seen this by now, perhaps when you asked an AI to write a snippet of code for you. The AI may insist the code works, but when you say “No, it doesn’t,” the AI may say “Oh! You’re right!” and offer a fix. Whenever you’re using AI it’s best to run the results past a human worker before making a critical decision based on an AI consultant’s analysis. BY KIT EATON @KITEATON

Far From Silicon Valley, This Founder’s Data Center Business Is Building the Future of AI

There’s a common refrain among business strategists that it’s better to be a pickaxe salesman than a gold prospector—or, in other words, that the best way to capitalize on a gold rush is to sell tools to the people hoping to strike it rich, rather than trying to hit paydirt yourself. With artificial intelligence currently enjoying a boom of its own—one that some big names have even called a bubble—plenty of companies are stepping in to sell the AI equivalent of pickaxes, such as backend infrastructure and computational power. That includes big-name ventures such as CoreWeave, the AI cloud company that IPO’d earlier this year and counts Microsoft, IBM and OpenAI among its clientele. CoreWeave claimed the No. 45 spot on this year’s Inc. 5000 list of the fastest-growing private companies in America, and is one of the AI boom’s biggest winners so far. But beating it out on that same list was another AI infrastructure company, the Chicago-based Introl, which came in at No. 14 on the list just a few years after its founding. Introl founder and CEO Ryan Puckett launched the company in 2021 while between jobs and looking to work as a freelance project manager. Introl helps set up GPUs, or graphics processing units: the computer chips that train and run modern AI models. A former low-voltage cable technician, Puckett has since built the company up to impressive scale: it’s grown annual revenue nearly 10,000 percent over the last three years, and Puckett says domestic revenue was about $38 million last year. All of that growth is bootstrapped, the success of which the CEO attributes to “managing cashflow effectively and efficiently”—as well as, at least initially, “a lot of credit card debt.” And though Introl was born in Dallas, Puckett moved it to Chicago a few years in; he’d lived in the Windy City during his early 20s, and wanted to go back. “There’s not a better city in the country,” he says of Chicago. “There was no other thought in my mind to build it anywhere else.” Blake Crosley, Introl’s CTO, says the company has deployed “up to 100,000 GPU units in a data center.” Each one needs multiple connections, he adds, requiring lots and lots of fiber optic cable; the company says it has run more than 40,000 miles of the stuff in all. “We don’t actually own or operate the data centers,” Crosley explains. “We basically help design, like, what does it look like to actually get that set up in the space? Once the racks are in place, how are we going to actually connect everything together?” This work, he adds, is known as “rack and stack.” Installation is followed by testing and quality control. NDAs limit Introl’s ability to disclose specific client names, but the company says it has around 45 to 50 full-time employees, plus over 1,000 subcontractors. The startup deploys that workforce to data centers around the country and the planet. Those data centers are so big, Puckett says, that people get around them in golf carts and measure their footprints in terms of how many Costcos could fit inside. Speed to market is the CEO’s biggest challenge, he tells Inc. Companies will sometimes give Introl barely a week’s notice to get people on-site to a data center, he says, and it can sometimes be hard to find enough hotel space to house all those staffers—who will sometimes number in the hundreds for a job—especially in the small towns where many data centers go up. “In a lot of cases, because they are trying to get things online so quickly…certain specific sections of [the data centers] are being built while you’re in a different part,” Puckett says. “It’s a constant flow of trucks coming in, dropping off pallets of cables.” AI is big business right now, but if the fervor starts to die down, demand for the underlying hardware could follow suit too. “I’m not 100 percent sure what our pivot would be [if], say, GPU deployments just kind of fell off the face of the earth,” Puckett says, although Introl’s focus could shift toward maintenance. Right now, he estimates, 70 percent of the company’s work surrounds new installations, while the other 30 percent has to do with maintaining pre-existing sites. For now, though, the company is feeling good about where things are headed. “Obviously there’s a lot of talk about [an] AI bubble and stuff like that,” says Crosley, the CTO. “The players are huge, and the money that’s flowing is even bigger. But from a user perspective, on the side of utilizing AI, I can only see things expanding faster in the total adoption and usage.” BY BRIAN CONTRERAS @_B_CONTRERAS_

Here’s How LinkedIn Co-Founder Reid Hoffman Says AI Needs to Be Regulated

Regulation can be good for technology, so long as it’s done thoughtfully, according to LinkedIn co-founder, investor, and AI-enthusiast Reid Hoffman. Speaking on the heels of a pitch event in San Francisco called Entrepreneurs First Demo Day, he compared AI regulation to seatbelts in vehicles. “Seatbelts are a good thing, relative to the fact that regulatory stuff can have a positive impact on society, technology evolution. Now doing it smart in the right way is important,” he tells Inc. “You don’t try to solve everything before you get on the road. You get on the road and then solve it as you go,” he adds. His voice joins a chorus of others from big names in tech speaking up about how much—or in the case of legendary investor Marc Andreessen and companies like Meta—how little regulation they support. Hoffman sits on the board of Entrepreneurs First, an international talent investment firm that hosts incubator-style programs and related annual pitch competitions. Those events are called Demo Days, and the most recent took place in San Francisco on Wednesday. Hoffman joined EF’s board after leading a significant round of investment in the company in 2017 through his capacity at venture capital firm Greylock Partners. Hoffman was not on the ground at Demo Day this year, but another big name in tech was: Anthropic co-founder Jack Clark was the keynote speaker in conversation with Entrepreneurs First CEO Alice Bentinck. Just a few days prior, Clark had made waves for commentary he gave at The Curve conference in Berkeley, California, and later published in essay form in his newsletter. He compared AI to a “mysterious creature” of humanity’s own creation. He said he was optimistic about its potential as well as appropriately afraid of it, especially if AI’s goals are not absolutely aligned with humanity’s. And finally, he ended by emphasizing the need for conversations with a broad swathe of society to help craft a “policy solution.” “There will surely be some crisis,” Clark notes in his blog. “We must be ready to meet that moment both with policy ideas, and with a pre-existing transparency regime which has been built by listening and responding to people.” In response to the post, U.S. AI and crypto czar David Sacks accused Anthropic of fearmongering. Hoffman’s take, which he wrote about in his recent book, is by no means anti-regulation, but does differ somewhat from Clark’s. “In the book that I published in January, Superagency, part of what I was arguing for within AI is iterative deployment and development,” he tells Inc. “We do the regulatory thing, but we do it in response to what we can actually see versus imagination of what [could] happen,” he adds. AI has never been more topical, especially among aspiring entrepreneurs. This week at Demo Day in San Francisco, founders from 20 different startups pitched more than 200 tech investors, among them big name firms like a16z, Khosla Ventures, Paladin Capital, Insight Partners and Engine Ventures, in hopes of landing as much as $7 million in seed funding. It represented the culmination of some six months of work the founders had put in during Entrepreneurs First’s incubator-style program. On the lips of most of those entrepreneurs was AI. “The majority of the companies that were pitching yesterday—85 to 90 percent—are all using AI in some way. Some of them are building novel AI models, others are creating wrappers or scaffolding around existing AI models,” says Bentinck. “If you look at what early stage investors want to put capital behind, they see this enormous opportunity in the new AI economy.” Originally founded in London, Entrepreneurs First started off as a nonprofit in 2011 before becoming the investment vehicle it is today, starting in 2015. The company expanded overseas to offer programming in San Francisco at the start of 2024, and continues to run cohorts across Europe, India and the U.S. Entrepreneurs First functions something like an incubator, although Bentinck says EF thinks of itself more as a “talent investing studio.” It searches out individuals, usually with technical backgrounds, who also possess certain qualities related to pacing, productivity, determination, and even aggression, Bentinck says—qualities that alert EF that these individuals may outperform their peers. EF then guides them through the process of building a startup including helping them ideate if they don’t already have an idea and introducing them to potential co-founders. “We find exceptional individuals, pre-team, pre-idea, pre-company. Really all that we’re looking for is their entrepreneurial potential and then we run them through a process that helps them build a startup from scratch,” Bentinck says. The group that pitched this week included the top tier companies from EF’s European and U.S. programs. Each of these teams had been selected by EF and received $250,000 in pre-seed investment in exchange for 8 percent equity. “That’s the culmination of EF and we then send them off into the wild to build enormous companies,” Bentinck says. BY CHLOE AIELLO @CHLOBO_ILO

How AI Can Make You a Better Negotiator: A Step-by-Step Guide

Earlier this year, Jennifer Barnes received an email from a client in financial distress, asking to renegotiate their contract. As the founder and CEO of Optima Office, an outsourced HR and business services company based in San Diego, this was not the first time she’d received a message like this. She’s been in business since 2018, growing her 100‑employee company to around $18 million in revenue and earning a place on the Inc. 5000. There isn’t much she hasn’t seen. From experience, Barnes knew negotiating was going to burn the better part of an hour. First, she’d have to read the whole exchange. Then she’d think about how to respond. After that, she’d have to spend a lot of time writing and editing the response. She’d have to be diplomatic and keep her own emotions in check, she says: “Clients can be really unreasonable when they’re very low on funds.” This time, however, instead of working through it on her own, Barnes popped the email into her paid version of the AI chatbot Claude and asked it for a three‑point summary of the client’s demands. She then uploaded a brief synopsis of her perspective on the situation, did a light edit and hit send. Total time to craft the message that solved the problem? Five minutes. Negotiating is much of the work of growing a company. Whether it’s with clients, suppliers, investors, joint venture partners, contractors, or employees, as an entrepreneur it can feel like you’re constantly either preparing, actually doing, or managing the results of a negotiation. All of that is intellectually and emotionally demanding, says Emily DeJeu, professor at the Tepper Business School at Carnegie Mellon University, who teaches classes on negotiation. “Even in our textual exchanges, negotiation is happening as much with our guts as with our brains.” But with the advent of AI, using your own brain unassisted has become a bit passé. If sensitive, time‑consuming tasks like negotiating can be even partially off‑loaded without inadvertently blowing up your company, well, that’s pretty compelling. On the other hand, a recent MIT report found that 95 percent of companies are getting literally zero return on their generative AI investment. But it doesn’t have to be that way. By deploying today’s AI tools to prepare for negotiation—with a keen understanding of their current limits—you can leverage them to your advantage right now. In this Premium article you will learn: A step-by-step guide for incorporating AI into the negotiation process Best practices for combining human intuition with artificial intelligence How many hours a week you can expect to save by using AI When an hour takes five minutes For growing companies, there’s a wide array of AI tools from business-function-specific programs like Salesforce’s Einstein to generally available large language models like ChatGPT or Perplexity. All of these AI tools can analyze data and generate cogent text or other forms of output. Between the time savings and the added bonus of strictly controlling her tone, which might have had a sarcastic edge, Barnes says she quickly found herself relying on Claude for her frequent negotiation tasks: to research, prepare, and learn from prior wins. “It saves me about eight hours a week,” she says. “It’s like having another executive on the team.” Of course, that extra executive is sometimes fallible. “It makes mistakes. Don’t get me wrong,” she says. Generative AI systems are known to hallucinate, or produce inaccurate and even fabricated results. But AI seems just as confident when it’s wrong as when it’s right. That’s why Barnes says she would “absolutely not” allow an AI to send a message on her behalf without reviewing it first. When I asked Claude for a comment it agreed: “People getting real value are using me as a tool they control, not as a replacement for judgment. The moment anyone treats me as a peer negotiator rather than a research assistant, things get questionable.” Machine processing power versus human nuance Even if AI never made a mistake, there are serious questions about how well machines can accomplish the extremely human task of negotiating a complicated deal, says DeJeu, who is also hosting a conference on how businesses can use generative AI later this year. Artificial intelligence lacks the ability to read human emotion, which is often cited by the negotiators she’s trained as an advantage. DeJeu, however, disagrees. “Emotion has a distinct, powerful role to play in persuasion,” she argues. “Negotiation is one of the most human‑touch-necessary communication scenarios. It’s nothing but nuance.” Today’s tools, she says, aren’t capable of the key basic task of accurately reading a room. DeJeu acknowledges that not every negotiation is that deep. Not every supplier contract needs an analysis of subtle body language. And she certainly sees many benefits of using an AI tool to research and synthesize information ahead of a negotiation. “It can make you a little more nimble,” she says. DeJeu specifically finds using voice‑enabled AI for rehearsing negotiations to be beneficial. She suggests preparing 10 minutes for every 10 seconds of talking time in a negotiation. It’s hard to imagine a human critique partner enduring that without diminishing returns, but AI is tireless. Agents of the Future Of course, AI tools are rapidly evolving. Building on LLMs are the more eye‑catching AI‑powered agents—also known as agentic AI—which are capable of self‑direction. “AI agents act with autonomy and authority to find and negotiate deals with suppliers at scale,” explains Kaspar Korjus, CEO of Pactum AI. If technology continues its progress, AI agents will eventually handle every step of negotiation, from first contact to final contract and delivery. While going totally hands‑off is not an available option for most small to midsize companies today, corporate giants have been working on this for a while. For example, way back in 2021, Walmart worked with Pactum AI to create a pilot to handle certain supplier negotiations with AI chatbots. That led to a wider deployment in 2022—which you may recall is the year when ChatGPT first launched its public version. Now that ChatGPT has more than warmed up the general audience—the latest estimates for this one platform alone are 700 million users worldwide—agentic AI will only become more common. Nearly three‑quarters (72 percent) of chief procurement officers surveyed by Gartner say that AI tools like these are their top technology priority over the next five years. However, the next five years are not the next five minutes. “It’s still early days—we really don’t see any process that is fully agentic,” says Sesh Iyer, North America chair of BCG X, the tech build-and-design unit of Boston Consulting Group. For one thing, agentic AIs are still flummoxed by unexpected things, and that can make them take strange, if not brazen, shortcuts. For example, in a Carnegie Mellon study of a simulated company, researchers found that when an AI agent couldn’t find a particular person it needed to contact to complete a task, it just renamed another user. Overall, the top‑performing AI agent successfully completed only 24 percent of its tasks. But make no mistake—the tech is evolving fast, says Iyer: “We always overestimate what new technology will do in the short term and underestimate what it will do in the long term.” A practical playbook To avoid these problems of estimation in either direction, Iyer suggests starting with what you have right now. It’s easy enough to incorporate AI into negotiation prep with the tech your business probably already uses. Test it out with background research, brainstorming arguments and counterarguments, and use the voice function for rehearsal. But even at the most basic level of AI usage, it’s best to check in with your legal and IT security teams, since there are heavy privacy implications for both. When you’re using LLMs to their fullest for negotiation, you’re “allowing an incredible amount of access to information, including emails and alendars,” says Cameron Powell, co-founder of DeepLaw, a legal consultancy that uses AI tools for negotiation on behalf of its clients. Sharing this information can raise questions about confidentiality, liability, and intellectual property. When AI has proved its mettle and security to your satisfaction, the next step is using it to conduct deep analysis of your current contracts, sales, and negotiation processes. This could mean using AI to review your less-used suppliers that might be costing you more than they’re worth. You could also use AI to create side-by-side comparisons of your competitors or to provide a deep analysis of your current contracts to see what advantages you may be leaving on the table. AI can help manage tasks that are too time‑consuming or unwieldy to otherwise manage closely. Eventually, you can move to testing semi‑autonomous AI agents on repetitive or less nuanced negotiating tasks. Wherever you are in the process of integrating this new tool, Iyer suggests moving deliberately, and with a sharp eye toward integrating AI into your and your employees’ workflows. That MIT report that found little return on generative AI investment attributed much of the problem to enthusiastic focus on the gee-whiz technology itself, at the expense of truly considering how to make the best use of it today. “Focus on things that truly matter to your business,” urges Iyer. “Don’t try to do a thousand things at once.” BY ALISON J. STEIN