Can you build a $1.5 billion company in a market that Zoom and Google already dominate? Chris Pedregal proved you can. The Granola founder sat down with the Silicon Valley Girl podcast to talk about his popular AI notepad, which records meetings, transcribes them and lets users chat with their entire meeting history.
Taking on tech giants requires patience. Rather than launching publicly, Pedregal spent a full year sitting next to 150 users, watching them install and use the product, going home to fix what was broken and doing it again the next day. He resisted a public launch until the product was “meaningfully better than the competition.” The result was 500 installs on day one with zero advertising, followed by viral organic growth that eventually attracted major enterprise clients. This kind of intense focus is harder for Big Tech companies that have to spread their attention across dozens of products and hundreds of millions of users.
The mistake most founders make, he says, is letting the noise of social media and FOMO win. “Do not let it mess with your head,” he told host Marina Mogilko. “Care more about your particular problem.” The underlying problem you’re solving probably hasn’t changed in two years. Neither has the only thing that beats Big Tech.
Founder, Strike Fire Productions. Entrepreneur Staff. Jonathan Small is a bestselling author, journalist, producer, and podcast host. For 25 years, he… Read more
For years, influencer marketing has been dominated by big names and even bigger followings. Brands have invested heavily in creators who can deliver instant reach and visibility. But a shift is quietly taking place.
More brands are now turning to micro influencers. These are creators with smaller, more defined audiences and often a stronger sense of community and niche interests. Their content tends to feel more personal and less like traditional advertising, which can make it more effective with audiences who are increasingly wary of feeds saturated with product placements.
The shift reflects a wider change in how people use social media. Trust and relatability are becoming more important than scale. Whether it’s discovering a new skincare product, booking a hotel or finding outfit inspiration, people are often more likely to take recommendations from someone who feels familiar rather than a distant figure with millions of followers.
For brands, that means rethinking what influence actually looks like. Rather than prioritising sheer reach, many are focusing on creators who can spark genuine conversations and recommendations.
The key question now is how brands can make this approach work at scale while keeping the authenticity that makes micro influencers so effective.
For Jamie Love, social media expert and founder of Monumental Marketing, the change is very much intentional. Once upon a time, brands were heavily focused on creating awareness; now they’re knuckling down on sales.
Jamie Love
Jamie cites TikTok Affiliate as completely revolutionising the game, with smaller creators able to drive real results with far lower investment risk.
Not to mention, feed visibility is becoming harder to attain.
“Smaller accounts tend to have more engaged communities and stronger audience relationships, which often leads to deeper influence and better conversion compared to creators with massive but less connected followings,” he shares.
The good thing for micro influencers is that they can pretty much work across any industry, Jamie notes, though influencer marketing “shouldn’t be treated as a one-trick pony”.
That said, there are areas that work particularly well for smaller creators, including beauty, fashion, hospitality and travel. These are categories where audiences are often looking for recommendations from people they trust, rather than content that feels overly polished or PR-perfected.
The digital social media age has forced people to evolve and become more savvy with the way they consume ads, and quite frankly, “people see through marketing much quicker now”.
“Good marketing should feel seamless, rather than jarring. A beauty influencer suddenly promoting a car with no natural connection? Audiences spot that immediately,” he shares.
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So, when it comes to selecting micro influencers for the job, what metrics actually matter?
Through and through, engagement is still key. According to Jamie, follower count is somewhat a thing of the past in terms of not being able to portray the full story.
“Views, for instance, are also incredibly important because they show how well content travels beyond an influencer’s existing audience,” he shares, citing that his company has worked with many creators who consistently outperform their follower count.
“That’s often a strong indicator that they’re producing genuinely valuable content that platforms want to push organically,” he shares.
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As for whether micro influencers are here to stay, Jamie sees the shift as part of a wider evolution, and something he has been discussing for some time.
“It’s not a quick-win strategy. The brands seeing the best results are the ones taking a long-term, ecosystem-led approach,” Jamie says. “Combining different creator types, building relationships over time and understanding how creators fit into the wider customer journey rather than treating influencer marketing as a standalone channel.”
We see people caged, restrained and immobilised under harsh spotlights. They’re subjected to sensory deprivation, gassed; showered with chemicals, injected with viruses and put under the knife.
But the disturbing video below isn’t a trailer for a new Saw movie. It’s the boldest and most challenging advert yet from the animal rights organisation PETA (also see our roundup of World Cup 2026 adverts).
PETA’s new campaign End Animal Abuse aims to help viewers grasp what happens behind closed laboratory doors. Instead of relying on graphic depictions of animals, the 60-second film aims to create a direct emotional connection between viewer and animals subjected to testing by confronting audiences with human suffering instead.
It closes with a shot of a trembling woman on a metal gurney as a blanket is draped over her shoulders, before the text appears: “Relax, these are professional actors. But in reality, animals get treated like this every day”.
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With stark monochromatic imagery and infrared imaging techniques set to Gabriel Fauré’s Requiem in D Minor, the aim was to create an aesthetic that blends elements of arthouse cinema and experimental documentary.
The ad also aims to co-opt the visual language of fashion, with headgear that blurs the line between Maison Margiela-esque haute couture and instruments of torture. The result is disorientating and unsettling to watch.
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The ad was conceived and directed by Favio Vinson through Flavour on the Rocks and co-produced by Papaya Films.
Favio Vinson, Director at Papaya said: “We engaged in making this campaign for the challenge of tackling an important issue through a fresh approach. We committed to a very simple concept that would make the viewer directly relate to animals through heightened visual means. This way, the viewer wouldn’t be able to look away, despite the implied horror of the subject.”
A whole industry of data brokers buys up vast quantities of electronic information from cell phone apps and web browsers and sells it to advertisers who use that data to target ads. The same industry also sells that data, including bulk cell phone location data, to police departments and federal government agencies in ways that can reveal intimate details about Americans without a warrant.
Now, privacy advocates say that the best chance for Congress to close the well-known loophole around the Fourth Amendment that allows for that sort of governmental snooping is coming up in just a few weeks.
That’s when Congress is expected to take up reauthorization of what is known as Section 702 of the Foreign Intelligence Surveillance Act, which is set to expire on April 20.
After a 2015 change to the law, federal agencies are not supposed to collect data on U.S. citizens in bulk. But some found a workaround to requesting warrants by simply buying the data instead.
Last week, some 130 civil society organizations signed on to a letter urging members of Congress to include closing the data broker loophole in FISA 702 reauthorization, citing the “unprecedented expansion of warrantless mass surveillance that is sweeping up the private information of communities across America” and the potential for the loophole to be used “to supercharge AI-powered surveillance.”
At a Senate hearing last week, Sen. Ron Wyden (D-Ore.) asked Federal Bureau of Investigations director Kash Patel if he would commit to not buying Americans’ location data, which is usually obtained from cell phones. Patel declined to do so, instead saying the FBI “uses all tools” and “we do purchase commercially available information that’s consistent with the Constitution and the laws under the Electronic Communications Privacy Act, and it has led to some valuable intelligence for us.”
A spokesperson for the FBI declined to comment on which commercial data the FBI purchases. In 2023, then-FBI director Christopher Wray had indicated that the agency had backed away from using “commercial database information that includes location data derived from internet advertising.”
Feature image credit: Mandel Ngan/AFP via Getty Images
Going all-in on social media? That’s not a strategy. It’s a gamble.
In today’s digital society, social media keeps the world connected. It keeps you informed about what’s happening in the world and provides a channel for founders to market their companies.
According to the University of Maine, there are 4.8 billion social media users, representing 59.9% of the global population and 92.7% of all internet users. There’s no denying the opportunity to reach consumers through social media marketing, whether organic or paid.
It’s easy to create an offer, start marketing it on social media, and receive instant sales. However, you don’t own social media platforms, which leaves you dependent on others to get clients.
Depending on someone else to market your business is not a sound strategy, especially given how AI is changing things. Here’s how to create a diversified marketing plan that increases sales no matter what changes online.
Use each social media platform for a different type of marketing.
The beauty of social media for founders is that each platform has its own nuances with the types of consumers who frequent each platform. LinkedIn is considered a professional network. Instagram is a place for visuals. YouTube offers everything from education to entertainment. Facebook is where you can find the best advertising opportunities. TikTok has some of the best organic reach. Lastly, Threads offers thought-provoking conversations.
One way to diversify your social media use for lead generation, consumer education, and client acquisition is to leverage each network in different ways and market to different audiences. Posting the same content across platforms is ineffective because consumers expect optimized content for each platform.
Diversifying your content across platforms gives you the opportunity to split-test different messaging, offers, and client acquisition strategies. It also creates diversification. If one platform is not functioning, you have the other platforms to make up the difference.
Use social media for lead generation. Then, send consumers to the platforms you control.
Facebook, Instagram, TikTok, LinkedIn, or any social media platform can change their algorithms, your reach, what you have access to, or how you can market your business. Social media platforms can and do make changes without notice, and those changes can affect your business if they’re your only marketing channel.
Your goal should be to take advantage of the reach of social media, educate your consumers, and then direct them back to your email list, website, and other owned media. Generate leads that are sent to your owned platforms, so that no matter what happens with social media, you have marketing channels.
Focus on building your email lists.
Founders’ and their companies’ greatest asset is their email list. With an email list, you always have a way to market your offers, even if social media disappears. It’s also smart to create multiple email lists that are segmented based on how consumers found your company. You can split-test messaging, offer different options to different audiences, and build an asset that increases your company’s valuation. Email lists are sellable assets.
Create a personal brand that shows up in traditional and AI search.
Leveraging PR, thought leadership content, podcast guests, public speaking, media features, and educational content creates a strong and visible personal brand. Building a personal brand means you’ll always be able to sell, no matter how your offers change.
Your personal brand is even more important in the age of AI, as chatbot search pulls your credibility from the internet. One way to diversify your marketing beyond social media is to continue building your personal brand and show up more visibly in traditional and AI search results.
Leverage offline marketing channels.
In the digital information age, it can be easy to focus on only online marketing strategies. There’s a whole world of opportunity offline, at conferences, events, meetups, local networking, and more. Consumers have online fatigue post-pandemic, and in the age of AI and the metaverse. You’ll find potential clients and your consumers participating in offline channels, and you can reach them when you show up.
One great way to diversify beyond social media marketing is to add offline networking to your marketing plan. Connect with your local consumer base and, if you’re a nomadic founder, as you travel.
Social media offers a great opportunity for marketing, but it shouldn’t be your only channel, as you don’t own or control it. Create a diversified marketing plan and watch your revenue increase. It’s wise to have options.
A growing share of shoppers are not human. They are AI agents researching, comparing, and increasingly purchasing on behalf of consumers. Recently, OpenAI has pushed ChatGPT deeper into product discovery and merchant apps; Google has launched a universal commerce protocol (UCP) to let AI agents transact across retailers; and Amazon has released tools that let its agents shop other retailers’ sites on customers’ behalf.
Persuasion tactics refined by marketers over decades, built on well-documented patterns in human cognition, do not work the same way on AI agents. Some don’t work at all. Some backfire. This is not speculation. When we tested eight common e-commerce promotional mechanisms across four AI models in thousands of simulated shopping rounds, we found that only one behaved consistently the way we would expect it to for human buyers.
Most companies are not prepared for this. In an exploratory survey of 50 e-commerce executives across the U.S. and UK, the majority said they have already noticed traffic or conversion shifts they attribute to AI agents and are actively seeking ways to improve how agents engage with their sites. Yet many of these same executives believe that the cues that persuade human shoppers also tend to influence AI agents in similar ways, and that they already understand which elements of their websites matter most to agent behaviour.
Our research suggests this confidence is misplaced. The mechanics of persuasion were built on human subjects: on loss aversion, anchoring, scarcity bias, social proof. For AI buyers, these are not reliable principles. They are hypotheses to test. And findings may expire with every model update.
What We Found
We developed a proprietary simulation that replicates how AI agents interact with typical e-commerce product pages. We tested four different AI models (GPT-4.1-mini, GPT-5, Gemini 2.5 Pro, and Gemini 2.5 Flash Lite), each tasked with selecting among products presented in a realistic grid layout. We varied eight types of promotional badges commonly used in online retail: assurance signals (“Money-back guarantee”), countdown timers, strike-through pricing, scarcity cues (“Only 2 left!”), social proof (purchase counts), vouchers, bundles, and star ratings. Product categories rotated across four everyday items—a phone, a fitness watch, a washing machine, and a mouse pad—to test whether patterns held across common retail contexts. For each model and product, we ran 1,000 simulated shopping rounds, yielding more than 16,000 choice situations in total.
The headline finding was clear: Only ratings consistently pushed choices upward across all four models and product categories, mirroring the well-established human reliance on quality signals. Every other badge produced effects that varied by model and product category, sometimes dramatically. Social proof was the next most robust signal, but even that varied across cases.
By contrast, well-known tactics such as strike-through pricing, countdown timers, and bundling showed no stable pattern. In some cases they increased selection; in others they had no effect; and in at least one case bundling reduced it.
A broader pattern did emerge: the non-reasoning models—Gemini 2.5 Flash Lite and GPT-4.1-mini—were generally more responsive to promotional cues, whereas the reasoning models—GPT-5 and Gemini 2.5 Pro—were less responsive. But even this generalisation has limits: The same badge could produce opposite effects on the same model depending on the product category.
We then asked a deeper question: Why do these tactics work on humans, and does that same logic explain how AI agents respond? Each of these promotional cues works on people for a specific psychological reason. Scarcity badges trigger fear of missing out or the sense of potential loss, prompting people to act quickly before the item sells out. Yet this cue had no effect on some models, and GPT-5 even reacted negatively in certain product categories, suggesting a pattern that runs counter to what is typically observed in humans.
Similarly, strike-through prices create an anchor that makes the discount feel like a gain, thereby encouraging purchase. But we did not observe a consistent pattern of response in line with that logic. In fact, for Gemini 2.5 Pro, as the discount cue became more extreme, its additional persuasive effect weakened rather than strengthened.
The broader picture is clear: The promotional cues sometimes influenced agent choices, but not for the reasons they influence humans.
What Should Marketers Do About It?
Our findings point to a clear strategic imperative: The principles for persuading human buyers do not reliably transfer to AI agents. But the research also reveals an actionable structure beneath the noise.
Get the fundamentals right first.
Across every model we tested, two factors behaved exactly as they do for humans: price and ratings. Higher prices consistently reduced selection; higher ratings consistently increased it. Other badges and cues were not reliable.
Before investing in agent-specific tactics, firms should ensure their fundamentals are airtight: competitive pricing and strong, authentic review profiles.
Treat each model as a distinct market segment.
Marketers have spent decades segmenting human buyers by demographics, geographics, psychographics, and behaviour. Our results suggest they now need to consider a new segmentation variable: the AI model itself. Thinking of each model as a distinct segment, with its own response profile to promotional cues, provides a familiar and actionable framework for managing this complexity.
Adapt what you present to who, or what, is looking.
If each model responds differently, the logical next step is to serve different versions of your product information depending on which agent is interacting with your site or data feed.
A practical starting point is identifying which AI models generate the most traffic or transactions in their category and optimizing for those. This is becoming easier. As purchases increasingly flow through commerce protocols like Google’s UCP, merchants gain visibility into which AI platforms are driving their transactions. This mirrors the early days of mobile optimization, when firms initially designed for the dominant device before building fully responsive experiences.
A more powerful approach is dynamic: detecting the agent model and adjusting promotional cues in real time—for example, which badges appear, how pricing is framed, whether bundles or vouchers are surfaced—based on which agent is evaluating the page.
Today, this remains difficult. Most AI shopping agents browse through standard web browsers, making them hard to distinguish from human visitors in real time. But as commerce protocols mature and behavioural detection improves, the gap will narrow. The companies that begin building the testing infrastructure now will be best positioned to act when real-time tailoring becomes increasingly feasible.
Understand the prompt, not just the agent.
An AI shopping agent does not arrive with its own preferences. It arrives with the user’s prompt. A consumer who tells their agent “find me the best-reviewed wireless headphones under £100” has given it a very different mandate than one who says “get me the cheapest option that ships tomorrow.” The agent’s behaviour is shaped by these instructions.
Understanding the most common prompt structures in your category is a new and important form of consumer research. Firms should begin studying what consumers are asking their agents to optimize for. This could be through direct research, analysis of query patterns, or partnerships with AI platforms. The brands that understand how their customers talk to their agents will be better positioned to ensure their products surface in the right way for the right queries.
Expect more advanced models to be sceptical of marketing tactics, not indifferent to them.
A common assumption is that as AI models become more capable, they will become more “rational”—less susceptible to marketing cues, more like the perfectly informed utility maximisers of economic theory.
Our findings challenge this. More advanced models like GPT-5 and Gemini 2.5 Pro were less responsive to certain promotional tactics but they were not simply ignoring them. In several cases they appeared to penalize overt persuasion cues, as though interpreting them as signals of low quality or manipulation.
This means that aggressive promotional tactics, the kind that still work on many human buyers, may increasingly become counterproductive as agent models advance. The direction of travel is not toward agents that simply ignore your marketing; it is toward agents where more persuasion produces less selection.
Build a testing infrastructure, not a one-off strategy.
Perhaps the most important takeaway is structural. The promotional effects we measured today will not be the same after the next model. Every major release, fine-tuning adjustment or new safety alignment can shift how an agent responds to pricing frames, urgency cues, or social proof. Any fixed “agent optimization strategy” has a short shelf life.
Firms should be building simulation environments where they can systematically run AI agents against their product pages across models, categories, and promotional configurations. They could maintain a versioned database of agent behaviour, indexed by model release, so they can detect when a tactic that worked last quarter has stopped working or started backfiring.
. . .
For decades, marketers have refined every tool of persuasion with one audience in mind: humans. That audience is splitting. A growing share of purchase decisions will be made, or filtered, by agents that do not respond to your carefully engineered cues the way people do. Some will ignore them. Some, as our data shows, will hold them against you. For marketers who have spent careers perfecting the art of persuasion, the uncomfortable takeaway is that sometimes the best move is to dial it back. The brands that thrive will be those disciplined enough to know when persuasion itself has become the problem.
Stripe’s co-founder says AI agents will replace search-based shopping, forcing brands to appeal to algorithms, not humans.
John Collison thinks keyword search is a “ridiculous” way to find things to buy. The Stripe co-founder told Bloomberg that agentic commerce, in which AI agents shop on behalf of consumers, will completely transform the online shopping experience, reshaping not just how people purchase but how retailers sell.
The argument is structural. For more than a decade, e-commerce has been built around targeted ads, algorithmic recommendations, search engine optimisation, and infinite scrolling, a system designed to capture human attention and convert it into transactions. Agentic commerce replaces the human in the loop. When an AI agent evaluates products, compares prices, checks reviews, and initiates a purchase on a consumer’s behalf, the entire advertising and discovery infrastructure built for human eyeballs becomes less relevant. Brands will need to appeal to AI agents as well as, or instead of, human buyers.
Collison’s perspective is informed by Stripe’s position at the centre of internet payments. The company processes transactions for millions of businesses and has been building infrastructure specifically designed for agent-to-agent commerce. At Stripe Sessions 2026, held in San Francisco last month, the company unveiled its Agentic Commerce Suite, live integrations with Meta, Google, OpenAI, and Microsoft, alongside a Machine Payments Protocol co-authored with its blockchain subsidiary Tempo that enables AI agents to pay each other in stablecoins or fiat currency. Amazon responded this week by putting its Alexa for Shopping agent inside the main Amazon.com search bar, a defensive move designed to keep the buy flow inside Amazon’s ecosystem before external agents capture the high-intent query.
The question Collison raised in the Bloomberg interview, whether AI agents can truly mimic human taste, cuts to the heart of agentic commerce’s limitations. For commodity purchases, groceries, toiletries, repeat orders, an agent optimising for price, speed, and past preferences is straightforwardly useful. For high-consideration purchases, fashion, furniture, electronics, the role of personal taste, aesthetic judgment, and the experience of browsing is harder to delegate. The technology is advancing rapidly, but the gap between an agent that can find the cheapest flight and one that understands why you prefer a window seat on the left side of the aircraft is not trivial.
China is already further along this trajectory than the West. Alibaba integrated its Qwen AI assistant with Taobao’s catalogue of more than four billion products, reaching 300 million monthly active users. Alipay processed 120 million AI-agent transactions in a single week in February. Meituan, JD.com, ByteDance, and Tencent are all deploying similar capabilities. The structural advantage of Chinese super-apps, which integrate discovery, communication, payment, and fulfilment within a single environment, means the entire agentic shopping workflow can happen without leaving the platform. In the West, the buy flow still typically crosses multiple apps and websites, creating friction that agents must navigate and that incumbents can exploit.
The implications for retailers are significant. If an AI agent is the primary buyer, search engine optimisation gives way to something closer to agent optimisation, the discipline of making products legible to AI systems rather than to human browsers. Product descriptions, structured data, pricing transparency, and return policies all become inputs that agents evaluate programmatically. A brand that ranks well on Google but poorly in a ChatGPT shopping query may find its traffic evaporating.
Stripe is positioning itself as the payment infrastructure for this transition. Its Link product, which now has 250 million consumer wallets, has been adapted to function as an agent wallet, allowing AI agents to spend money on a user’s behalf within boundaries the user sets. Google, Amazon, and OpenAI are all building their own agentic commerce protocols, and the competition to control the payment rail that agents use is intensifying. Stripe’s bet is that it can be the neutral infrastructure layer that all agents transact through, regardless of which AI company built them.
Collison has previously described agentic commerce and stablecoins as “twin revolutions in intelligence and money.” At Stripe Sessions, William Gaybrick, Stripe’s president of product, used the same framing. The company’s $159 billion valuation, confirmed in a recent tender offer, reflects investor confidence that Stripe can capture value from both transitions simultaneously. Whether that confidence is justified depends on whether agentic commerce reaches the scale its proponents predict, or whether it remains, for the near term, a compelling idea that works better in conference keynotes than in the messy reality of online shopping.
Terms like insight, disruption, and engagement are misunderstood, misleading, and misdirect your media spend.
When marketers talk about their “films,” as if they are producing minor Spielbergian classics, it doesn’t just sound pompous and self-absorbed. This kind of thinking is what leads to bad advertising.
We pay to watch films. We want to understand the story and relate to the characters. Ads, by contrast, are watched unwillingly—not only with an abject lack of interest, but with significant motivation to ignore the message.
There are two Cannes festivals: one for film, and one for advertising. The industry would do well to remember that.
So when the industry refers to ads as “films,” it’s a marketing misnomer of grand proportions: not just inappropriate but directionally false. And it’s far from the only one.
Ad breaks: These are not breaks for ads—they are breaks from them. The TV industry’s own behavioural data shows more than half of in-room viewers disengage entirely during commercial breaks. Yet media buyers price reach against an exposure that, for the majority of impressions, never actually happens. We value a room with two adults in it more highly than with one, even though the research shows a lone viewer is more than twice as likely to watch the ads.
Storytelling: Most modern advertising is structurally incapable of telling a story. A 6-second bumper has a logo and a prayer. Calling that “storytelling” is creative cowardice dressed up as craft.
Activation: Whether it’s a tent at SXSW, a sampling stall in Westfield, or a TikTok stunt, most don’t move consumers. First, “activation” lets a team confuse doing a thing with achieving a thing. Second, it eats brand budget to the tune of six figures of media money being spent on canapés and an Instagram influencer.
Engagement: The metric of choice for the strategically lost. A Like is not engagement. A comment is not engagement. A share, in most cases, is not engagement. In essence “engagement” does not actually mean engagement. The misnomer has redirected an entire generation of marketing investment toward the 0.5% of category buyers who interact with brand content—usually because their hand slipped—while the 99.5% who actually drive sales go un-served.
Brand loyalty: The oldest lie in marketing. The Ehrenberg-Bass Institute has spent 40 years demonstrating that loyalty—in the sense of exclusive, committed, repeat purchase—is fictional. Category buying is a polygamous, stochastic, wobbly thing driven by mental and physical availability, not anthropomorphic devotion.
Brand love: The phrase implies an emotional bond between human and brand that no behavioural dataset has ever supported at any meaningful scale. Yes, we all have one or two brands we actually love. But the other 2,984 in our current repertoire don’t make our heart skip even a little beat. The job isn’t to be cherished—it’s to come to mind at the moment of purchase. Less romantic. Far more profitable.
Insight: They exist. But a genuine insight—a non-obvious observation about consumer behaviour that, acted upon, unlocks enormous growth—is a career exception, not a process; 99% of what gets stamped “insight” meets none of that definition. “Moms are busy.” “Gen Z values authenticity.” “People want convenience.” These are not insights. They aren’t even accurate. They are observations a moderately attentive 12-year-old could supply while playing a video game.
Full funnel: Advertising’s core concept is bandied around in a shotgun manner to suggest that A. we extract the whole customer journey, and B. get a firehose out and soak that puppy from top to bottom. That’s not what it should mean. It’s crucial to take in the full funnel during any initial diagnosis. But then you activate data and strategic thinking to work out where you want to apply resources to unlock growth.
Disruption: Clayton Christensen’s theory was a precise, narrow account of how low-end entrants displace incumbents: It’s usually slow and initially ignored by incumbents who don’t see the threat. Yet the word now means literally anything. Every Series A deck describes a disruption play. Every challenger brand pitches itself as disruptive when it is, in fact, a slightly cheaper version of an existing thing. Real disruption—rare, hard, terrifying—gets buried under the marketing copy of a marginally cheaper razor delivered by mail.
Consumer: We call them that because consumption is the only part of their lives we are interested in. But consumption is, for almost every human alive, the least interesting thing they do. A “consumer portrait” is likely to be 900 words on what they think, feel, hope, and want from a brand’s product—which should be one sentence. The remaining 875 words should be about a human: their job, kids, fears, Saturday mornings. If we saw them as human first, ironically, we’d understand them better as consumers second.
Mark Ritson has a PhD in Marketing and spent 25 years working as a marketing professor, and has also worked as both a global brand consultant and as the in-house brand consultant for LVMH. His articles have appeared in the Sloan Management Review, Harvard Business Review, the Journal of Advertising and the Journal of Consumer Research.
They may be artificial, but their impact is anything but. AI influencers are taking on huge brand deals and reaching millions worldwide.
Artificial intelligence has been consistently making waves in the marketing world – and the influencer sector certainly hasn’t escaped the AI revolution.
You’ve most likely heard about some of the (seedier) scandals involving AI models, virtual adult content creators, sinister deep fakes, and bogus product promotions. And if you haven’t, don’t worry – we’ve already written an entire article about it.
Well, AI influencers are no longer just cheap tricks or potential scams. They’ve officially hit the mainstream, with virtual influencers like Lil Miquela and Laila Khadraa striking up legitimate brand partnerships with the likes of Prada, Puma, and Samsung. There’s a growing business interest in AI-generated influencer campaigns, and it feels like this new sub-sector of the influencer world is gaining momentum.
Far from being a niche or novelty, these virtual creators are taking on incredible lucrative brand deals and reaching millions of people worldwide. While their avatars may be artificial, their impact is anything but.
So what does this all mean for real, human influencers? Is there still a role for creators who aren’t made of pixels, or is the 2025 AI takeover inevitable?
What are AI influencers? And Why have they taken off?
AI influencers (also known as virtual influencers) are computer-generated avatars that play a similar role to human influencers. They promote brands, sell products, and connect with audiences online.
Apart from being a shiny new use of AI technology, virtual influencers do offer some interesting benefits for marketers – according to Influencer Marketing Hub, 50% of those who have worked with virtual influencers found the experience to be ‘very positive’.
So what is so appealing about an AI influencer for digital advertisers?
They’re cost-efficient – and easy to scale
Since virtual influencers are generated by a computer, they’re not particularly fussy about payment. They don’t negotiate travel expenses or contract terms, and more importantly, they can rapidly produce content at scale – in multiple languages.
While there may be some costs associated with developing a new avatar (or partnering with an existing AI influencer) it’s likely to be cheaper – and this can be an appealing proposition for cost-conscious brands.
Brands have total control over creative messaging
Brands have complete control over what a virtual influencer says and does.
For example, artificial intelligence Instagram influencers won’t need to adjust a creative message to be more on-brand. Brands don’t need to explain product benefits or technical specifications to them, and they don’t need multiple content amendment rounds.
Marketers can avoid controversy and apply more control
When you’re working with a robot, it’s very difficult to get your brand into hot water. Marketers can dictate exactly what an AI influencer says, controlling everything from brand guidelines to specific language.
There’s no room for unexpected comments or influencer misinterpretations, which might be a big selling point for more cautious advertisers.
Do AI influencers make money? Are these virtual brand ambassadors actually effective?
The jury is still out on this one. While some evidence suggests that AI influencers can drive up to 3% more engagement on platforms like Instagram, other statistics say otherwise.
For instance, data from CreatorIQ states that many AI influencers utilised by global brands just aren’t delivering the same levels of engagement as their human counterparts.
Lil Miquela, a prominent AI influencer mentioned earlier in this blog, posted 7 pieces of content for BMW in 2023. These posts averaged a 0.6% engagement rate – compared to the 3.6% engagement rate achieved by human creators for BMW. In a similar story, Aitana Lopez (a Spanish AI model) has posted for clients like Nike, Fortnite, and Patagonia, delivering an average engagement rate of 2.9% – 1.03% below usual creator benchmarks for these brands.
Now, this isn’t to say that virtual influencers are totally ineffective. In some instances, they can certainly outperform human creators, and there are some respectable engagement rates delivered. But it feels like they’re not quite cutting the social media mustard.
So while these virtual influencers are making headlines, they’re not outperforming their real-life equivalents. Which begs the question – why?
The case for humanity in influencer marketing
Virtual influencers have plenty of similarities with real creators. They’re pictured with different products, they post lifestyle content, and they even respond to comments from their followers.
(Some of which are quite weird, but that’s a topic for another blog post.)
However, they lack the fundamental feature that makes influencer magic. A human personality.
When you really drill down to the core, AI influencers are essentially just virtual billboards, playing the pre-determined brand messages they’ve been programmed to deliver. They can’t reminisce about a recent holiday, excitedly unbox a new product, or express their true thoughts/feelings on a brand.
Now, I’m about to drop a serious buzzword, but it’s relevant. At its very best, influencer marketing is all about authenticity. Businesses partner with creators who can act as effective brand ambassadors because they actually use and enjoy their products. As humans, we can tell when someone is genuinely advocating for a product or service, and when they are, it can immediately shape our buying behaviours.
Virtual influencers, at best, can only imitate what a real influencer does. And personally, I don’t think that’s going to be enough in the long run.
Reflecting on the value of human creators
While I don’t believe virtual influencers can truly dominate the industry, they have given me the opportunity to reflect on why we connect so naturally with human influencers.
Real creators aren’t always perfect, but that’s what makes them so accessible and relatable.
My prediction? 2025 isn’t going to see the influencer world captured by AI creators and their questionable comment sections.
(Seriously, go and look if you don’t believe me.)
In fact, the role of human influencers in marketing is only going to become more crucial in a world grappling with deep fakes, AI-fuelled controversies, and rampant misinformation. Influencers won’t just be viewed as content creators or marketing assets – when used correctly, they’ll provide brands with a real, trustworthy, human face, and provide customers with real, trustworthy, human opinions.
*BUT, before I’m accused of being an AI-hating stick in the mud, I want to emphasise that there are plenty of other ways for artificial intelligence to enhance influencer marketing in 2025. In fact, AI influencers are probably one of the least exciting prospects here.
Instead, AI should be used to analyse online audience behaviours, understand the nuances behind high-performing content, and optimise influencer performance.
The bad news is that there’s a 0% chance of me being able to explain the finer details of AI potential for influencer marketing. The good news is that I don’t have to, because our resident AI genius James Wolman (from our sister agency Braidr) has given this brilliant synopsis:
“Everyone’s worried about AI creating fake influencers, but that’s missing the point. The real power of AI in 2025 won’t be about replacing humans – it’ll be about finally understanding what makes content actually connect with people. With LLMs/agentic AI now able to independently analyse and act on audience behaviour patterns, creators will have smart assistants that can actually help shape their content strategy in real-time. We’re moving past basic follower counts to seeing why some creators build genuine communities while others don’t. That’s the game-changer.”
So there you have it. Strike the delicate balance between relatable human influencers and AI-fuelled data analytics, and you’ll be golden in 2025.
We see incredible results with our clients because we place a strong emphasis on identifying the right influencers to connect with high-value audiences, influence real behaviours, and convert at scale. If you’re keen to leverage the full potential of human influencers in 2025, don’t hesitate to reach out for a chat!
Harley Finkelstein offers new details about the explosion of AI search on the e-commerce platform.
AI search is already upending e-commerce. Over the past year, shopping suggestions from popular large language models, such as ChatGPT, Claude, and Gemini, have delivered a sizable uptick in site traffic, sales, and new customers. That’s according to Shopify.
The $140 billion e-commerce company reported first quarter results earlier today, and during the conference call, president Harley Finkelstein offered new details about just how transformative AI-powered search has been for the millions of merchants on the platform. AI-driven traffic to Shopify stores has skyrocketed by 8x, compared to the first quarter of last year. Over that same period, orders that originated with AI-powered search have spiked by nearly 13x. LLMs have also been helping companies source new customers.
“New buyer orders from AI searches are actually occurring at nearly 2x the rate of traditional organic search,” said Finkelstein during the earrings call. “These merchants are now discovering new buyers on these agentic services that they may not otherwise have seen.”
More than three-quarters of e-commerce companies have already started rethinking their marketing plans to account for AI search, according to a survey conducted by the financial technology company Mercury last fall. This tide shift has spurned an entirely new industry of generative engine optimization, often abbreviated as GEO. This strategy, which has supplanted its digital forefather search engine optimization: starts with a straightforward question: How do I get this agent to recommend my company?
While startups are still very much in the experimental stage of answering that question, founders have told Inc. that they have found success so far by expanding their digital footprint, so that their name and their company name is included in as much AI training data as possible. In practice, that means blanketing the internet: talking with journalists, going on podcasts, posting on LinkedIn, hosting webinars, publishing case studies, conducting original research, producing highly-specific educational content, and engaging in thought leadership as a founder.
When in doubt, go straight to the source and prompt the LLM itself. “The trick is to ask. Ask Google in AI mode, or ask ChatGPT,” 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. last year. “Very few people have had a conversation with AI about why it would or wouldn’t recommend them.”
The three-time Inc. 5000 founder says the goal is “about training the AI to believe that you’re the best option.”
