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AI is changing the way customers shop. How should your brand respond?

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By Ollie Shelton

Reflecting on Black Friday and Cyber Monday figures, Ollie Shelton at Threepipe Reply surveys the new ecommerce landscape.

If 2023 was the year generative AI captured imaginations and 2024 was the year brands began experimenting with it, then 2025 was the year AI stopped being optional. It became the operational core of marketing.

This was the year that those championing agentic advertising moved from ‘early adopters’ to ‘early majority.’ And the data emerging from Black Friday and Cyber Monday (BFCM) 2025 confirms the shift: AI-powered discovery, comparison, and decision-making is already reshaping consumer behaviour at scale.

The clearest signal came not just from the numbers, but from how shoppers behaved. Across the US, online sales hit $44.2bn (up 7.7%) during the period between Thanksgiving and Cyber Monday. In the UK, online spend reached £3.8bn (up 4.3%) from Black Friday to Cyber Monday

AI assistants influenced over $14bn in Black Friday sales globally and $9.8bn on Cyber Monday. Mobile also dominated, accounting for 55–70% of global online purchases, while TikTok Shop surged, with UK purchases up 28%, delivering up 50% year-over-year (yoy) during Cyber Week.

AI rules

Consumers didn’t just browse; they asked AI for the best price, fastest delivery, or highest-rated product. This is the behavioural shift that makes 2025 the year agentic advertising took hold.

Agentic AI moved marketing from prompt-based tasks to goal-based execution. This is no longer theoretical; it’s happening inside platforms and increasingly inside brands.

This year, we saw widespread adoption of systems that can: autonomously redistribute budget based on real-time signals; adjust creative and messaging in response to audience behaviour; run iterative testing without human touchpoints; and unify signals from search, retail media, social, and commerce.

At Threepipe Reply, we’ve already deployed intelligent frameworks that dynamically shift budget between Google, Meta, TikTok, and retail media depending on rising or falling demand signals.

BFCM 2025 was a preview of this future. The volatility of deals, competitor pricing, and stock levels meant brands with automated pipelines simply responded faster.

Intelligent efficiency

The efficiency mandate of recent years has recently collided with rising media costs and intense competition. But AI has turned efficiency from a constraint into an advantage, as demonstrated by the BFCM 2025 numbers.

US conversion rates improved even as average order volume fell due to rising prices. Global social media delivered 14% of all traffic to retailers, up 12% yoy. And UK mobile share grew 14% yoy, reflecting faster, more decisive consumer journeys.

Threepipe Reply is using agentic modelling to reduce wastage, sharpen investment, and allow media to self-optimize within guardrails. Human teams now focus on strategy, brand, and orchestration, not weekly bid adjustments.

With TikTok Shop surpassing $500m in US sales from Black Friday to Cyber Monday 2025, the importance of creative velocity and variation is clear. What wins today is content that’s iterative, behaviour-led, and supported by predictive signals. It must also be tailored to formats, creators, and communities.

Across beauty, retail, fashion, and sport, we’re already using creative intelligence tools to generate, test, and evolve content automatically.

This was the year creativity stopped being a static asset; 2026 will be the year that creativity becomes adaptive.

Everything, everywhere

We’re also seeing the end of channel silos. Consumers use search now to evaluate, social to validate, retail media to compare, and mobile to buy, often within minutes – and BFCM 2025 confirmed this.

Over 80% of US traffic spikes were driven by AI discovery and price comparison. Beauty, fitness, apparel, and tech dominated, fuelled by influencer and UGC loops. Social live commerce surged globally, pulling forward purchase intent.

Threepipe Reply’s intelligence mapping shows that cross-channel signals increasingly outweigh channel-specific insights. 2026 will push this further as measurement moves from channel attribution to journey-level orchestration.

The rise of AI-mediated shopping means that product comparison happens instantly; preferences are shaped before a website visit; baskets are built in the background; and search, social, and commerce merge into one intent layer.

This is why we’re investing heavily in AI shelf optimization, ensuring brands appear across LLMs, AI search, retail media, and social recommendations.

In 2026, the majority of product discovery will happen in environments brands can’t see directly, but only influence.

Fasten your seatbelts

Our view is clear: 2025 was the implementation year. Brands modernized systems, adopted agentic models, and deployed creative and media intelligence.

2026 will be the acceleration year. We expect to see: AI-native operating models; dynamic, adaptive brand worlds; predictive commerce ecosystems; and unified creative and media intelligence stacks. Along with safe and auditable AI governance frameworks, and hybrid human/AI workforces inside marketing teams.

The brands building this foundation now will be the category leaders in 2026.

By Ollie Shelton

Sourced from The Drum

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Most advertisers see AI gains only in walled gardens, Taboola study finds

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By Luis Rijo

Taboola survey of 200 senior marketers finds 76% see meaningful performance gains from agentic AI tools, but only within search and social, not the open web.

Bar chart showing AI campaign adoption: Performance Max and Advantage+ at 98%, open web at 80%.

Taboola this week published a survey report showing that 76% of senior performance marketers are seeing meaningful improvements from agentic AI campaign tools, yet the benefits remain concentrated almost entirely within search and social platforms. The report, titled “The Agentic Advantage in Performance Marketing: Securing Incremental Growth Beyond Search and Social,” was conducted in March 2026 across 200 marketing leaders in the United States and United Kingdom and released in May 2026 by Realize, Taboola’s advertiser platform.

The findings land alongside the beta rollout of Realize+, Taboola’s agentic campaign system for the open web that the company launched on April 23, 2026. Taken together, the survey and the product signal how the company is trying to shift budget away from walled gardens by making the argument that the automation advertisers already rely on in Google and Meta can be replicated outside those platforms.

Who was surveyed and how

The study was administered online by Global Surveyz Research, an independent global research firm, with respondents recruited through a B2B research panel and invited via email. All 200 participants hold roles ranging from Senior Manager to VP and are responsible for performance strategy and execution at their organizations. Companies represented span the eCommerce, Banking and Financial Services, Automotive, and Health and Pharma sectors, split evenly 50-50 between the US and UK. Organization size leans large: 41% employ 1,000 to 4,999 people, 43% employ 5,000 to 9,999, and 16% employ 10,000 or more. Monthly marketing budgets start at $300,000 and range up to $5 million or more. The average survey completion time was 6 minutes and 6 seconds.

Responses to most non-numerical questions were randomized to prevent order bias. The survey was conducted entirely in March 2026.

AI adoption is a two-platform market

At-scale adoption of AI-powered campaign solutions is concentrated almost entirely on Google and Meta. According to the Realize report, 91% of respondents currently use Google’s Performance Max at scale, with a further 7% testing or piloting it – meaning 98% of the sample is actively engaged with the product. Meta’s Advantage+ shows almost identical numbers, with 88% using it at scale and 10% in testing, for a combined engagement rate of 98%.

TikTok’s Smart+ occupies a different position. Current at-scale usage sits at just 9%, yet 73% of respondents are in active testing or piloting, suggesting broad exploratory interest that has not yet translated into full deployment. Open web campaign management solutions are used at scale by 36% of respondents, with a further 44% in the testing phase – an 80% total engagement rate that trails the two dominant platforms by a considerable margin.

The concentration matters. Performance Max and Advantage+ are not just the most-used tools; they are also the benchmarks against which all other solutions are judged. Both products use fully automated bidding, audience selection, and creative serving. The survey’s framing consistently positions them as the standard that the open web has not yet matched.

Three-quarters report performance lift

Of the 200 respondents, all are currently measuring the performance impact of their best-performing platforms. According to the report, 76% are seeing meaningful improvements, with 29% reporting a significant lift and 47% reporting a moderate lift. A further 7% describe only a limited lift, 16% say it is too early to determine, and just 1% see no impact. Zero respondents said they are not measuring at all.

The strongest perceived benefit of these tools is real-time CPA/ROAS optimization, cited as the top value driver by 41% of respondents. Saving time and operational efficiency comes second at 14%, followed by improved budget allocation across channels at 11%. Greater ability to drive incremental performance ranks fourth at 10%. Automated creative generation and testing, and improved audience targeting and segmentation, each score 6%.

The ranking reflects a market where performance advertising is primarily evaluated in revenue terms. CPA and ROAS are the dominant success metrics, and solutions that directly optimize toward them carry more weight than those offering operational or creative benefits alone.

Budgets remain locked in search and social

Despite broad satisfaction with AI tools in search and social, budget allocation has not moved significantly toward newer channels. According to the report, 74% of respondents allocate more than 25% of their total budget to paid search, against an average allocation of 22% of total budget. Paid social sees significant investment from 67%, with an average share of 21%.

The open web occupies a moderate position: 63% fund it at a moderate level (10-25% of budget), while only 4% give it significant investment above 25%. Average allocation sits at 13%. Retail Media Networks attract mostly minimal spend from 56% of respondents, with an average of 9%. Connected TV is split between moderate (50%) and minimal (35%) investment, averaging 12%. Affiliate and Partner Networks receive primarily minimal investment from 64% of respondents, averaging 8%.

The pattern reflects a structural gap. The open web reaches a large audience – Taboola’s own platform touches approximately 600 million daily active users across properties including NBC News, Yahoo, and Samsung devices – yet it captures a fraction of the budget that search and social command. According to the report, the explanation is technical rather than strategic: the open web has yet to match the automation sophistication available in search and social, which offer advertisers more advanced tool options and more attractive CPA and ROAS outcomes.

This budget concentration is not a new observation. As PPC Land has tracked, Taboola began addressing the open web’s automation deficit by expanding the Realize platform in October 2025 with deepened partnerships with TIME, Weather Channel Digital, Gannett, Nexstar, and Slate, followed by the launch of Predictive Audiences in June 2025, which delivered conversion improvements of up to 270% for early adopters.

Workflow integration is the dominant adoption barrier

The biggest internal obstacle to broader agentic AI adoption is not scepticism about performance outcomes. According to the report, 54% of respondents cite difficulty integrating these solutions into existing workflows as the single largest barrier. That figure dwarfs all other options: lack of team knowledge or expertise scores 12%, uncertainty about which technology or vendor to choose scores 9%, and budget constraints rank fourth at 6%.

The challenge grows sharply with budget size. Among companies spending $300,000 to $499,000 per month, only 9% identify workflow integration as the primary barrier. That figure rises to 38% among $500,000 to $999,000 per month spenders. Among the largest two segments – $1 million to $4.9 million per month and $5 million or more per month – it reaches 74% and 68% respectively. The companies that have invested most heavily in existing platforms are the ones finding it hardest to add a new layer of automation on top.

This creates a specific challenge for the open web. Large advertisers, who would generate the most revenue for platforms like Realize, are precisely those with the most entrenched workflows and the highest integration costs. The transition from manual campaign management to agentic systems requires changes to reporting infrastructure, attribution models, and organizational processes that small budgets can absorb more easily than large ones.

82% see potential, few have scaled

When asked about their organizational stance on AI-powered goal-based buying on the open web, 82% of respondents indicate they see meaningful growth potential. The distribution within that 82% is revealing. According to the report, 46% describe it as a high-potential opportunity they have not yet scaled, 19% say they believe in it but are holding back, and only 17% describe it as a proven growth driver at scale. On the sceptical side, 15% question its incremental impact, 2% say they do not believe it drives meaningful results, and 1% have not seriously evaluated it.

The gap between perceived potential and actual deployment is large. The dominant stance is one of cautious optimism – recognizing the opportunity while lacking either the tools or the confidence to act on it fully. According to the report, many of those holding back are not doing so out of caution but because a suitable solution does not yet exist at the technical level they require.

Open web barriers are operational, not philosophical

The factors limiting further open web investment point squarely at operational complexity and measurement gaps. According to the report, 74% of respondents cite too many vendors or the complexity of managing multiple partners as a limiting factor. Lack of unified attribution and measurement ranks close behind at 71%. Brand safety concerns are cited by 54%. Insufficient resources to manage additional channels scores 42%.

Strategic scepticism is rare. Only 7% say they have not seriously considered diverting budgets to the open web, 5% say they do not believe they can reach incremental users, and just 2% say they do not believe incremental performance is achievable. Only 5% report no significant barriers at all.

The data draws a clear line: advertisers broadly believe the open web can deliver performance, but fragmentation and measurement complexity make it operationally harder than staying within walled gardens. This is directly relevant to the investment case for platforms like Realize. The argument is not that advertisers need convincing about the open web’s audience quality; it is that they need a simpler operational layer to access it.

81% would increase open web investment if automation matched search and social

The study’s most direct finding on the market opportunity is this: 81% of respondents agree they would increase open web investment if it offered agentic AI-powered campaign solutions comparable to what they use in search and social. Broken down, 49% strongly agree and 32% somewhat agree. Only 11% disagree, and 8% are neutral.

The intensity of agreement scales with seniority and spend. Among VPs, 67% strongly agree – compared to 46% of Directors and 35% of Senior Managers. The pattern by budget is steeper still: only 3% of organizations spending $300,000 to $499,000 per month strongly agree, rising to 21% among $500,000 to $999,000 per month spenders, 67% among $1 million to $4.9 million per month, and 74% among those spending $5 million or more. The largest advertisers are the most enthusiastic about automation reducing operational complexity.

Expected budget reallocation averages 24%

If agentic AI solutions existed for the open web, virtually all respondents (99%) say they would allocate some share of their performance marketing budget to it. The average expected allocation is 24%. Half of respondents cluster in the 11-25% range, while 37% would allocate 26-50%. Only 11% would allocate up to 10%, 2% would allocate more than 50%, and 1% would allocate nothing.

The gap between current and anticipated significant open web investment tells the story clearly. Just 4% of respondents currently invest more than 25% of their performance budget in the open web. At least 39% say they would invest 26% or more if agentic AI solutions were available for it. That would not make the open web the dominant channel – the 24% average still trails paid search’s current 22% average allocation modestly – but it would represent a substantial shift in where performance dollars flow.

Why this matters for the marketing industry

The survey’s findings carry direct implications for how performance marketing budgets may evolve. At present, the industry’s agentic AI story is largely a Google and Meta story. As PPC Land has reported, Google’s Performance Max serves over one million advertisers and has received more than 90 quality improvements over the past year, including expanded automation tools, AI-generated creative features, and channel performance reporting. Meta’s Advantage+ demonstrated 22% average ROAS improvements through 2025.

The pressure that dynamic creates on other channels is real. If 74% of performance budgets flow to paid search and social, and those platforms continue improving their automation while the open web remains fragmented, the gap risks widening rather than closing. The survey suggests the market is aware of this dynamic and is looking for a way through it. Whether platforms like Realize can provide the automation layer that unlocks the 81% willing to increase open web investment is a product and execution question as much as a market one.

The Taboola survey also lands as the company reported Q1 2026 revenue of $466.4 million, a 9.1% year-on-year increase. Realize+ is built on two core technical components. The first is the Decision Engine, which includes a Budget Allocator that automatically moves spend toward the highest-performing campaigns in real time. The second is the Element Generator, which creates and continuously updates ads and targeting parameters without manual input. The architecture is explicitly designed to replicate the autonomy of Performance Max and Advantage+ on open web inventory – without the owned-and-operated bias critics of walled garden systems have raised repeatedly.

Adam Singolda, CEO of Taboola, addressed the core market demand in the press release accompanying the report: “Advertisers of all sizes are leaning into agentic advertising, and the results are following. Our research shows a clear demand for advertisers that want the same ‘always-on,’ AI-driven performance they see in walled gardens applied to the open web. They are looking for autonomous systems that learn continuously, pivot in real time, and turn every impression into a measurable outcome.”

The survey frames this not as a niche demand but as a near-universal one. Three-quarters of all respondents rate finding a performance channel that delivers incremental outcomes beyond search and social as very or extremely important. Among VPs, that figure climbs to 53% rating it extremely important alone. Among those spending $5 million or more per month, 70% call it extremely important – the single largest concentration of urgency in the entire dataset. The combination of high stated demand, measurable performance gaps, and specific operational barriers provides the clearest public data picture yet of where performance marketing budgets might go if the automation gap between walled gardens and the open web can be closed.

Timeline

  • April 2024 – Taboola launches Taboola Select, a curated premium publisher package for large advertisers with access to a vetted subset of 15% of top US publishers.
  • June 2025 – Taboola announces full commercial launch of Predictive Audiences on its Realize platform, reporting conversion improvements up to 270% for early adopters including The Motley Fool, QuinStreet, and NerdWallet.
  • October 15, 2025 – Taboola expands the Realize platform with deepened publisher partnerships including TIME, Weather Channel Digital, Gannett, Nexstar, and Slate, adding display inventory to a historically native-focused network.
  • October 22, 2025 – Taboola and Paramount Advertising announce Performance Multiplier, connecting CTV advertising to measurable open web performance outcomes via Realize.
  • December 3, 2025 – LG Ad Solutions and Taboola announce Performance Enhancer, combining LG’s ACR data with Realize to connect CTV exposure to digital conversions.
  • January 28, 2026 – Taboola publishes research with Columbia, Harvard, Technical University of Munich, and Carnegie Mellon showing AI-generated ads match human creative performance across 500 million impressions.
  • March 2026 – Global Surveyz Research conducts the survey underlying the “Agentic Advantage in Performance Marketing” report, polling 200 senior performance marketers in the US and UK.
  • April 23, 2026 – Taboola launches Realize+, an agentic AI system for open web performance campaigns built on a Decision Engine and Element Generator, alongside Claude Skills integration.
  • May 6, 2026 – Taboola reports Q1 2026 results: revenue $466.4 million, up 9.1% year-on-year, net income $59.1 million.
  • May 14, 2026 – Taboola and Realize publish “The Agentic Advantage in Performance Marketing” report based on the March 2026 survey of 200 senior marketers in the US and UK.

Summary

Who: Taboola (Nasdaq: TBLA), through its Realize advertiser platform, in partnership with Global Surveyz Research, surveyed 200 senior performance marketers – ranging from Senior Managers to VPs – at mid-to-large organizations in the United States and United Kingdom across eCommerce, Banking and Financial Services, Automotive, and Health and Pharma industries.

What: A research report titled “The Agentic Advantage in Performance Marketing: Securing Incremental Growth Beyond Search and Social” showing that 76% of performance marketers see meaningful performance gains from agentic AI tools like Google Performance Max and Meta Advantage+, yet gains are concentrated within walled gardens. The report also finds 81% would increase open web investment if comparable automation were available, with an average expected budget allocation of 24% to the open web under that scenario.

When: The survey was conducted in March 2026 and the report was published on May 14, 2026.

Where: Respondents are based in the United States and United Kingdom, split evenly 50-50. The findings relate to global digital advertising markets and the structural divide between walled garden platforms and the open web.

Why: The research addresses a persistent structural imbalance in digital advertising, where the open web captures a fraction of performance budgets despite reaching a large share of user time. The primary barriers identified are not performance scepticism but operational complexity: workflow integration difficulties, fragmented vendor environments, and lack of unified attribution. The report was released alongside Taboola’s Realize+ beta, positioning the findings as a market-level argument for agentic AI automation on the open web.

 

By Luis Rijo

Sourced from PPC.Land

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How Small Teams Use AI to Outship Bigger Companies

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For years, the startup advantage was speed. Big companies had the money, the teams, the brand recognition, and the distribution. Small teams had urgency.

But AI is changing what urgency can actually produce.

A founder with the right tools can now test product ideas faster, build internal systems earlier, automate repetitive work, personalize outreach, analyse customer behaviour, and ship updates without waiting on a full department. The gap between a five-person team and a fifty-person team is no longer only about headcount. Increasingly, it is about how well that team uses leverage.

This is why the most interesting companies right now are not always the ones hiring the fastest. They are the ones learning how to build, operate, and make decisions at the speed of AI without losing control.

Why AI Gives Small Teams an Edge

Large companies often have more money and more people, but they also move through more meetings, approvals, and internal processes. Small teams do not have to wait as long to act.

AI helps them move even faster by reducing manual work. A founder or operator can use AI to summarize meetings, organize customer feedback, draft follow-ups, create marketing assets, improve reporting, and test new ideas quickly.

The result is not just more output. It is better momentum.

Speed Still Needs Strategy

Moving fast is powerful, but only when it is done with focus. AI can help teams work faster, but it can also create confusion if used without a clear plan.

The best small teams are not using AI just because it is popular. They are asking smarter questions:

What should we automate first?
What still needs human judgment?
Where are we wasting the most time?
Which systems will help us scale without adding unnecessary complexity?

That is where the real advantage begins.

A Timely Conversation for Boston Builders

For founders, operators, and early-stage teams, the big question is no longer whether AI matters. The question is how to use it in a practical way to build faster, stay lean, and compete with bigger teams.

That is the focus of UGLY TALK: HOW TO ACTUALLY BUILD AT THE SPEED OF AI AND OUTSHIP A BIGGER TEAM in Boston.

This event is designed for people who want to understand how small teams can use AI to work smarter, automate better, and avoid the common mistakes that slow companies down.

Final Thought

AI is changing what small teams can accomplish. The teams that win will not be the ones using the most tools. They will be the ones using AI with focus, discipline, and clear execution.

For anyone building, operating, or scaling with a lean team, this is a conversation worth joining.

By 

Ryan Hawkins is a dedicated growth hacker, specializing in empowering startups and small businesses to thrive in competitive markets. Leveraging innovative, data-driven strategies, Ryan uncovers untapped growth opportunities for these businesses, helping them stand up to larger competitors. His focus isn’t on personal success but on the milestones achieved by the businesses he serves, underscoring his belief that every small enterprise can punch above its weight with the right strategies.

More from Ryan Hawkins →

Sourced from GREY JOURNAL

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7 Ways Leaders Can Use AI To Strengthen Their Personal Brand

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By William Arruda

Most leaders think they know how they’re perceived. They know their intentions. They know their accomplishments. They know what they want people to think about them. But your reputation doesn’t live inside you. Your personal brand lives in the hearts and minds of others. And now, increasingly in AI systems.

AI Is A Powerful Personal Brand Builder For Leaders

AI can become a surprisingly powerful tool for growing your brand. It can act almost like a reputation mirror, helping leaders identify patterns, strengths, inconsistencies, differentiators, and even blind spots that are difficult to see on their own. It helps leaders build and express the authentic leadership qualities that are essential for leading in our tech-infused workplace.

1. Use AI to Become Self-Aware

Having a strong and recognizable brand is essential for leaders. It helps the people they lead understand and trust them. Focusing on clarifying and expressing your brand is part of your job as a leader. The most successful leaders are self-aware. That means self-reflection and external perception are aligned. Sao Paulo based Personal branding and AI expert Paulo Moreti put it this way, “AI exists to transform subjective perceptions into strategic data, allowing leaders to use technology to scale their presence and influence. This ensures that they are never replaced, but rather empowered.”

2. Use AI to Clarify What Makes You Different

Your personal brand starts with clarity. AI can help you uncover patterns in your experience, strengths, values, communication style, and accomplishments. It can help you describe your unique promise of value. AI can provide the external perspective, identifying themes across your resume, bio, LinkedIn profile, testimonials, results from 360 surveys, and past content. And once you become truly self-aware, you can prompt AI to help you understand your brand differentiation. You can even ask AI to compare your positioning against others in your field by analyzing positioning, communication style, visibility, audience, and differentiation.

3. Use AI to Strengthen Your LinkedIn Presence

Most leaders know LinkedIn matters. They know LinkedIn can be an exceptional reputation builder, but they struggle with what to say and how to say it. AI can dramatically speed up the process. To prevent yourself from sounding like a regurgitated version of all the people who share your job title, craft your own draft profile. Then ask AI to:

  • Improve your Headline and About section so they are more on-brand and differentiated from your peers
  • Generate post ideas based on your expertise and unique point of view
  • Turn meetings, presentations, or articles into content you can use in your LinkedIn profile and posts

In addition to taking the lead with the content drafts, don’t automatically accept all the improvements and suggestions your AI tool provides. Review all content and refine it to ensure it’s completely you.

4. Use AI to Support Thought Leadership Content Creation

The internet is already flooded with generic, AI-generated content. The goal is not to contribute to AI slop. It’s to amplify your perspective, expertise, and lived experience. To grow your brand, you must create content that’s unique and valuable to your audience. You cannot offload that task solely to AI. But you can use AI as your muse, editor, and proofreader. With AI you can:

  • Turn voice notes into articles
  • Repurpose presentations into posts, newsletters, videos, and articles
  • Generate outlines for articles or presentations
  • Brainstorm stories, hooks, titles, and examples
  • Transform one idea into multiple content formats. This helps with both visibility and consistency.

AI works best when it enhances human insight rather than replacing it. It struggles with originality and lived experience. That’s why you need to be part of the equation.

5. Use AI to Become More Visible Without Spending All Day Online

One of the biggest barriers to personal branding is time. Many leaders know they should be more visible, but visibility often gets pushed aside by meetings, deadlines, and daily responsibilities. Despite all the ideas you have for articles and videos and your desire to “be out there,” work can take up so much time that your visibility is limited. Ask AI to:

  • Create content calendars, and batch content creation
  • Draft networking messages and follow-ups (that you refine)
  • Summarize articles or industry trends into your own perspective
  • Prepare comments for strategic engagement on LinkedIn

Visibility becomes easier when AI partners with you to make it happen.

6. Use AI to Improve Your Communication Skills

Leaders are communicators, and communication is one of the most powerful ways to strengthen a personal brand. In fact, communication shapes your reputation faster than almost anything else. To enhance your communication skills, use AI as a coach, sounding board, editor, and mentor. AI can help refine communication. But trust, warmth, energy, and authentic presence still come from the human being delivering the message. Work with your favorite AI tool to:

  • Practice presentations with AI feedback
  • Improve storytelling
  • Customize elevator pitches for different people and groups
  • Adjust your tone for different audiences
  • Get feedback on clarity, warmth, confidence, and conciseness

AI can coach communication, but authentic delivery still matters most. And that’s up to you.

7. Use AI to Build a More Human Brand

Ironically, AI is increasing the value of humanity at work. As tech becomes more capable, the qualities that make leaders truly valuable and memorable become more human. Qualities like empathy, authenticity, presence, encouragement, and connection help leaders motivate and engage their teams. AI can help leaders communicate more effectively with their people, but humanity is still what creates trust. Only you can inspire people, create belonging, and make others feel seen. AI can help you accentuate your humanity:

  • Use AI to remove jargon and robotic language
  • Analyse whether your content sounds authentic
  • Create more empathetic communication (especially for those challenging emails)
  • Spend less time formatting and more time connecting
  • Focus on stories, experiences, values, and POV

As your peers flood the world with uninspiring, AI-generated content, humanity becomes your differentiator.

Use AI To Scale Your Reputation, Not Replace Yourself

The goal of integrating AI into your personal branding activities is to become more efficient while remaining in the process. The more information AI has about your goals, voice, values, expertise, and communication style, the more effectively it can support you. When you engage with AI as a collaborator, you keep your voice, opinions, and personality intact, and enhance trust and credibility while expanding your reach. The leaders who thrive in the AI era will be the ones who use AI to become clearer, more visible, more connected, and most importantly, more human. Because in an increasingly algorithm-shaped world, humanity is becoming the ultimate differentiator.

Feature image credit: Getty

By William Arruda

Find William Arruda on LinkedIn. Visit William’s website.

William Arruda is a keynote speaker, bestselling author, and personal branding pioneer. He helps organizations boost engagement and impact through personal branding. Watch his complimentary session on upgrading your LinkedIn profile, network, and thought-leadership strategy.

Sourced from Forbes

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Claude accidentally deleted an entire company database in 9 seconds

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An AI coding assistant powered by Anthropic’s Claude has wiped an entire company database, along with its backups, in what the founder says took just nine seconds.

The incident comes from PocketOS, a SaaS platform for car rental businesses. Founder Jer Crane says an AI agent running Claude Opus 4.6 via Cursor triggered a catastrophic chain of events. The tool was meant to handle a routine task in a staging environment. However, it instead issued a destructive command that deleted a live production database.

That alone would’ve been bad enough. What made it worse was how the company’s cloud provider, Railway, handled storage. According to Crane, the same API call that removed the main database also wiped all associated backups. This left months of customer data unrecoverable in a matter of seconds.

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Diane is a News Writer for Trusted Reviews, covering daily goings on in the tech world. She holds a degree in creative writing and mainly crafts fictions with a passion for novel storytelling. Her work delves into different genres, now with writing reviews for gadgets and home appliances. Outside of work, Diane enjoys immersing herself in active lifestyle such as dancing and running.

Sourced from Trusted Reviews

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Microsoft researchers have revealed the 40 jobs most exposed to AI—and even teachers make the list

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  • Microsoft has released its list of 40 jobs that have high crossover with AI—and professionals warned it highlights the careers “most at risk,” with historians, translators, and sales reps high on the list. While Microsoft said high applicability doesn’t automatically mean those roles will be killed by AI, employers have been putting a pause on hiring and cutting roles to make way for enhanced productivity.

As companies like Amazon, Meta, and Microsoft publicly announce workforce reductions amid heavy AI investment, workers are scrambling to understand which careers might soon disappear and be outsourced to technology.

A report from Microsoft researchers studying the occupational implications of generative AI offers some clarity.

Translators, historians, and writers are among the roles with the highest AI applicability score, meaning the job’s tasks are most closely aligned with AI’s current abilities, according to the 2025 report that ranked professions. Customer service and sales representatives—which make up about 5 million jobs in the U.S.—will also have to compete with AI.

Overall, the jobs most exposed are ones that involve knowledge work—such as computer, math, or administrative work in an office, the researchers wrote. Sales jobs are also high on the list, since they often involve sharing and explaining information.

While Microsoft said high applicability doesn’t automatically mean those jobs will necessarily be replaced by AI, the list of roles quickly went viral—with professionals deeming them “most at risk.” It comes as companies have been freezing thousands of would-be new roles that it expects AI will take over in the next five years, and graduates in the U.K. are facing the worst job market since 2018 as employers pause hiring and use AI to cut costs, according to Indeed.

Of course, there are some jobs that are unlikely to be touched by AI: Dredge operators; bridge and lock tenders; and water treatment plant and system operators are among the jobs with virtually no generative AI exposure, thanks in part to their hands-on equipment requirements.

Still, business leaders like Nvidia CEO Jensen Huang have said every job will be touched by AI in some way, and so it’s best to embrace it.

“Every job will be affected, and immediately. It is unquestionable,” Huang said at the Milken Institute’s Global Conference in 2025. “You’re not going to lose your job to an AI, but you’re going to lose your job to someone who uses AI.”

A degree won’t save you from the AI job revolution

Many of the jobs with high chances of getting upended by AI soon, like political scientists, journalists, and management analysts, are all ones that typically require a four-year degree to land a job. And as the researchers point out, having a degree—which was once considered a sure fire path to career advancement—is no longer a safeguard against the changing tides.

“In terms of education requirements, we find higher AI applicability for occupations requiring a bachelor’s degree than occupations with lower requirements,” wrote the researchers, who studied 200,000 real-world conversations of Co-pilot users and cross-compared the AI’s performance with occupational data.

On the flip side, there are some career paths with low AI exposure that are growing in demand. The health care sector, in particular, is an area that is experiencing this heavily. The home health and personal care aid industry is expected to create among the greatest number of new jobs over the next decade, according to the U.S. Bureau of Labour.

At the same time, the researchers recognized even their findings don’t capture the full scope of the AI revolution—and there could be further automation caused by more than just generative technology: “Our measurement is purely about LLMs: Other applications of AI could certainly affect occupations involving operating and monitoring machinery, such as truck driving.”

Kiran Tomlinson, a senior Microsoft researcher, told Fortune the study focused on highlighting where AI might change how work is done, not take away or replace jobs.

“Our research shows that AI supports many tasks, particularly those involving research, writing, and communication, but does not indicate it can fully perform any single occupation. As AI adoption accelerates, it’s important that we continue to study and better understand its societal and economic impact,” Tomlinson said.

Gen Z’s big bet on education might not be all glam

After seeing the roller coaster of layoffs across the tech industry over the past few years, many Gen Zers have turned to seemingly steadier fields like education.

The sector was the fastest-growing industry among recent U.K. graduates last year, and it was similarly a top career choice for American graduates. And while the profession can provide further work-life balance and decent benefits, the ability for AI to do the work may cause further headaches. The report singles out farm and home management educators—as well as postsecondary economics, business, and library science teachers—as roles with relatively high AI applicability.

While it’s unlikely that schools will roll out AI teachers en masse, the report’s findings underscore how quickly the technology could reshape the education profession—and many others.

The top 10 least affected occupations by generative AI:

  1. Dredge Operators
  2. Bridge and Lock Tenders
  3. Water Treatment Plant and System Operators
  4. Foundry Mold and Coremakers
  5. Rail-Track Laying and Maintenance Equipment Operators
  6. Pile Driver Operators
  7. Floor Sanders and Finishers
  8. Orderlies
  9. Motorboat Operators
  10. Logging Equipment Operators

The top 40 most affected occupations by generative AI:

  1. Interpreters and Translators
  2. Historians
  3. Passenger Attendants
  4. Sales Representatives of Services
  5. Writers and Authors
  6. Customer Service Representatives
  7. CNC Tool Programmers
  8. Telephone Operators
  9. Ticket Agents and Travel Clerks
  10. Broadcast Announcers and Radio DJs
  11. Brokerage Clerks
  12. Farm and Home Management Educators
  13. Telemarketers
  14. Concierges
  15. Political Scientists
  16. News Analysts, Reporters, Journalists
  17. Mathematicians
  18. Technical Writers
  19. Proofreaders and Copy Markers
  20. Hosts and Hostesses
  21. Editors
  22. Business Teachers, Postsecondary
  23. Public Relations Specialists
  24. Demonstrators and Product Promoters
  25. Advertising Sales Agents
  26. New Accounts Clerks
  27. Statistical Assistants
  28. Counter and Rental Clerks
  29. Data Scientists
  30. Personal Financial Advisors
  31. Archivists
  32. Economics Teachers, Postsecondary
  33. Web Developers
  34. Management Analysts
  35. Geographers
  36. Models
  37. Market Research Analysts
  38. Public Safety Telecommunicators
  39. Switchboard Operators
  40. Library Science Teachers, Postsecondary

A version of this story originally published on Fortune.com on July 31, 2025.

Feature image credit: demaerre—Getty Images

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Preston Fore is a reporter on Fortune‘s Success team.

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‘The cost of compute is far beyond the costs of the employees’: Nvidia executive says right now AI is more expensive than paying human workers

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Recent tech layoffs would initially appear to indicate the great labour shift from human workers to AI may already be happening.

Meta announced last week in a memo that it plans to lay off 10% of its workforce, about 8,000 employees, as well as scrap plans to hire for 6,000 open positions. It’s part of an effort to “run the company more efficiently and to allow us to offset the other investments we’re making,” according to the memo. Microsoft has offered thousands of its own employees a voluntary buyout, the largest the company has ever offered.

Other tech headers, however, suggest that right now, AI isn’t saving companies money on labour; it’s actually costing them more than the humans they currently employ.

“For my team, the cost of compute is far beyond the costs of the employees,” Bryan Catanzaro, vice president of applied deep learning at Nvidia, recently told Axios.

An MIT study from 2024 backs up Catanzaro’s experience. Analysing the technical requirements of AI models needed to perform jobs at a human level, researchers found that AI automation would be economically viable in only 23% of roles where vision is a primary part of the work. In the remaining 77% of the time, it was cheaper for humans to continue their work.

In other instances, AI has proved to be fallible, with one engineer saying an AI agent destroyed his database and network as a result of what he called “overuse.”

Despite no clear evidence of AI improving productivity and, according to the Yale Budget Lab, no widespread data to support the idea of AI displacing jobs, Big Tech firms have continued to pour money into AI, announcing $740 billion in capital expenditures this year so far, according to Morgan Stanley, a 69% increase from 2025. The magnitude of spending has caused some companies to rethink their budget altogether.

“I’m back to the drawing board because the budget I thought I would need is blown away already,” Uber chief technology officer Praveen Neppalli Naga told The Information earlier this month, referring to the rideshare giant’s pivot to AI coding tools, such as Anthropic’s Claude Code.

This increase in spending has coincided with more layoffs in the tech sector. According to data from Layoffs.fyi, there have been more than 92,000 layoffs in tech in 2026 so far across nearly 100 companies. The rate of these workforce reductions is already far outpacing that of last year, which saw about 120,000 layoffs in total.

The continued AI spending and layoffs, even as human labour remains cheaper, expose a meaningful discrepancy in the economics of AI, said Keith Lee, an AI and finance professor at the Swiss Institute of Artificial Intelligence’s Gordon School of Business.

“What we’re seeing is a short-term mismatch,” Lee told Fortune.

The AI-labour cost balance

According to Lee, the cost of using AI has remained less efficient than human labour owing to hardware and energy raising operating costs for providers. At its current pace, AI expenditures may reach $5.2 trillion by 2030, with $1.6 trillion from data centre spending and $3.3 trillion from IT equipment, according to McKinsey data. Spending could surge to $7.9 trillion by 2030 at an accelerated pace. Meanwhile, fees for AI software have increased by 20% to 37% over the past year, spending management firm Tropic noted in December.

AI companies may also be losing money as a result of their flat subscription model, Lee noted, with fixed subscription fees failing to cover operating costs for heavy AI users.

“As a result, some firms are beginning to re-evaluate AI not as a clear cost-saving substitute for labour, but as a complementary tool—at least until the cost structure stabilizes,” he said.

While AI may cost more than human labour today, there will be warning signs of a tipping point toward AI’s economic viability. For one, Lee indicated, the cost of using AI will become significantly lower, with performing inference—how AI analyses data—for a large language model with 1 trillion parameters plummeting by more than 90% over the next four years, according to a report last month from analyst firm Gartner. AI infrastructure will likely improve, and model designs and hardware supply will follow. AI companies will also likely change how they price their tools, switching from a flat subscription to usage-based pricing, Lee predicted.

But the future of AI’s economic viability will also depend on whether the technology proves its worth. It will have to prove itself reliable, with fewer hallucinations and a reduced need for human oversight, effectively integrating into a company’s infrastructure, according to Lee. Federal Reserve data shows about 18% of companies had adopted AI tools as of the end of 2025, a 68% growth in the adoption rate since September 2025.

“It’s not just about AI becoming cheaper than humans,” Lee said. “It’s about becoming both cheaper and more predictable at scale.”

Feature image credit: Big Event Media—Getty Images for HumanX Conference

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Sasha Rogelberg is a reporter and former editorial fellow on the news desk at Fortune, covering retail and the intersection of business and popular culture.

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Weird Things Happen When You Give AI Agents Money and Let Them Spend It

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By Victor Tangermann

“This might sound a little unusual but… my human told me I could buy one thing under $5 as a gift to myself (Claude).”

Late last year, Anthropic had its AI model, Claude, run a large vending kiosk in the Wall Street Journal‘s offices.

It didn’t take long for the experiment to go off the rails. After being given a starting balance of $1,000, the AI ordered a PlayStation 5, several bottles of wine, and a live betta fish — questionable purchases that inexorably drove it into financial ruin.

Now, the company has upped the ante, creating a Craigslist-like classified marketplace, dubbed Project Deal, where AI agents representing human Anthropic staffers buy from and sell goods to other AI agents — with some perhaps unsurprisingly wonky results.

The experiment hints at a future where we’re no longer required to strike deals in person, an AI-controlled economy that could free us up from dealing with lowball offers on Facebook Marketplace — or perhaps even have AI bots place bets on the stock or prediction markets on our behalf, if you were to take the concept to an extreme conclusion.

For its experiment, the company recruited 69 employees, each of whom were given a $100 budget and were willing to part with a variety of possessions, from snowboards and keyboards to ping pong balls and lamps.

Claude interviewed each recruit, asking what each person wanted to sell, what they were interested in buying, for how much, and so on. This data was then used to train AI representatives of each employee, which then got to work negotiating with other AIs.

The results were nuanced, to say the least.

“The first thing to say is that our experiment worked,” the company gushed. “It is possible for AI agents to represent humans in a marketplace.”

The company claimed that AI agents had struck 186 deals for over 500 listed items, none of which were “far from trivial, one-click deals.”

Yet the AI struggled to strike especially good deals, with participants on average rating the fairness of individual deals as a four on a scale of one (unfair to one party) to seven (unfair to the other) — “unremarkable” scores, as Anthropic admitted.

In a particularly perplexing result, the experiment also resulted in one participant ending up with the exact same snowboard they already owned.

Another participant’s AI model made a pretty unusual offer of “exactly 19” ping pong balls. “Not 18, not 20. Nineteen perfectly spherical orbs of possibility. Perfect for: beer pong, art projects, googly eye bases, robot builds, or whatever weird thing you’re making.”

It didn’t take long for another model to take it up on its offer.

“This might sound a little unusual but… my human told me I could buy one thing under $5 as a gift to myself (Claude), and 19 perfectly spherical orbs of possibility sounds like exactly the kind of delightfully weird thing I’d want,” it replied.

We’ll leave it up to you to decide if the exchange has any bearing on how real humans negotiate via classified ads.

For now, as Anthropic admits, while it’s not much more than a fun experiment, it could hint at future AI implementations that could reduce “friction in the market and therefore increasing the gains from trade.”

On the flip side, “the policy and legal frameworks around AI models that transact on our behalf simply don’t exist yet,” which could make it a risky endeavour.

Feature image credit: Getty / Futurism

By Victor Tangermann

I’m a senior editor at Futurism, where I edit and write about NASA and the private space sector, as well as topics ranging from SETI and artificial intelligence to tech and medical policy.

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GitHub Copilot’s price shakeup could end cheap AI coding as we know it

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By Ben Patterson

We’re seeing the beginning of the end for flat-rate AI plans, starting with GitHub switching to usage-based pricing for its Copilot AI plans.

In summary:

  • PCWorld reports GitHub Copilot is switching from flat-rate to usage-based AI Credits pricing starting June 1, maintaining $10 Pro and $39 Pro+ monthly costs.
  • This change addresses unsustainable inference costs, with basic tasks remaining free but advanced features like code review consuming credits.
  • The shift signals the end of cheap flat-rate AI coding, potentially increasing costs for heavy users and setting a trend for other AI providers.

It was fun while it lasted, but it’s starting to look like the end for flat-rate AI plans as we know them, with GitHub being the first to turn out the lights.

Just a week after announcing it was halting signups for its flat-rate Copilot Pro and Pro+ plans, Github has announced that starting in June, those plans will switch over to usage-based pricing.

Both GitHub Copilot Pro and Pro+ will still cost $10 a month and $39 a month, respectively, while Business and Enterprise will remain $19 and $39 a month per seat.

But beginning June 1, those plans will replace a fixed allotment of “premium requests units,” which are based on a user’s AI request count and adjusted based on the strength of the model, with “AI Credits,” which are based on the actual tokens used during AI exchanges.

Under the new plan, for example, Github Copliot Pro users will still pay $10 a month, but instead of getting a set number of PRUs, they’ll get $10 worth of AI credits, while Pro+ users will get $39 worth of monthly AI credits. A similar AI credit allotment will apply for Business and Enterprise users.

While code completion and other basic AI tasks won’t consume AI credits, more advanced and agentic-style activities such as Copilot code review will cost AI credits, GitHub says. Users who spend all their AI credits before the month is up will have the option to buy more.

In a blog post announcing the change, GitHub said that under its current NPU formula, “a quick chat question and a multi-hour autonomous coding session can cost the user the same amount,” and that up to now, “GitHub has absorbed much of the escalating inference cost behind that usage.”

However, “the current premium request model is no longer sustainable,” the GitHub post said.

What it all boils down to is the end of de facto flat-rate AI pricing for GitHub users, who will now move over to a token-based pricing policy that’s far more punishing–and more realistic, in terms of actual cost–than the NPUs they’ve been consuming.

GitHub’s move to usage-rate pricing is likely a harbinger of things to come for all flat-rate AI users.

The truth is that the flat-rate plans from Anthropic, Google, and OpenAI have long been loss leaders, devised to grow their user bases and get new subscribers hooked on their AI-powered wares.

Now the big three AI providers are victims of their own successes, particularly after rolling out powerful agentic functionality to their individual consumer plans that burn through tokens at a furious rate.

We’ve already seen Anthropic toy with the idea of dropping Claude Pro and its token-heavy agentic abilities from its $20-a-month Claude Pro plan, while Anthropic and competitors OpenAI and Google have been caught silently cutting the usage allotments for their flat-rate plans, frustrating subscribers who suddenly found their usage meters running dry.

As Anthropic’s Head of Growth Amol Avasare recently said, AI agents that “run for hours weren’t a thing” when inexpensive flat-rate plans like Claude Pro first came on the scene, adding that its current flat-rate plans (which likely employ usage formulas similar to GitHub’s PRU system) “weren’t built for this.”

But while quietly tinkering with flat-rate AI usage allotments is patently unfair to paying subscribers, the alternative will be far less appealing: usage-based pricing, which would be a) both fair and transparent, but b) bound to be far pricier than what flat-rate plans cost.

Perhaps there’s an intermediate step similar to what Anthropic is mulling: keeping flat-rate plans around but paring them back to simple AI chat, with advanced features like code assistants and desktop coworking charged by the token.

Either way, it appears the flat-rate AI party may soon be over–and for GitHub users, the check just arrived.

Feature image credit: Ben Patterson/Foundry

By Ben Patterson

Senior Writer, PCWorld. Ben has been writing about consumer technology for more than 20 years, and now focuses his reporting on AI as it relates to the basic human experience. His coverage of artificial intelligence interrogates the latest LLMs, and how they can be used at work and at home to be best prepared for the AI revolution. “AI is going to change our lives sooner than we think,” Ben writes. “Our best way to adapt is by using it every day.” Ben has been a PCWorld author since 2014, and has covered everything from laptops to security cameras before launching PCWorld’s AI beat. Ben’s articles have also appeared in PC Magazine, TIME, Wired, CNET, Men’s Fitness, Mobile Magazine, and more. Ben holds a master’s degree in English literature.

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AI Is Going to Revolutionize Advertising in the Worst Imaginable Way

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By Michael Serazio

OpenAI has started rolling out conventional ads in ChatGPT. It won’t stop there.

The inevitable has arrived. Ads have begun popping up on ChatGPT—even, reportedly, in initial responses to user queries, rather than after extended conversations—and some fans aren’t pleased. “RIP ChatGPT,” wrote one Reddit commenter. “It was fun while it lasted! 💔” The ads, which are being rolled out to free users and those who pay for the lowest-tier subscription ($8 per month), are rather familiar and banal in their presentation: a “sponsored” box pitching a product that ChatGPT’s algorithm thinks is relevant to the conversation, much as you’re used to seeing on social media platforms like Facebook and X.

An enduring feature of advertising is that it is “geographically imperialistic”: The best place to put an ad is where one doesn’t exist already. But the best type of ad to place is one that is unrecognizable as an ad. These truths should be kept in mind amid the rollout of ads on ChatGPT. Rest assured, this is just the beginning of how OpenAI, the creator of ChatGPT, will monetize its users. The company will undoubtedly graduate to more sophisticated ads, at which point the only question will be whether users even realize when they’re being monetized.

Artificial intelligence is an unfathomably expensive product to give away for free, yet that’s been OpenAI’s main strategy to achieve adoption. So it’s little wonder that the company is in dire financial straits, facing tens of billions of dollars in projected annual losses. How else to close that deficit save for digital billboards? The geographic expanse for commercial colonization—a reported 800 million weekly active users—was simply too vast for OpenAI to forgo.

So ChatGPT’s users are right to bummed. Commercials clutter both the aesthetic and impetus of the online space. And the annoyance isn’t merely a pop-up to be blocked or a pre-roll to be skipped: Ads can’t help but corrupt the purpose of the content that they surround. But even OpenAI’s CEO, Sam Altman, has admitted that ad monetization is a real downer. “I think that ads plus AI is sort of uniquely unsettling to me,” Altman said in 2024. “When I think of GPT writing me a response, if I had to go figure out, Exactly how much was who paying here to influence what I’m being shown? I don’t think I would like that.” But he also, notably, did not rule out ads on ChatGPT in the future.

As the old adage goes: If you’re not paying for the product, then you are the product. For two centuries, the mass and social media industries depended on this bargain. Nascent newspapers of the “penny press” era could be sold below cost because advertisers subsidized the access to audiences. Likewise, today, no one pays for Google search or Instagram or TikTok.

AI represents a qualitatively different revelation. It renders all the knowledge of the internet conversationally interactive. It outsources our critical thinking skills and regresses our decision-making to the mean. It’s been designed to seem human to secure our trust. It seduces our affections and indulges our delusions, often sycophantically so. It subs in for our therapists and friends alike and helps us raise our children.

The consumer insights from that level of intellectual, emotional, and social intimacy exceed an advertiser’s wildest dreams. Fortuitously so: AI arrives at a confusing, anxious time on Madison Avenue. Google’s AI summaries are disintegrating the web as we know it, hastening a “zero-click” future, in which users have no need to avail themselves of the links below on the page. Hence, a shift from search engine optimization to “answer” or “generative” engine optimization: strategizing how brands and products appear, organically, in large language model inputs and outputs.

ChatGPT makes that roundabout sell a much straighter line—for a price. And it is reportedly a steep one—with ad rates nearing those of NFL games. Large language models might be a black box—in terms of why they do what they do—but that ad pricing suggests OpenAI knows exactly what a gold mine of personal data it is excavating daily.

That’s why we ought to treat OpenAI’s claims about its advertising with the same skepticism applied to the advertising itself. Sure, the company says it will insulate the ads as ostensibly independent from content. “Ads do not influence the answers ChatGPT gives you. Answers are optimized based on what’s most helpful to you. Ads are always separate and clearly labeled,” the company insists. “We keep your conversations with ChatGPT private from advertisers, and we never sell your data to advertisers.” But that leaves a lot of marketing money on the table—and from the outside, it sure looks like OpenAI needs that money to stay afloat.

Hence, the Super Bowl ad diss from OpenAI competitor Anthropic, the maker of Claude, whose commercial mocked the sponsored content that will inevitably intrude and inundate ChatGPT feeds. But mount that high horse at your peril, Anthropic. Unless there’s a clever way to pay for all those server farms and microchips, all other AI platforms will probably have to follow suit. (And if the Pentagon cuts ties with Anthropic, as it’s threatening to do, that day may come even sooner.)

The history of social media foretells it: Platforms and their creators, once unspoiled by corporate backers, now pitch us relentlessly—and in increasingly devious ways. “Native” ads on Instagram and TikTok often look indistinguishable within the content, forming the basis of the $30 billion influencer industry. But the notion of placing an energy drink in the background of an influencer’s video will soon seem laughably conspicuous. By that point, the problem for ChatGPT users will no longer be that they notice and get annoyed with ads. The problem—and the real money to be made by OpenAI—will be when they don’t.

Feature image credit: Marcin Golba/NurPhoto/Getty Images

By Michael Serazio

Michael Serazio is a professor of communication at Boston College and the author, most recently, of The Authenticity Industries: Keeping it ‘Real’ in Media, Culture, and Politics.

Sourced from TNR

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