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Muck Rack, the AI communications platform, today joined the Sounds Profitable Partner Network, the Boston-based trade association for the podcasting industry announced on May 13, 2026. The partnership brings together two organizations whose work has been converging as podcast appearances generate editorial pickups, YouTube clips, and citations in AI-powered search results – blurring the lines between earned media strategy and audio distribution.

The announcement signals something broader than a standard partnership deal. Sounds Profitable, which counts nearly 210 organizations globally in its network, has historically served podcast companies and audio platforms. Muck Rack is not a podcast company. It is a PR software platform used by communications professionals to track media coverage, monitor brand mentions, and measure how organizations appear in news and in AI-generated answers. Its entry into the Sounds Profitable ecosystem reflects a shift in where the podcast industry is drawing attention from outside the audio world.

Podcasting as earned media infrastructure

The rationale for the partnership is grounded in a structural change in how podcast content travels. A single brand appearance on a podcast no longer stays within that episode’s listenership. It lives on YouTube as a video clip, gets picked up by journalists writing about the same topics, and increasingly surfaces when users query AI-powered search systems. According to the press release from Sounds Profitable, 71% of podcast creators now produce video content, meaning the distribution surface of any given audio appearance has expanded considerably.

Muck Rack’s platform monitors exactly that kind of multi-channel propagation. The company combines global media monitoring, Generative Engine Optimization (GEO) insights, social listening, media data, AI automation, and analyst advisory services. According to the announcement, the platform helps organizations manage reputation, act quickly, and demonstrate impact across the PR workflow. Thousands of journalists also use Muck Rack’s free tools to showcase their work and analyse news.

For PR professionals advising brands on podcast strategy, the question has shifted. It is no longer only about which shows to appear on. It is about how that appearance travels – whether it earns editorial coverage, whether it surfaces in AI-powered search results when someone asks a brand-related question, and whether the brand’s communications team can measure the full downstream reach of a single recorded conversation.

“PR professionals are finally recognizing what podcast listeners have always known: audio is where trust gets built. Muck Rack has been part of my toolkit throughout my career because it’s one of the few platforms that can actually measure that trust over time,” said Molly DeMellier, Head of Communications at Sounds Profitable. “Bringing Muck Rack into the Sounds Profitable Partner Network gives our team, clients, and the broader podcast industry, the strategic communications infrastructure they deserve.”

The Sounds Profitable network and what membership includes

Sounds Profitable describes itself as the trade association for the podcasting industry. Founded to address a gap between podcasting’s audience scale and the industry’s ability to communicate its value to brands and media buyers, the organization operates an influential newsletter with 10,000 subscribers globally. It runs a podcast covering audio industry developments, maintains what it describes as the only searchable repository of key data points in podcasting, and hosts events including Podcast Movement, Cannes Lions, SXSW, and The Podcast Show.

Partner Network membership, according to the announcement, includes direct access to that research database, membership in a Slack community of more than 2,100 industry leaders, monthly strategic advising sessions, and priority access to major industry events. The nearly 210 members span the breadth of the audio ecosystem – hosting platforms, ad tech providers, agencies, publishers, and now, for the first time in a clearly visible way, a PR software company.

That last detail is the one industry observers are likely to note. The Sounds Profitable Partner Network has functioned as a map of where the podcast industry’s infrastructure sits. Muck Rack’s entry suggests that infrastructure is expanding upstream – into the communications and reputation management layer that operates before a podcast is even distributed, and well after the episode file is downloaded.

The Podcast Show London: where the partnership begins

The partnership launches with a joint appearance at The Podcast Show London, scheduled for May 20 to 21, 2026. Molly DeMellier, Head of Communications at Sounds Profitable, and Natan Edelsburg, Chief Partnerships Officer at Muck Rack, will appear together on the Brand Stage for a fireside chat titled “The New Word of Mouth: Podcasts, Earned Media, and AI Search.”

The session is framed around original research from both organizations. According to the press release, DeMellier and Edelsburg will examine how awareness, earned media, and discoverability now compound across channels, and what that means for communications strategy. The 71% video creator figure from Sounds Profitable’s research shapes part of the session’s argument: that a brand appearance can no longer be treated as a single-channel event.

The Brand Stage placement is notable. The Podcast Show London’s Brand Stage is specifically oriented toward how companies and communications professionals engage with the medium – not the creator or technical side of podcasting. It is a signal about who the session is aimed at: marketing and communications decision-makers who are still forming their frameworks for podcast strategy, rather than podcast industry insiders already embedded in the space.

Edelsburg addressed that gap directly. “Podcasts have become one of the most powerful channels for building brand credibility, but most PR teams don’t yet have a framework for thinking about them strategically,” he said. “Sounds Profitable is the organization that understands this space better than anyone. We’re excited to bring our research and platform to their network and to start that conversation on stage in London.”

Market context: podcast advertising at record scale

The partnership arrives at a moment of documented commercial growth in podcasting. Podcast advertising spending climbed 32% year-over-year in the fourth quarter of 2025, according to Magellan AI data. That followed 26% year-over-year growth in Q3 2025. The IAB and PwC’s 2025 Internet Advertising Revenue Report placed total podcast advertising at $2.9 billion in the United States for the full year.

Edison Research’s Infinite Dial 2026, released in March 2026, found that 58% of Americans now listen to podcasts monthly – a new record, equivalent to 167 million people. Weekly listeners stood at 45%, approximately 130 million. The figures represent a medium that has moved well beyond niche status, yet a structural imbalance persists. Consumers dedicate 31% of their media time to audio content while advertisers allocate only 9% of budgets to audio platforms, a 22-percentage-point gap widely cited as the central problem in audio advertising economics.

Video has accelerated the audience reach numbers but complicated the measurement picture. Edison Research updated its podcast ranking methodology in 2025 to include individuals whose sole podcast consumption occurred through video platforms, reflecting the scale shift brought by YouTube. Audioboom reported that over 13% of its business came from video revenue by Q3 2025, and Apple introduced HLS video podcast infrastructure with dynamic ad insertion in February 2026.

That complexity – audio appearing on video platforms, podcast appearances generating editorial coverage, brand mentions surfacing in AI-generated answers – is precisely the environment Muck Rack was built to monitor. The partnership with Sounds Profitable places Muck Rack in direct proximity to the industry’s primary research and knowledge network at a moment when brands are actively working out what podcast measurement actually means.

GEO and AI search: the emerging measurement frontier

One of the more technically specific aspects of Muck Rack’s offering, as described in the announcement, is its Generative Engine Optimization (GEO) insights capability. GEO refers to the practice of understanding and improving how a brand or organization appears in answers generated by large language models and AI-powered search systems such as Google’s AI Overviews, ChatGPT, and similar tools.

The addition of GEO to the podcast context is not incidental. As podcast appearances generate transcripts, editorial pickups, and YouTube clips, those downstream artifacts become part of the content corpus that AI search systems index and synthesize when generating answers. A brand that appears consistently in high-quality podcast conversations, and whose appearances generate further editorial coverage, may surface more frequently in AI-generated brand-related answers.

Muck Rack’s platform tracks both traditional media monitoring and how brands appear in AI-generated answers. That dual capability places it at an intersection that few PR platforms have reached. For communications professionals working with brands that are expanding into podcasting, the ability to track the full chain from audio appearance to AI search citation represents a new measurement surface.

Industry convergence: PR technology meets the podcast ecosystem

The Sounds Profitable Partner Network has grown from its earlier configuration of around 150 partners – visible in materials from Podcast Movement 2024 – to nearly 210 as of this announcement, a figure also reflected in the organization’s most recent public-facing descriptions. That growth trajectory maps onto the period of strongest commercial development in podcasting, when advertising spending, audience measurement, and distribution infrastructure were all advancing simultaneously.

Muck Rack joining that network is, in one reading, a data point about normalization. Podcasting is now sufficiently embedded in mainstream media and brand communications that the PR platform sector has direct strategic interest in understanding it – not as a novelty or a supplemental channel but as a primary channel for earned media that requires monitoring, measurement, and reputation management at the same level as print, broadcast, or digital news.

The announcement noted that Sounds Profitable sits at the center of the industry for companies looking to enter the space. That positioning has historically attracted audio and advertising technology companies. Its attraction of a communications platform suggests the categories of companies that see strategic value in the podcast ecosystem are expanding.

For marketing and communications professionals, the partnership offers a practical signal: the infrastructure for treating podcast appearances with the same analytical rigor as traditional press placements is taking shape. Whether through Muck Rack’s monitoring and GEO tools, through Sounds Profitable’s research database, or through the two organizations’ joint work that will be visible at The Podcast Show London and in future programming, the gap between podcast strategy and mainstream PR measurement is narrowing.

Timeline

Summary

Who: Sounds Profitable, the trade association for the podcasting industry, and Muck Rack, an AI communications platform used by PR professionals to monitor media coverage and AI search appearances.

What: Muck Rack joined the Sounds Profitable Partner Network, a network of nearly 210 organizations globally. The partnership includes a joint appearance at The Podcast Show London on May 20-21, 2026, with a Brand Stage fireside chat on podcasts, earned media, and AI search. Muck Rack brings global media monitoring, Generative Engine Optimization insights, social listening, and AI automation to a network historically focused on audio and advertising technology companies.

When: The partnership was announced on May 13, 2026. The first joint public appearance is scheduled for The Podcast Show London on May 21, 2026.

Where: Sounds Profitable is based in Boston, Massachusetts. The Podcast Show London takes place in London. The Partner Network operates globally, spanning nearly 210 organizations across the audio and advertising industries.

Why: Podcast appearances now travel across YouTube, editorial coverage, and AI-powered search results – expanding the measurement surface beyond traditional listener data. Muck Rack’s platform tracks how brands appear across all of these channels, including in AI-generated answers. According to Sounds Profitable’s own research, 71% of podcast creators now produce video content, meaning a single brand appearance can reach multiple audiences and platforms simultaneously. As podcast advertising spending reached $2.9 billion in the United States in 2025 and monthly listenership hit a record 58% of Americans, PR professionals are under increasing pressure to account for podcasting within mainstream communications measurement frameworks.

By

Luís Rijo is a seasoned marketing professional with over 10 years of experience in Digital Marketing, Search, Social, Display, Video, and DOOH. Based in Europe. Also writing in the spend. Reach out via [email protected]

Sourced from PPC LAND

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

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

 

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.

 

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

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

By 

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.

By 

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

By 

  • 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 AmazonMeta, 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

By 

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

Sourced from Fortune

By 

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

By 

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.

Sourced from Fortune

 

“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

 

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.

Sourced from Futurism

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

Sourced from PCWorld