Slowing down becomes marketers' secret weapon in the AI speed race

The advertising industry has spent 2025 accelerating toward automation at unprecedented velocity. LiveRamp introduced agentic orchestration on October 1. Adobe released AI agents on October 9. Amazon launched Ads Agent on November 11. McKinsey data shows $1.1 billion in equity investment flowed into agentic AI during 2024, with job postings increasing 985% year-over-year.

Yet on December 29, 2025, marketing entrepreneur Dan Koe published a statement that directly contradicts this acceleration imperative: "When everyone has an advantage, it is no longer an advantage. When everyone can learn and create anything at the click of a button, your advantage comes from slowing down, focusing on your craft, doing the right things manually, and acquiring knowledge that is so specific to you that nobody can generate it with AI."

The same day, Luiza Jarovsky, PhD, co-founder of aitechprivacy.com, reached the same conclusion. "The rise of human excellence is here and, it's NOT one click away. It still takes time, effort, discipline, etc," she wrote in a post. Her analysis continued: "Due to cheap AI access and how easily people can fake expertise, the bar actually got higher."

These convergent observations articulate something marketing professionals increasingly sense intuitively: speed has become commoditized. The competitive advantage no longer resides in executing faster than competitors—AI tools provide that capability universally. Instead, differentiation emerges from deliberate deceleration, from choosing when not to automate, from investing time in understanding problems that cannot be solved through velocity alone.

The paradox manifests throughout digital advertising operations. Platforms promise efficiency gains through automation while simultaneously creating environments where speed produces diminishing returns. Meta's Advantage+ automation demonstrates this tension acutely.

Meta unveiled its Andromeda retrieval engine on December 2, 2024, describing the system as delivering "a step-function improvement in value" for advertisers. The platform processes tens of millions of ad candidates and narrows them to thousands of relevant options within strict latency constraints. Built specifically for Nvidia Grace Hopper Superchips, the system achieved a 6% recall improvement and delivered an 8% ads quality improvement on selected segments.

Yet digital marketing specialist Bram Van der Hallen challenged what he characterized as "absolute nonsense" surrounding Andromeda-style campaign consolidation in a November 27, 2025 LinkedIn post. Van der Hallen recommended that marketing professionals avoid converting setups overnight despite automation promises. "Your testing roadmap can absolutely include 'Andromeda' setups, just don't let yourself be influenced too heavily by the current hype," he wrote.

The skepticism stems from fundamental transparency problems. The lack of visibility makes it impossible to diagnose whether performance drops stem from creative fatigue, audience saturation, competitive pressure, or algorithmic changes. Advertisers cannot determine true incrementality versus harvesting low-hanging fruit. Harvard Business School research identified five pitfalls specific to AI marketing automation: people blame AI first when things go wrong; when one AI fails, people lose faith in others; people place more blame on companies that overstate AI capabilities; people judge humanized AI more harshly; and people feel outraged by deceptive AI practices.

This environment validates Koe's prescription about slowing down. When automation operates as a black box, speed provides no strategic advantage. Marketing professionals must instead invest time understanding causal mechanisms, testing hypotheses systematically, and building knowledge about how their specific businesses respond to different optimization approaches—precisely the manual craft work that Koe described.

The measurement crisis reinforces this dynamic. Research released October 21, 2025, by TransUnion and EMARKETER surveyed 196 marketing professionals and found that 54.1% reported no change in their measurement confidence compared to the previous year, while 14.3% said confidence actually declined.

Most marketers—61.7%—maintain confidence in their performance metrics. But that confidence has stopped growing at a time when tools have improved and data has become more abundant. "Marketers have access to more data than ever before, yet effective and trustworthy measurement is getting harder—not easier—to come by," according to Brian Silver, Executive Vice President at TransUnion.

Siloed and incomplete data emerged as the primary culprit. The research found 49.5% of respondents cited fragmented data as the main reason they question measurement accuracy. Cross-channel deduplication issues affected 48% of marketers, while 40.8% pointed to walled-garden reporting limitations as barriers to accurate measurement.

Additional research released December 2, 2025, by Funnel and Ravn Research found that 86% of in-house marketers and 79% of agency marketers struggle to determine the impact of each marketing channel on overall performance despite unprecedented access to analytics tools and measurement infrastructure.

The contradiction stands stark: data flows from hundreds of platforms while actionable insights remain elusive. Speed of data collection provides no competitive advantage when that data cannot be synthesized into strategic understanding. Marketing professionals must instead slow down to analyze fragmented information sources, reconcile conflicting signals, and develop coherent frameworks for interpreting performance—exactly the manual analytical work that resists automation.

Separate research released September 4, 2025, surveyed 200 chief marketing officers across five major markets and found that 45% of marketing data used for business decisions is incomplete, inaccurate, or outdated. No single CMO considered their data more than 75% reliable.

When questioned about factors that would most improve marketing performance, 30% of respondents identified data quality improvements as their primary focus. This response significantly exceeded automation of data workflows at 22% and improved data democratization at 21%. Despite rapid advancement in artificial intelligence analytics tools, marketing leaders recognize that sophisticated algorithms cannot compensate for fundamental data deficiencies.

This priority ranking demonstrates precisely what Koe described: slowing down to address foundational quality problems delivers more competitive advantage than accelerating through additional automation layers. The manual work of data cleaning, validation, reconciliation, and systematic quality improvement cannot be automated away—it requires human judgment about what constitutes reliable information for specific business contexts.

The platform economics driving content creation demonstrate similar dynamics. Major social media platforms actively fund AI content production through creator monetization programs. TikTok's Creator Fund offers payments between $0.02 and $0.04 per 1,000 views. While these rates appear modest individually, creators discovered that AI tools enable production of hundreds of videos with minimal time investment, scaling earnings through volume rather than quality.

This economic structure incentivizes what industry experts term "AI slop"—low-quality, mass-produced artificial intelligence-generated content designed primarily to capture engagement metrics. According to analysis from a June 23, 2025 HBO Last Week Tonight segment, "Not all AI content is spam, but right now, all spam is AI content."

The velocity enabled by AI tools produces diminishing returns. Content created at maximum speed saturates platforms, reduces individual piece visibility, and undermines audience trust. Research conducted by Raptive and published July 15, 2025, surveyed 3,000 U.S. adults and documented that suspected AI-generated content reduces reader trust by nearly 50%. The study revealed a 14% decline in both purchase consideration and willingness to pay premiums for products advertised alongside content perceived as artificially created.

Anna Blender, Senior Vice President of Data Strategy & Insights at Raptive, highlighted the study's most counterintuitive finding: "When people thought something was AI-generated, they rated that content much worse across metrics like trust and authenticity, regardless of whether it was really AI generated or not."

This perception problem validates Koe's framework about manual craft. Speed of production provides no advantage when audiences discount the output as low-quality or inauthentic. Marketing professionals must instead invest time in deliberate content development—research, original analysis, authentic voice cultivation, quality refinement—that cannot be compressed through automation tools.

The operational challenges facing marketing teams reinforce deceleration benefits. Digital marketing specialist John Ho's analysis of Meta advertising campaigns revealed that identical structural problems appear consistently from startup ventures to global enterprise brands, despite the platform's continuous algorithm improvements and expanded automation features.

Visual content management presents widespread issues affecting campaign performance metrics. The audit findings indicate that many campaigns operate without manually selected thumbnails, resulting in automatically generated frames that often appear blurry or unprofessional. These poor-quality visual representations directly impact click-through rates across campaign elements.

The solution involves manual thumbnail selection focusing on clean, product-first imagery that accurately represents the advertised offering. This approach ensures visual consistency and professional presentation across all campaign touchpoints. Speed of automation produces inferior results compared to deliberate manual curation—precisely the dynamic Koe described about doing "the right things manually."

Commerce media network maturity demonstrates similar patterns. Research released November 19, 2025, assessed 788 decision-makers and found that while 42% describe their operations as operationalized or fully advanced, only 13% meet criteria for "trailblazers" across strategy, technology, measurement, and operations.

Networks that centralize and automate campaign management accelerate time to market and unlock efficiency. However, most still rely on manual creative approvals and have disconnected tech stacks and uncoordinated workflows. Only 12% can seamlessly activate and measure campaigns across onsite, offsite, and in-store environments, exposing operational and data silos that limit omnichannel maturity.

The research identifies a substantial gap between perceived and actual operational sophistication. Organizations with formalized programs often lack the integration, automation, and measurement capabilities required to operate at scale. This suggests that rushing toward automation without addressing foundational infrastructure problems creates illusions of sophistication while delivering limited strategic advantage.

Slowing down to build proper integration, establish measurement frameworks, and develop operational capabilities produces better outcomes than accelerating toward automation with inadequate foundations. This validates Koe's emphasis on "focusing on your craft"—the unsexy manual work of building proper systems infrastructure that enables effective automation later.

The incrementality measurement framework released November 3, 2025, by IAB and IAB Europe further demonstrates why deceleration matters. The guidelines categorize incrementality methods into four distinct types, each assessed for causal reliability and business applications.

Experiment-based approaches including randomized control tests represent the gold standard for proving causal lift. Yet these methods require significant time investment—weeks or months for proper test design, execution, and analysis. Speed-optimized hybrid proxies offer quick directional validation but the guidelines classify these as weak in causal strength with narrow scope and substantial bias risks.

Marketing professionals face a fundamental choice: execute quickly with unreliable measurement or invest time in rigorous testing that produces actionable insights. The framework explicitly recommends slower, more methodologically sound approaches over rapid but unreliable alternatives. This recommendation directly reflects Koe's prescription about slowing down to focus on craft and doing things manually when that produces superior strategic understanding.

The manual work Koe references extends beyond campaign execution into strategic planning. When everyone has access to the same AI tools, differentiation emerges from asking better questions, developing more sophisticated hypotheses, and building contextual knowledge about specific business environments that cannot be generated through pattern matching algorithms.

Koe's statement emphasized "acquiring knowledge that is so specific to you that nobody can generate it with AI." This specificity requirement means marketing professionals must develop expertise grounded in direct experience with particular industries, customer segments, platform behaviors, or competitive dynamics.

A marketing professional developing genuine expertise in attribution modeling for subscription businesses cannot shortcut that learning through AI queries. The knowledge emerges from sustained engagement with problems—observing how different cohorts behave across various subscription tiers, understanding which touchpoints influence retention versus acquisition, recognizing how seasonality affects renewal patterns, identifying where second-order consequences emerge from pricing changes.

This accumulated pattern recognition requires time investment that resists compression. Slowing down to observe, analyze, test, fail, correct, and gradually build understanding produces knowledge that AI systems cannot replicate through statistical prediction over aggregate training data.

The authenticity crisis affecting consumer trust validates this approach. Research on AI-generated reviews documented extraordinary growth rates. Analysis found Shein's AI-generated review percentage increased from 0.51% in 2022 to 6.61% by 2024, representing approximately 1,280% growth. Temu's trajectory proved even more compressed, with AI reviews jumping from 0.75% in 2022 to 10.90% in 2025.

The timing correlates precisely with generative AI availability. ChatGPT launched in November 2022, followed by widespread adoption throughout 2023. This technological availability coincided directly with sharp increases observed in AI-generated review data.

When significant portions of product reviews utilize AI generation rather than authentic customer experiences, the informational value supporting purchase decisions deteriorates. Marketing strategies relying on customer advocacy face challenges when automated content dilutes genuine feedback signals. Speed of review generation provides no advantage when audiences discount the content as inauthentic.

The solution requires slowing down: conducting genuine customer research, collecting authentic feedback through proper survey methodologies, analyzing real behavioral data, and developing insights grounded in actual customer experiences rather than AI-generated approximations. This manual research work cannot be automated without sacrificing the authenticity that provides competitive differentiation.

YouTube's policy clarifications announced July 15, 2025, addressed precisely this distinction. The platform renamed its "repetitious content" guideline to "inauthentic content" while maintaining established enforcement criteria. YouTube emphasized that authentic content creation using AI tools remains acceptable while mass-produced spam content continues to be prohibited.

The differentiation between thoughtful tool usage and wholesale automation reflects Koe's framework. AI tools can accelerate specific tasks within deliberate creative processes without replacing the human judgment that determines what deserves creation, how it should be structured, what audience needs it addresses, and why it merits attention. The speed enabled by tools provides value only when guided by strategic thinking developed through careful observation—thinking that requires slowing down rather than accelerating.

Koe's prescription centers on counterintuitive deceleration: "When everyone can learn and create anything at the click of a button, your advantage comes from slowing down, focusing on your craft, doing the right things manually."

This approach directly opposes the velocity incentives built into platform economics, creator monetization structures, and AI tool marketing narratives. Yet the evidence from measurement confidence stagnation, data quality crises, operational maturity gaps, and consumer trust erosion consistently validates deceleration as strategic advantage.

Marketing professionals who invest time understanding causal mechanisms develop judgment that AI systems cannot replicate. Those who slow down to build proper measurement infrastructure gain insights that remain opaque to competitors racing through automated optimization. Practitioners who deliberately practice core skills through manual execution maintain capabilities that atrophy under wholesale automation.

The knowledge that emerges from sustained engagement with particular problems—observing patterns, testing hypotheses, experiencing failures, making corrections, gradually accumulating contextual understanding—cannot be generated through AI queries or compressed through automation tools. This accumulated expertise represents exactly what Koe described as "knowledge that is so specific to you that nobody can generate it with AI."

Jarovsky's parallel observation reinforces the dynamic from a different angle: "Due to cheap AI access and how easily people can fake expertise, the bar actually got higher. People will have to prove that they know what they say they know AND that they aren't AI."

The proof requirement creates pressure for authentic demonstration of genuine expertise. Marketing professionals cannot simply deploy AI tools and expect audiences to accept the output as valuable. They must instead provide evidence of authentic mastery—the kind that emerges only through sustained, deliberate practice over time.

This evidence manifests through depth of analysis, sophistication of strategic frameworks, accurate prediction of second-order consequences, and recognition of contextual nuances that affect how general principles apply to specific situations. None of these capabilities can be demonstrated through fast execution. They emerge only from slow, deliberate engagement with problems that resist compression.

The advertising industry's rush toward automation throughout 2025 has created an environment where everyone possesses the same technological capabilities. In this environment, as Koe stated, "when everyone has an advantage, it is no longer an advantage."

Differentiation shifts from tool access to tool application, from execution speed to judgment quality, from automation capability to strategic sophistication. These advantages emerge from slowing down, focusing on craft, doing things manually when that produces superior understanding, and accumulating knowledge specific enough that it cannot be replicated through pattern matching algorithms.

The convergence of Koe and Jarovsky's independent observations on December 29, 2025, marks a moment when the implications of AI democratization became clear: speed has been commoditized, and the new competitive advantage resides in deliberate deceleration.

Timeline

• November 2022: ChatGPT launches, initiating widespread AI content generation capabilities
• June 23, 2025: HBO Last Week Tonight highlights platform monetization driving AI slop epidemic
• July 15, 2025: YouTube clarifies inauthentic content policy for AI-generated material
• July 15, 2025: Raptive study shows AI content cuts reader trust by 50%
• September 4, 2025: Research reveals 45% of marketing data is incomplete or inaccurate
• September 27, 2025: Meta advertising expert reveals common campaign mistakes killing performance
• October 1, 2025: LiveRamp introduces agentic AI tools for marketing automation
• October 9, 2025: Adobe releases AI agents targeting B2B workflows
• October 21, 2025: TransUnion research shows marketing measurement confidence stalls despite data growth
• November 3, 2025: IAB releases measurement framework for commerce media
• November 11, 2025: Amazon launches AI agent for automated campaign management
• November 19, 2025: Commerce media maturity research shows only 13% are trailblazers
• November 27, 2025: Meta's AI automation draws skepticism from advertisers
• December 2, 2025: Funnel research reveals 86% of marketers cannot determine channel impact
• December 2024: AI-generated reviews surge over 1,000% on major platforms
• December 29, 2025: Dan Koe and Luiza Jarovsky independently publish observations on slowing down as competitive advantage

Summary

Who: Dan Koe, co-founder of Kortex building Eden AI canvas, and Luiza Jarovsky, PhD, co-founder of aitechprivacy.com, independently articulated how universal AI access makes deliberate deceleration the new competitive advantage for marketing professionals navigating automation-saturated environments.

What: Both observers identified that slowing down, focusing on manual craft, and acquiring specialized contextual knowledge provides differentiation when AI tools democratize execution speed, requiring marketers to invest time in foundational work like data quality improvement, rigorous measurement, and authentic expertise development rather than rushing toward automation.

When: December 29, 2025, marked convergent publication of independent observations about deceleration as strategic advantage, occurring amid widespread AI adoption throughout 2025 while research simultaneously documented measurement confidence stagnation, data quality crises affecting 45% of business decisions, and operational maturity gaps despite automation investments.

Where: The observations emerged from social media platforms and affect global marketing operations where platforms including Meta, Amazon, Adobe, and LiveRamp deployed agentic AI capabilities while marketers reported deteriorating measurement confidence, fragmented data infrastructure, and declining consumer trust in AI-generated content.

Why: Universal AI tool access commoditized execution speed while creating environments where acceleration produces diminishing returns through measurement opacity, data quality problems, operational infrastructure gaps, and consumer skepticism toward AI content, forcing marketers to differentiate through deliberate deceleration that builds genuine expertise, rigorous measurement frameworks, and authentic knowledge that cannot be replicated through automation.