The Dimmer Switch Era—Why "Always Off" is the New Strategic Default
Ben Gordon
April 24, 2026
16
minutes read
Digital advertising is still growing. That much is not in dispute. US internet ad revenue hit $258.6 billion in 2024, up nearly 15% year on year, and IAB's 2025 outlook forecast another 5.7% rise, with retail media, CTV and social leading the charge. The money, in other words, continues to arrive. What is harder to sustain is the belief that spending it well has become any easier.
For the better part of a decade, the industry's answer to almost every strategic question was the same: always on. Stay visible, stay present, keep the machinery turning. It was a logic that belonged to a younger, simpler internet—one where scale could still pass for strategy and nobody asked too many questions about where the impressions were actually landing. That era is over, or ought to be.
Buyers now operate in a landscape of fragmented signals, inconsistent quality, opaque mechanics and an expanding tide of AI-generated clutter. EMARKETER's 2026 programmatic forecast captures the prevailing mood rather well: AI is already reshaping traffic flows, curated deals are redrawing the terms of trade, and the tension between automation and transparency is becoming impossible to wave away.
Why “always on” belongs to a different era
Which is why "always on" deserves to be retired. Not because brands should vanish from the market, but because indiscriminate presence has become a liability. The smarter default, as Chris Kane sees it, is "always off." Kane—founder of Jounce Media and one of programmatic's most rigorous supply-chain researchers—does not mean permanently off. He means strategically off. Off until a context, an audience, a moment has proved it warrants the spend. It is a quiet but important inversion: reach is not something to maximize for its own sake but a variable to be weighed, while presence (so often treated as a given) is really a privilege that has to be justified.
On and off gets you so far, and that is further than most advertisers currently manage. But the more useful analogy is a dimmer. Once the default is off, the work becomes finer-grained: where to turn things up, where to ease them back, and where to go dark altogether. Live sports, breaking cultural moments, high-intent contextual placements—these still justify intensity. But the mere existence of inventory does not entitle it to budget. A growing share of competent programmatic strategy now consists, in essence, of the discipline to say no.
The dimmer switch model
The market, to its credit, is already heading in this direction. At Programmatic I/O 2025, AdExchanger framed one of the conference's central debates in characteristically blunt terms: "Curation Is Eating The Supply Chain." Sell-side packaging is no longer a niche concern. It is becoming the operational reality of how a large and growing share of programmatic inventory changes hands. Basis makes a complementary argument in its 2026 trends analysis, noting that rising scrutiny around media quality, AI-generated content and shifting discovery behavior is pushing the sector into a more disciplined phase of growth—whether it wants to be there or not.
None of this would matter much if cheap scale still worked. But the economics of low-quality reach are getting harder to defend. The original promise of automation was efficiency by default. The lived reality, for many brands, is a persistent quality tax—budget flowing into environments that are cheap, abundant and technically available, but commercially inert.
ANA's Q2 2025 Programmatic Transparency Benchmark makes the point with uncomfortable clarity: the TrueCPM Index fell from 37.8 to 36.5%, even as paid CPMs held relatively steady. Put plainly, a smaller share of every dollar spent was translating into benchmark-qualified media value. Cheap supply is not always cheap once you account for what it fails to do.
The real cost of the quality tax
There is, however, an encouraging counterweight. The same ANA benchmarking program shows that when advertisers tighten quality governance, inspect their supply paths with greater seriousness and cut waste with genuine intent, the economics improve markedly. ANA's Q3 2025 benchmark reported that marketers reclaimed $13.6 billion in media value and lifted working-media share to 47.1%. Its Q4 release went further: quality-led advertisers converted 56.7% of programmatic spend into benchmark-qualified impressions. This is the part too many industry debates neglect. Selectivity is not a matter of taste or principle. It is a matter of money.
Pic. By Q3 2025, web and mobile programmatic working media had risen to 47.1%, showing how transparency and lower MFA exposure can materially improve media efficiency. (Source).
The trouble is that the waste problem has evolved. Made-for-advertising sites were one warning sign. "AI slop" is the next—low-effort pages, synthetic publishing operations, content farms engineered to absorb traffic rather than hold attention—and it now sits inside the bidstream as a permanent feature.
Because AI is simultaneously reshaping how users discover content, the market is being squeezed from both ends: weaker traffic quality in certain environments, and greater pressure to demonstrate value in the placements that remain. More inventory does not resolve this. Better filtering does.
Pic. From discovery economy to selection economy.
Curation is becoming the strategy
This is why curation ought not to be confused with a prettier word for blocklisting. A blocklist is defensive. Curation, done properly, is selective inclusion—a way of deciding which environments have earned access to budget and which have not. It sounds obvious, but the industry spent years behaving as though optionality itself were the objective. It is not. More choice without better judgement simply creates more sophisticated ways to waste money. The genuine differentiator is not access to everything. It is the confidence to buy less, and to buy better.
None of which works without transparency. If buyers cannot see where their money is going, how inventory is being packaged, what intermediaries are extracting from the chain, or how quality is being defined, then "premium" becomes an article of faith. And faith, it should be said, is a weak operating model.
Transparency has moved well past its years as a conference talking point. It is now a precondition for efficient buying. In a market governed by automated decisions, buyer trust depends increasingly on whether those decisions can be inspected after the fact.
Open Garden and the return of buyer control
This is where an Open Garden model begins to look less like a philosophical stance and more like a practical necessity. The logic is straightforward enough: brands need cross-platform visibility, DSP-agnostic execution, and supply decisions that serve commercial outcomes rather than platform incentives. AI Digital’sOpen Gardenis built around precisely that principle—transparent media execution, curated premium supply and an intelligence layer designed to unify planning, optimization and reporting across the digital landscape.
That intelligence layer matters because selective buying is considerably harder than broad buying. It demands richer context, faster synthesis and sharper judgement across fragmented environments. Elevate exists to address that need. Its purpose is not simply to automate decisions but to connect research, planning, optimization and reporting in a single place, so that buyers can act on cross-channel signals with greater speed and clarity. In a dimmer-switch market, that is the difference between possessing data and possessing direction.
Outcome-driven intelligence after the click
The same logic extends to measurement. The industry is moving towards a post-click standard of accountability, but that should not mean swapping one simplistic metric for another. Attention matters, yes, but IAB and the MRC have been clear that attention signals require methodological rigor and must not be mistaken for business outcomes in their own right. IAB is making a parallel push on marketing mix modelling, calling for more timely, more transparent and more decision-ready MMM in a fragmented media environment. The implication is plain: impressions are activity, attention is a signal, and outcomes are the standard.
That is the real shift underway. The future does not belong to brands that remain visible everywhere by default. It belongs to those that know when to press harder, when to hold back, and when absence is the better strategic choice. In that sense, "always off" is not a retreat from modern media. It is a more grown-up way of using it.
AI Digital helps brands make that shift—with transparent, DSP-agnostic media execution, curated supply strategies, and intelligence that connects planning, optimization and reporting around real business outcomes.
If you'd like to think through these shifts together, I'd welcome the conversation. Get in touch.
Blind spot
Key issues
Business impact
AI Digital solution
Lack of transparency in AI models
• Platforms own AI models and train on proprietary data • Brands have little visibility into decision-making • "Walled gardens" restrict data access
• Inefficient ad spend • Limited strategic control • Eroded consumer trust • Potential budget mismanagement
Open Garden framework providing: • Complete transparency • DSP-agnostic execution • Cross-platform data & insights
Optimizing ads vs. optimizing impact
• AI excels at short-term metrics but may struggle with brand building • Consumers can detect AI-generated content • Efficiency might come at cost of authenticity
• Short-term gains at expense of brand health • Potential loss of authentic connection • Reduced effectiveness in storytelling
Smart Supply offering: • Human oversight of AI recommendations • Custom KPI alignment beyond clicks • Brand-safe inventory verification
The illusion of personalization
• Segment optimization rebranded as personalization • First-party data infrastructure challenges • Personalization vs. surveillance concerns
• Potential mismatch between promise and reality • Privacy concerns affecting consumer trust • Cost barriers for smaller businesses
Elevate platform features: • Real-time AI + human intelligence • First-party data activation • Ethical personalization strategies
AI-Driven efficiency vs. decision-making
• AI shifting from tool to decision-maker • Black box optimization like Google Performance Max • Human oversight limitations
• Strategic control loss • Difficulty questioning AI outputs • Inability to measure granular impact • Potential brand damage from mistakes
Managed Service with: • Human strategists overseeing AI • Custom KPI optimization • Complete campaign transparency
Fig. 1. Summary of AI blind spots in advertising
Dimension
Walled garden advantage
Walled garden limitation
Strategic impact
Audience access
Massive, engaged user bases
Limited visibility beyond platform
Reach without understanding
Data control
Sophisticated targeting tools
Data remains siloed within platform
Fragmented customer view
Measurement
Detailed in-platform metrics
Inconsistent cross-platform standards
Difficult performance comparison
Intelligence
Platform-specific insights
Limited data portability
Restricted strategic learning
Optimization
Powerful automated tools
Black-box algorithms
Reduced marketer control
Fig. 2. Strategic trade-offs in walled garden advertising.
Core issue
Platform priority
Walled garden limitation
Real-world example
Attribution opacity
Claiming maximum credit for conversions
Limited visibility into true conversion paths
Meta and TikTok's conflicting attribution models after iOS privacy updates
Data restrictions
Maintaining proprietary data control
Inability to combine platform data with other sources
Amazon DSP's limitations on detailed performance data exports
Cross-channel blindspots
Keeping advertisers within ecosystem
Fragmented view of customer journey
YouTube/DV360 campaigns lacking integration with non-Google platforms
Black box algorithms
Optimizing for platform revenue
Reduced control over campaign execution
Self-serve platforms using opaque ML models with little advertiser input
Performance reporting
Presenting platform in best light
Discrepancies between platform-reported and independently measured results
Consistently higher performance metrics in platform reports vs. third-party measurement
Fig. 1. The Walled garden misalignment: Platform interests vs. advertiser needs.
Key dimension
Challenge
Strategic imperative
ROAS volatility
Softer returns across digital channels
Shift from soft KPIs to measurable revenue impact
Media planning
Static plans no longer effective
Develop agile, modular approaches adaptable to changing conditions
Brand/performance
Traditional division dissolving
Create full-funnel strategies balancing long-term equity with short-term conversion
Capability
Key features
Benefits
Performance data
Elevate forecasting tool
• Vertical-specific insights • Historical data from past economic turbulence • "Cascade planning" functionality • Real-time adaptation
• Provides agility to adjust campaign strategy based on performance • Shows which media channels work best to drive efficient and effective performance • Confident budget reallocation • Reduces reaction time to market shifts
• Dataset from 10,000+ campaigns • Cuts response time from weeks to minutes
• Reaches people most likely to buy • Avoids wasted impressions and budgets on poor-performing placements • Context-aligned messaging
• 25+ billion bid requests analyzed daily • 18% improvement in working media efficiency • 26% increase in engagement during recessions
Full-funnel accountability
• Links awareness campaigns to lower funnel outcomes • Tests if ads actually drive new business • Measures brand perception changes • "Ask Elevate" AI Chat Assistant
• Upper-funnel to outcome connection • Sentiment shift tracking • Personalized messaging • Helps balance immediate sales vs. long-term brand building
• Natural language data queries • True business impact measurement
Open Garden approach
• Cross-platform and channel planning • Not locked into specific platforms • Unified cross-platform reach • Shows exactly where money is spent
• Reduces complexity across channels • Performance-based ad placement • Rapid budget reallocation • Eliminates platform-specific commitments and provides platform-based optimization and agility
• Coverage across all inventory sources • Provides full visibility into spending • Avoids the inability to pivot across platform as you’re not in a singular platform
Fig. 1. How AI Digital helps during economic uncertainty.
Trend
What it means for marketers
Supply & demand lines are blurring
Platforms from Google (P-Max) to Microsoft are merging optimization and inventory in one opaque box. Expect more bundled “best available” media where the algorithm, not the trader, decides channel and publisher mix.
Walled gardens get taller
Microsoft’s O&O set now spans Bing, Xbox, Outlook, Edge and LinkedIn, which just launched revenue-sharing video programs to lure creators and ad dollars. (Business Insider)
Retail & commerce media shape strategy
Microsoft’s Curate lets retailers and data owners package first-party segments, an echo of Amazon’s and Walmart’s approaches. Agencies must master seller-defined audiences as well as buyer-side tactics.
AI oversight becomes critical
Closed AI bidding means fewer levers for traders. Independent verification, incrementality testing and commercial guardrails rise in importance.
Fig. 1. Platform trends and their implications.
Metric
Connected TV (CTV)
Linear TV
Video Completion Rate
94.5%
70%
Purchase Rate After Ad
23%
12%
Ad Attention Rate
57% (prefer CTV ads)
54.5%
Viewer Reach (U.S.)
85% of households
228 million viewers
Retail Media Trends 2025
Access Complete consumer behaviour analyses and competitor benchmarks.
Identify and categorize audience groups based on behaviors, preferences, and characteristics
Michaels Stores: Implemented a genAI platform that increased email personalization from 20% to 95%, leading to a 41% boost in SMS click through rates and a 25% increase in engagement.
Estée Lauder: Partnered with Google Cloud to leverage genAI technologies for real-time consumer feedback monitoring and analyzing consumer sentiment across various channels.
High
Medium
Automated ad campaigns
Automate ad creation, placement, and optimization across various platforms
Showmax: Partnered with AI firms toautomate ad creation and testing, reducing production time by 70% while streamlining their quality assurance process.
Headway: Employed AI tools for ad creation and optimization, boosting performance by 40% and reaching 3.3 billion impressions while incorporating AI-generated content in 20% of their paid campaigns.
High
High
Brand sentiment tracking
Monitor and analyze public opinion about a brand across multiple channels in real time
L’Oréal: Analyzed millions of online comments, images, and videos to identify potential product innovation opportunities, effectively tracking brand sentiment and consumer trends.
Kellogg Company: Used AI to scan trending recipes featuring cereal, leveraging this data to launch targeted social campaigns that capitalize on positive brand sentiment and culinary trends.
High
Low
Campaign strategy optimization
Analyze data to predict optimal campaign approaches, channels, and timing
DoorDash: Leveraged Google’s AI-powered Demand Gen tool, which boosted its conversion rate by 15 times and improved cost per action efficiency by 50% compared with previous campaigns.
Kitsch: Employed Meta’s Advantage+ shopping campaigns with AI-powered tools to optimize campaigns, identifying and delivering top-performing ads to high-value consumers.
High
High
Content strategy
Generate content ideas, predict performance, and optimize distribution strategies
JPMorgan Chase: Collaborated with Persado to develop LLMs for marketing copy, achieving up to 450% higher clickthrough rates compared with human-written ads in pilot tests.
Hotel Chocolat: Employed genAI for concept development and production of its Velvetiser TV ad, which earned the highest-ever System1 score for adomestic appliance commercial.
High
High
Personalization strategy development
Create tailored messaging and experiences for consumers at scale
Stitch Fix: Uses genAI to help stylists interpret customer feedback and provide product recommendations, effectively personalizing shopping experiences.
Instacart: Uses genAI to offer customers personalized recipes, mealplanning ideas, and shopping lists based on individual preferences and habits.
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