Rising CPCs aren’t just the result of more competition inside search auctions. They’re increasingly the result of what happens before anyone places a bid.

AI Overviews, shrinking organic click volume, and stronger brands competing for a smaller pool of commercial traffic have changed the economics of paid search. Improving bids and ad copy still matter, but the biggest opportunities now sit outside the auction itself.

Why paid search keeps getting more expensive

Paid search costs are climbing across almost every category. The cross-industry average CPC is $5.42, per the latest WordStream benchmarks, more than double what it was a decade ago. 

Stackmatix has Google Search up 14-18% year over year. LinkedIn is up 18 to 22%. Some accounts are seeing 25% inflation in their primary commercial keywords.

For most of the last decade, organic search helped offset PPC costs. Today, AI Overviews absorb the clicks that used to keep your paid search efficient. 

The latest zero-click study from Sparktoro shows an 8% reduction in clicks through from search engines vs. 2025, further impacting brands whose users now get information from AI summaries. 

Digiday’s research with brand and agency professionals shows 37% of respondents have already seen informational search traffic decline, which mirrors what we’re seeing across our client base, putting more pressure on navigational and transactional traffic to hold steady.

The number of advertisers participating in search auctions has risen 35% year over year because AI creative tools have lowered the barrier to entry for new bidders. In its first year, AI Max for Search has expanded query space for advertisers willing to use it, but also concentrated competition into a narrower set of commercial queries.

Fewer organic clicks reaching your site means more pressure to make up the gap in paid auctions. More advertisers fighting for those auctions means higher CPCs. And the auctions that still resolve to a click are increasingly the only ones where a user has exited the AI summary and chosen to scroll past it.

Dig deeper: The authority era: How AI is reshaping what ranks in search

3 levers that matter more than the auction

Paid search performance in 2026 is decided across three layers. The auction itself now offers the least opportunity to improve results.

1. Brand: Upstream of the click

This layer determines whether a click is generated or absorbed before any auction begins. It’s influenced by authority signals, brand mentions, AI Overview inclusion, LLM citations, and visibility in the publications and communities AI systems use to build their answers.

Most CPC inflation starts here. When AI Overviews answer more queries directly, the pool of clicks available to advertisers shrinks. The auction doesn’t get cheaper as a result. It gets more expensive because the same number of advertisers compete for fewer clicks.

As CPCs continue to rise, the brands protecting their margins are building visibility across multiple platforms. A stronger organic presence lets them rely less on paid search while remaining visible throughout the buying journey.

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2. Reach: At the click

The auction itself is influenced by ID strategy, match types, ad copy, automation, Smart Bidding configurations, and Performance Max guardrails.

This is where most paid teams still focus their efforts. It’s also the layer with the least leverage left because the size and quality of the click pool are determined upstream. The work still matters, but the opportunity to improve performance here is smaller than it used to be.

Red ocean vs. blue ocean media

The paid channels where most performance teams compete are becoming red oceans: saturated auctions where advertisers bid against one another for the same shrinking pool of commercial clicks.

Blue oceans are channels where buyer intent already exists, but advertiser competition hasn’t yet caught up.

Red ocean (where competition is fiercest)	Blue ocean (where intent exists but competition is lower)	Why the shift works
Google Search non-brand commercial keywords	Microsoft Advertising (Bing), including its growing share of AI Search ad surfaces	CPCs typically 20 to 40% lower, audience skews older and higher value, much less crowded
Standard LinkedIn Sponsored Content	LinkedIn Thought Leader Ads (peer-to-peer format from a real profile)	Around 1.7x higher CTR than company-page ads, lower effective CPC, better fit for considered purchases
Meta feed ads (broad demographic targeting)	Reddit Ads, community sponsorships, niche newsletter and Substack advertising	High-intent attention in environments LLMs cite, less saturated auction dynamics, more first-party engagement
Performance Max and Google display	Connected TV, BVOD, podcast advertising	Premium attention at the top of the funnel, fewer competing bidders, channel measurement is maturing fast
Branded search defense at any cost	AI Search and early ChatGPT ad inventory	First-movers are buying tomorrow’s primary discovery surface at today’s clearance prices

This isn’t an argument for abandoning Google Search. It’s a reminder not to overinvest your paid budget in the most competitive auctions. Instead, start shifting some of your existing budget into testing emerging channels and compare their performance with traditional search.

3. Experience: After the click

The post-click experience is an essential part of media economics, but most paid teams still treat it as something to hand off to another function.

Every other lever in this article governs what you pay to enter the auction. Experience determines what each click is worth after you’ve paid for it, and it’s the only one of the three layers you fully control.

In an inflating market, post-click conversion stops being a quarterly optimization exercise and becomes your primary defense against rising acquisition costs.

Google’s Ad Rank is calculated using your bid, your Quality Score, and the expected impact of assets. Landing page experience is one of the three components of Quality Score.

A higher Quality Score directly offsets the need to bid more aggressively. A stronger landing page can help you outrank a better-funded competitor while paying less per click.

That said, most expensive clicks don’t convert on the first visit, especially in B2B, where buying cycles are longer and purchases are more considered. The job of the experience layer is to convert and capture. Think first-party data, a reason to return, and an entry point into nurture and CRM.

The advertisers protecting their margins through this transition will stop treating media and landing pages as separate disciplines. They’ll manage them as a single P&L shared across paid media, CRO, UX, content, and lead nurture.

Dig deeper: The new SEO imperative: Building your brand

What winning paid search looks like now

Paid search accounts for a smaller share of where performance is earned, and it’s becoming a more expensive channel to operate if you haven’t built brand strength around it. Much of the work that makes paid search efficient now sits in disciplines paid search teams have historically considered someone else’s job.

The teams that protect their margins through this transition won’t be the ones with better targeting or bidding strategies. They’ll be the ones who’ve built enough visibility and authority outside the auction to win it when it matters.

Coordinate reach and frequency across campaigns while still maintaining individual campaign settings.

Not a week goes by without me reading about ChatGPT ads in my LinkedIn feed. I’m guilty of it, too. 

There’s so much happening so quickly. The product feed integrations, the Ads Manager beta, and the inevitable comparisons to Google’s dominance in search advertising. 

While it’s a compelling narrative for agencies looking to drive new business, it’s also short-sighted. The real shift isn’t advertising on LLMs like ChatGPT. It’s happening in checkout flows, voice assistants, and agentic commerce infrastructure, where the transaction itself becomes the ad unit. 

The brands quietly winning this next cycle are refining their product data to ensure they factor into AI-powered purchase decisions.

ChatGPT Ads is structurally weak

Referral traffic from ChatGPT to the rest of the web grew 206% in 2025, according to Semrush’s analysis of 17 months of U.S. clickstream data. That’s the headline most people stopped at.

What they didn’t read was the footnote: that growth is driven by deeper engagement from existing users, not by audience expansion. ChatGPT’s U.S. user base has been essentially flat since September 2025. The people who use it are using it more. But the addressable audience isn’t growing.

This obviously matters if you’re trying to build an advertising business. Ad revenue scales with reach, and reach requires a growing audience. Without new users coming in, you’re running the standard sequence (build an audience and then monetize at scale) backward. 

OpenAI’s leaked financials show another structural challenge: $13 billion in revenue against $34 billion in total costs and expenses in 2025, translating to an operating loss of nearly $21 billion. 

On the bright side, OpenAI spent $2.37 to generate every $1 in revenue in 2024. By 2025, that ratio had improved to $1.60 per dollar. Progress, but not nearly enough, or fast enough. It’s no surprise OpenAI postponed its IPO until next year.

To put those numbers in perspective, Amazon lost $30 million the year it went public. Google and Meta were already profitable before their IPOs. OpenAI isn’t playing in the same league. 

The scale of the bet is unlike anything the tech industry has attempted before. How long before investors pull back? Your guess is as good as mine.

See exactly how your competitors win.

Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.

Analyze your competitors

OpenAI’s master plan is agentic

Here’s what the ChatGPT Ads narrative misses: Ads are a defensive move, not a strategic vision. Sam Altman has always been against them. 

But OpenAI had to realize that to fund its dollar-hungry master plan, it needed something to help balance the books, at least partially.

So what’s that master plan? Look elsewhere for a clearer view.

At Google I/O 2026, Google introduced Universal Cart, building on Universal Commerce Protocol (UCP), infrastructure that lets AI agents complete purchases on your behalf. This isn’t a shopping tab redesign. It’s a transaction layer between intent and purchase, with Gemini deciding what gets recommended and bought. If you’re skeptical, remember you can already onboard UCP. This is happening today, not in some distant future.

It’s the same story at Amazon. The company combined Rufus, its expert shopping assistant used by more than 300 million customers in 2025, and Alexa+, its personalized AI assistant available across hundreds of millions of devices, into a unified experience called Alexa for Shopping. 

The result is a surface that enables customers to automate deal-finding and routine purchases based on personalized insights. Like Gemini, Alexa can complete the transaction, dramatically shortening the distance from ad impression to purchase.

It’s the same story at OpenAI. The company already has integrated product feeds in Ads Manager. You’d think it’s a basic feature because Google, Meta, and Amazon all have it. But that soon in a brand-new advertising platform? 

OpenAI launching product feed ads in 2026 isn’t an advertising innovation. It’s a platform reaching for familiar revenue streams while the more interesting architecture takes shape beneath the surface.

Dig deeper: Why product feeds need an organic strategy for AI search

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What this means if you’re buying paid media today

Most of the industry is asking the wrong question. The question isn’t, “Should I test ChatGPT Ads?” (Obviously, you should.) Or even, “Should I diversify beyond Google?” (Again, obviously you should.)

The right question is: “Is my product data ready for agentic commerce?”

Because when an AI agent — whether it’s Alexa, Google’s shopping agent, or whatever OpenAI builds next — makes a purchase recommendation on behalf of a user, it won’t pull from your campaign creatives. It’ll pull from your product feed. The cleanliness and completeness of that data will determine whether you exist in the recommendation.

This is the same transition we’ve lived through before, just at a different layer of the stack.

When Google moved from keywords to audiences to intent signals, the advertisers who won were the ones with cleaner conversion tracking and stronger first-party data foundations. When Meta moved to Advantage+ and black-box optimization, the winners built better creative systems.

It’s the same pattern here: Data is king. Agentic commerce applies the same dynamic to the transaction layer.

I know. The checklist isn’t glamorous. 

• Make sure your product feeds are complete, accurate, and updated in near real time. 
• Ensure your structured data — product attributes, availability, profitability, and more — is properly implemented across your catalog. 
• Invest in API integrations with the platforms building agentic infrastructure. 

Bottom line: Treat your product data the way you should’ve been treating your conversion tracking for the last decade. It’s a competitive advantage, not a maintenance task.

Dig deeper: Winning the AI decision layer: From AI discovery to agentic commerce

The real battle isn’t in the ad console

ChatGPT Ads will generate some revenue. It’ll attract some advertisers, produce some case studies, and exist as a line item in some media plans. That’s fine.

But it won’t be the next Google Ads. It won’t scale into a duopoly-level advertising machine. The audience ceiling is real, the cost structure is brutal, and the competitive moat — the thing that made Google’s search ads irreplaceable for 20 years — simply isn’t there.

The tidal wave isn’t in the ad console. It’s in the infrastructure being built around task completion, automated purchasing, and agent-to-agent commerce. Google and Amazon are already constructing it. The brands that show up there won’t do it through better bidding strategies. They’ll do it through better data. Start there.

Most paid media campaigns shouldn’t launch with the biggest budget you can afford. 

Spending aggressively before you’ve validated performance often leads to higher acquisition costs, slower optimization, and weaker stakeholder confidence when results fall short.

A phased rollout gives your campaigns time to generate meaningful data, improve bidding efficiency, and identify what’s working before you scale. 

Here’s why frontloading ad spend usually backfires, the few situations where it may make sense, and how to grow your budget without sacrificing long-term performance.

Fire bullets before cannonballs

For those of us who make a living driving growth through paid media, there’s one thing almost as bad as a tiny advertising budget: an advertiser who wants to spend too much, too soon.

Paid media launches should follow a plan. As Jim Collins wrote in “Great by Choice,” successful companies fire “bullets” first, learn from the results, and then fire “calibrated cannonballs” with greater confidence.

Most campaigns aren’t ready for a cannonball on day one. The algorithms are still learning, Quality Scores haven’t matured, and you don’t yet know which audiences, keywords, or creative will perform best. That’s when acquisition costs and inefficiencies tend to be highest.

There are exceptions. Occasionally, years of historical data or a high degree of confidence justify launching more aggressively. Those cases are rare.

More often, frontloading ad spend creates expensive lessons instead of faster growth. The following scenarios explain why companies make this decision, and why a measured rollout usually delivers better long-term results.

See exactly how your competitors win.

Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.

Analyze your competitors

Your budget isn’t a KPI

As a marketing principle, it’s safe to assume that the amount you spend on ads shouldn’t be confused with “performance” (despite Google’s opinion).

Street-smart, owner-operated companies typically start with careful ad budgets. It’s deep-pocketed intellectuals who are more likely to talk about how much they’re capable of spending.

In this context, intellectuals could mean high-ranking Fortune-something executives, venture capitalists, or even serial entrepreneurs suddenly flush with an unusually generous investment from a single backer.

When Nassim Taleb praises those with “skin in the game,” he’s urging us to empathize with people who bear the consequences of risk-taking. Risk asymmetry means splashy failures don’t always hurt the “intellectual class.”

Directly or indirectly, I’ve analyzed close to 1,000 ad accounts over the years. The pattern is clear: Advertisers who overspend early in pursuit of hypergrowth often flame out and lose stakeholder buy-in.

Dig deeper: PPC budgeting in 2026: When to adjust, scale, and optimize with data

4 examples of frontloading, and the cases against them

1. ‘It’s a land grab. Gaining market share quickly is our justification for aggressive early spending.’

While I rarely agree that it’s a prudent course of action, it’s worth understanding the motivation behind frontloaded ad spend strategies.

This is an all-out attempt to achieve market share and first-mover advantages before new entrants catch up. I can think of all kinds of examples in fast-moving customer acquisition environments for tech startups.

We once came on the scene to help a startup with a much-diminished, modest, incremental Google Ads campaign. What was shocking was how little they’d learned. And how little money they had left after raising more than $250 million. Nearly all of it had been burned, including large sums on ads. There wasn’t going to be more where that came from.

We helped them measure KPIs such as “new accounts that actually led to revenue” and “lifetime revenue from those accounts.” No one had seen fit to do this in three years, as nine figures in funding blazed relentlessly.

Even bootstrapped startups celebrating their first $1 million to $2 million in “real” venture funding can get carried away by the same logic. It’s so unnecessary.

We’ve helped numerous niche SaaS startups, such as Clio for legal practice management and SuccessFactors in HR management, achieve prominence.

Small beginnings and careful ad budgets don’t preclude unicorn status. Matching your customer acquisition budget to your stage of growth is entirely feasible. It isn’t a life sentence of smallness. 

Define your addressable market for initial paid growth efforts relatively tightly. Save the “huge addressable market” hype for conversations with larger investors who are viewing things over a longer time horizon.

As a helpful exercise, remind yourself how a behemoth like Uber got started. Its seed round was $1.25 million, valuing the company at a modest $4 million.

Feel free to think big. But don’t try to “act bigger than you are” with money and product-market fit you don’t yet have. Network effects and access to more capital will, if all goes well, accelerate growth once you’ve established a meaningful lead.

Why do founders sometimes get stars in their eyes and want to race through growth stages by lighting their newly raised, but finite, cash on fire? It could be because certain investors goad them into it. Or it could be because the team responsible for growth decided to party hearty with the money.

Eventually, the hangover hits. When investors see high churn rates and stratospheric CACs — or, worse yet, few tangible signs of customer acquisition of any kind — they squeal as if mortally wounded, even though they sort of asked for it in the first place.

Unit economics do matter. Other founders may have recently repealed the laws of economics, but as your mom once said, “If Billy jumped off a cliff, would you do it too?”

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2.  ‘We’ll learn faster’

It’s indisputable that predictive bidding algorithms perform poorly when conversion and value signals are sparse. More data helps them identify patterns associated with higher-value sessions.

Human teams also need to cycle through feedback loops to understand what works, what doesn’t, and how to iterate.

One example of faster learning is the quick discovery of necessary pools of negative keywords. Higher query volumes speed up that process, especially because lower volumes can keep many bad queries hidden in “Other Search Terms” for a long time.

But beyond a certain budget level, impatient spending becomes counterproductive.

• What if your sales cycle varies in length and typical order or deal value? If two or three months commonly pass between the first ad view and a sale, and you try to shoehorn too much budget into the first month, you’re still running ads blind, with little opportunity to iterate along the way. That can be an expensive lesson.
• Overspending can raise your own CPCs. Barging into ad auctions that have reached a certain equilibrium and overbidding aggressively could trigger competitors to bid higher, too.
• Your key metrics will typically be at their worst early on because you haven’t established Quality Scores in the ad platform yet. That means higher CPCs, all else being equal. The account for our “get spendy” client mentioned earlier recently saw CPCs drop by 80% between establishing Quality Scores and our optimizations. Good thing the initial pilot ran on a modest budget.

Investing a deluge of funds into the worst ROI environment your budget is ever likely to see defies logic. Even four to six weeks later, ROI is almost always substantially better based on Quality Score statistical confidence alone.

Dig deeper: Stop looking for the perfect PPC budget split

3. ‘We’re pre-revenue. With a hefty check our lead investor just sent over, we want a quick estimate of the market size to help us evaluate the investment hypothesis.’

What could possibly go wrong?

This takes the land-grab approach even further into the intellectual ether. No customers — or virtually any other outcome — seem to be the goal, at least for now.

One or two steps removed, the investors are telling you plainly: We don’t care if we spend a huge wad of cash in the first month. Just get us a pile of data.

When Mr. Big’s name comes up, we shrug and figure, “Billionaire knows best.” We dutifully throw money at a performance channel, don’t ask it to perform, and feel sad 35 days later when, you know what, the investor suddenly isn’t going to invest another penny, and the founder is left with no credible Plan B.

A new investor pops in with questions.

• “Q: What is the company, exactly? I mean, what product or service do you provide?”
• “A: We’re still figuring that out, but we know there must be a gold mine in there somewhere, given how many music fans are searching for [music examples redacted to protect the innocent].”

The project never truly launches because it was never defined in the first place.

To be fair, fail-fast market research can be a good idea. Over a short period, we once spent around $10,000 on ads for a client exploring a telecommunications business model. He got a definitive answer about demand patterns in his space and decided not to move forward in that vertical.

Google Ads is an invaluable tool for market research. But if you’re not using it in a disciplined way to measure a business outcome that requires potential customers to clear a meaningful hurdle of intent, why bother? Scratch that itch with the free Google Trends tool, Google Analytics on a content site you create, or Semrush. Or hire a market research company.

The key is to rein in waste in unusual situations like this. You can’t always eliminate it entirely.

4. ‘There’s a vendor who won’t work with us unless we spend more out of the gate’

Some ad platforms, and even third-party software tools or managed services, set steep minimums. Some advertisers are tempted to overspend to join these exclusive clubs out of FOMO. 

A timely example is the early days of the OpenAI ad pilot. Steep minimums and uncomfortably high CPMs seemed to rule out entry for the typical advertiser.

As you’ve probably gathered, I think wildly overpaying for each ad interaction is a bad idea. Don’t twist yourself into a pretzel trying to rationalize it. At some point, the market will come to you. Just look at how much easier it is to get started with StackAdapt in programmatic compared with Google DV360 and The Trade Desk.

If you’re small, grow first, and only step up to new levels when your company’s size and budget justify it. It’s a bit of the old The Millionaire Next Door logic. Buying a house you can’t afford or getting into a luxury car doesn’t make you rich. It might even prevent you from getting there.

Dig deeper: How to diagnose and fix the biggest blocker to PPC growth

Earn the right to scale

The common thread running through most frontloaded ad spending mistakes is that they kill buy-in. Why taint an entire channel, or your company’s growth function, by accelerating spend so quickly that you skid into the ditch? You’ll get farther once you’ve built solid traction.

If you’re a smaller business owner with skin in the game, it’s more than a buy-in problem. Nasty waste isn’t just bad optics — it can jeopardize your future.

So, when that overconfident investor or ad platform sales rep comes calling, urging you to go from “zero to sixty in 3.5,” it might be time to tap the brakes — or pray the airbags are functioning.

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