What the Alphabet Q2 Earnings Forecast Means for Advertisers

For a Q3 planning meeting, the Alphabet Q2 earnings forecast matters less as an investor event than as a budget defense problem. If Google’s AI search layer is bringing in more commercial queries, then higher Google Ads spend may be justified. If that same layer is raising the cost of visibility while moving more delivery into automated campaign formats, then the same budget may simply be buying a less explainable version of last quarter’s demand.

The hard ground is Q1. Google Search + Other Advertising reached $60.4 billion in Q1 2026, up 19% year over year, with management pointing to AI Overviews and AI Mode as contributors to expanded query activity; CNBC characterized it as the fastest Search growth rate in years.[1] Q2 is still a forecast as of July 21, because Alphabet reports on July 22, but consensus sits around $116.5 billion in revenue, roughly 21% year over year, with EPS near $2.87 and Cloud growth still treated as a swing factor by market watchers.[2][3][4]

That combination is enough to keep Google Ads in the plan. It is not enough to increase spend without a more careful answer to where the growth is coming from, who is paying for the new ad surface, and whether the return path is still legible inside your own accounts.

The useful read on Q2 is not “AI is working.” It is “working for whom?”

At the platform level, Google’s AI monetization story is no longer theoretical. AI Overviews have reached 1.5 billion monthly users across more than 200 countries, which makes them a mainstream search feature rather than a limited test environment.[5] When a feature at that scale is attached to the largest performance media channel in most acquisition plans, it stops being a product update and becomes a line item.

The advertiser’s version of the story is less clean. Adthena data reported by Search Engine Land, covering more than 5 million ads across six industries from December 2025 to January 2026, found that technology queries with AI Overviews consistently carried higher CPCs.[6] That does not prove every AI Overview category will become more expensive, and it does not prove AI Overviews caused all of the CPC pressure. It does support a narrower and more useful planning assumption: in categories where AI Overviews are common and paid visibility is still valuable, advertisers may need to budget for a visibility tax.

This is the tension leadership teams tend to flatten. Alphabet can post strong Search revenue growth while an individual advertiser sees the same conversion volume require more spend, broader matching, or heavier reliance on automated formats. Those statements are not contradictory. One is an aggregate revenue result. The other is an account-level profitability problem.

The Q2 report should therefore be treated as a confirmation point, not a starting gun. If Search growth remains strong and management continues to argue that AI search surfaces monetize well, paid media teams will have stronger evidence that Google intends to keep expanding this layer. But the decision to raise budgets still belongs in the account data: CPC movement, conversion quality, impression share changes, and whether marginal spend is finding incremental demand or just defending visibility in a more expensive interface.

The new cost of search visibility is showing up before the new reporting clarity

AI Overviews change the shape of the results page. They can answer, summarize, compare, and redirect attention before a user reaches the familiar stack of paid and organic listings. Google has said AI Overviews monetize at parity with traditional search in its own A/B testing, but that claim comes from Google-controlled tests rather than broad independent verification.[7] It is useful directional evidence, not a blank check for advertisers.

The operational problem is that advertisers can be asked to pay more before they receive better explanations. If AI-Overview-heavy auctions push CPCs up, the budget conversation moves from “we need more spend because demand is growing” to “we need more spend because the visible part of demand is more expensive to access.” That is a harder argument to make when keyword-level control and auction transparency are also being softened by automation.

A useful Q3 planning review should separate three effects that are easy to blend together in a platform dashboard:

• Category-level CPC pressure: Are CPC increases concentrated in query groups where AI Overviews are visibly common, or are they broad across the account?
• Conversion quality: Are higher-cost clicks producing the same downstream lead quality, pipeline acceptance, revenue, or repeat purchase behavior?
• Control loss: Are performance changes tied to exact and phrase match campaigns, or are they coming through broad match, Performance Max, AI Max, or other automated delivery?

That last distinction matters because the visible metric can look acceptable while the explanation gets worse. A campaign may hold target ROAS while shifting spend into broader queries, different placements, or less familiar user journeys. Finance may accept the ROAS. Sales may not accept the lead quality. The paid media lead is the person left reconciling both versions of performance.

AI Max, Performance Max, and broad match are the campaign layer of the same earnings story

The earnings narrative and the campaign product narrative should not be read separately. Google reported that AI response costs have fallen 30% since the Gemini 3 upgrade, improving the economic case for scaling AI search features.[1] When the cost of generating AI responses falls and the user base is already in the billions, Google has both the incentive and the infrastructure to keep putting more search behavior through AI-shaped experiences.

On the buying side, that shows up through products that ask advertisers to provide more inputs and accept less deterministic delivery. AI Max for Search is the clearest recent example. Google said its beta produced an average 27% increase in conversions at similar ROI targets.[5] That number deserves attention, but it should be carried into planning with the label attached: Google-reported beta data, likely involving advertisers willing to test the product early, and not necessarily representative of full rollout performance in every account.

The right question is not whether a 27% lift is possible. It is whether “similar ROI targets” resemble your actual constraints. A B2B software account with strict lead qualification rules, a retailer with thin margins, and a local services advertiser with limited service coverage can all hit the same surface-level conversion target while experiencing very different business outcomes.

Before Q3 budgets are locked, campaign managers should inspect the parts of the account that automation depends on most:

For teams already seeing AI Mode placements and automated search expansion in their accounts, the tactical work belongs deeper than an earnings read. The practical settings, creative library, and landing page implications are covered in AI Mode Ads Are Already in Your Google Ads — Here’s How to Adapt. The strategic point here is simpler: Alphabet’s numbers make it more likely that these formats become the default path for incremental reach, not a side experiment.

What to tell leadership before the Q2 print

A clean internal message is better than a heroic forecast. As of July 21, Q2 actuals are not public. The consensus expectation of roughly $116.5 billion in revenue and about 21% year-over-year growth gives planning context, but it should not be treated as evidence that the quarter has already confirmed advertiser returns.[2][3][4]

The defensible stance is to keep Google Ads central where account economics still hold, while reserving budget flexibility for categories where AI Overviews are changing the auction. That may mean increasing spend in campaigns with clean downstream performance and holding back in areas where CPCs are rising faster than qualified conversions. It may also mean shifting the budget conversation away from average account ROAS and toward marginal performance by query theme, campaign type, and lead or order quality.

If leadership wants a one-line answer, it is this: Q1 supports continued investment in Google’s AI-driven ad ecosystem, but Q2 should decide the size and conditions of the increase. The platform is monetizing AI search. Advertisers still have to prove they are not just funding the transition.

The post-earnings check to run on July 22

Once Alphabet reports Q2, do not stop at whether revenue beat or missed consensus. For paid media planning, the more useful review is narrower:

1. Compare Search growth against Q1’s 19% benchmark. If growth stays strong, assume Google has more confidence to keep expanding AI-shaped search inventory.
2. Listen for updated language on AI Overviews, AI Mode, and monetization parity. Treat Google’s wording as directional unless supported by advertiser-side data.
3. Check whether management links AI adoption to commercial query growth, not just user engagement.
4. Re-run CPC and conversion-quality cuts in your own AI-Overview-exposed categories before approving Q4 budget increases.
5. Prepare stakeholders for less keyword-level certainty if more growth is coming through AI Max, Performance Max, and broad match.

The budget answer does not need to be defensive. It does need to be conditional. Keep Google Ads in the plan if returns still hold, budget for higher visibility costs where AI Overviews are most present, and make sure any spend increase comes with a clear explanation of what control the team is giving up in exchange for automated reach.

References

1. Alphabet Q1 2026 earnings, CNBC, Apr. 2026
2. Alphabet Q2 2026 earnings forecast, Yahoo Finance
3. Alphabet Q2 2026 earnings preview, IG International
4. Alphabet Q2 2026 earnings estimates, InsiderFinance
5. Google Marketing Live 2026, Google, May 2026
6. Adthena data on AI Overviews and CPCs, Search Engine Land, Dec. 2025-Jan. 2026
7. Google says AI Overviews monetize at parity with traditional search, Search Engine Journal, Google Marketing Live 2026