The first half of 2026 moved money, traffic, jobs and market cap before anyone could prove how much value AI created.
Search behaviour changed, token budgets exploded, software stocks sold off, and companies blamed layoffs on AI.
Every major AI story in H1 2026 was really a story about attribution.
The common thread: AI's economic impact is expanding faster than we can attribute it.
AI Mode is now a click away from AI Overviews — two clicks from regular search results.
1. Tracking a brand's presence in AI Search is highly complex — engine, personalisation, reasoning levels, model updates and stochastic variability all factor in.
2. Measurement is fragmented. 91% of citations appear in only one of ChatGPT, Perplexity, or AI Overviews.
Prompt tracking should be closer to polling and focus groups than SEO rank tracking.
3. Brand mentions in AI answers are more impactful on business outcomes than citations. Most vendors and merchants need to watch how often they show up in a panel of prompts, in which context, with what sentiment, and whether they are recommended ahead of competitors.
The citation-versus-mention gap is covered in Ghost Citations.
4. Trust is the highest currency in AI search.
In AI Overviews, by contrast, users click, evaluate and compare a lot — classic search behaviour that does not carry over to AI chatbots.
5. Writing style, token budget and technical factors matter for agents. Lead with unique information, write directly, remove fluff, and keep your site technically fast and accessible.
AI recommendations shape demand — not citations. The unit of optimisation is whether AI names, trusts and recommends your brand.
The second story of H1 2026 is money.
Peter Steinberger went viral with Clawdbot, leading to millions of installs, long lines in China, and Nvidia building a Nemoclaw clone.
Then it got stranger. Shopify, Uber, Meta and other tech giants built token leaderboards that incentivised engineers to light as many tokens on fire as possible.
"Let me give you a thought experiment. Let's say you have a software engineer or AI researcher, and you pay them $500,000 a year. At the end of the year, I'm going to ask him how much did you spend in tokens. And [if] that person said $5,000, I will go ape something else. If that $500,000 engineer did not consume at least $250,000 worth of tokens, I am going to be deeply alarmed." — Jensen Huang
A Meta engineer created an internal leaderboard dubbed "Claudeonomics" ranking over 85,000 employees by the number of AI tokens they burned.
It led to massive waste as employees left AI agents running on idle or useless tasks just to climb the ranks and earn titles like "Token Legend." Reportedly, Meta engineers consumed 73.7 trillion tokens in just 30 days.
In April, CFOs pumped the brakes after discovering employees had burned through their annual token budget in four months.
TokenMAXXXING shifted to valueMAXXXING. Meta's leaderboards shut down in April — but not before the #1 user averaged 281 billion tokens, which would cost over $1.4 million at standard API rates.
What survives the all-you-can-eat token craze is a step change in productivity that we cannot yet measure.
The author cites his own case:
One outcome of the token tsunami is that a lot more people started using Claude, which in turn fired up growth engines for AI infrastructure companies Claude recommended, like Supabase.
In February 2026, following the Opus 4.6 release and Claude Cowork, the software-as-a-service industry saw a severe market downturn wiping out hundreds of billions of dollars in market cap over just a few trading sessions.
The SaaS vertical saw a steep annual decline of over 30%. Valuations for high-growth software companies collapsed, with revenue multiples shrinking dramatically against pandemic highs.
Anyone in B2B likely felt it as lower conversion rates, longer sales cycles and softening brand searches.
It is the bottom quartile of software companies that pulls the market down.
Interestingly, the decline is purely associated with how "the market" views a company's AI robustness.
Company valuation drops are not related to performance, but rather to whether people think AI will disrupt their product.
Over the last 30 trading days, both the top quartile and median IGV stock outperformed the ETF as a whole — the bottom quartile, including some of the largest companies, dragged overall performance down.
The practical advice: in B2B, if you are forced or privileged to take a new job, pick a company the market is optimistic about.
You have heard it: "We're laying off x% of our workforce due to AI." The narrative du jour.
Challenger, Gray & Christmas reported AI was the leading cited reason for May 2026 job cuts, cited in 87,714 cuts year-to-date — 22% of all 2026 announced layoffs through May.
Layoffs are real:
The author has written extensively about the Big Labs using AI replacement as a marketing narrative. But it hasn't come true yet.
A lot of AI layoffs were really to offset capital expenditures, pandemic overhiring, and economic turbulence.
We can imagine a future where AI makes certain roles obsolete. That time is not now.
A year ago, in the H1 2025 report, the author predicted AI layoffs were a PR stunt — and was right:
Despite clear productivity gains from AI, the recent waves of layoffs show no indication of AI being the underlying cause. In fact, companies using AI as a reason to lay employees off are rehiring them.
ChatGPT's market share decreased from 78% in July 2025 to 56% in July 2026, while Gemini went from 15% to 30% and Claude from 2% to 10%.
Google is now the most likely player to win the AI consumer market, and ChatGPT has lost its significance in AI Search to Google.
OpenAI still has ~1.1B users but refocused on an enterprise pivot — shutting down Sora, dropping video in ChatGPT, and killing Instant Checkout. Enterprise makes up ~40% of revenue, heading to ~50% ahead of a potential IPO.
The author is skeptical about buying OpenAI stock. According to Ed Zitron, OpenAI spends $2.8 billion every month to make $1.1B.
Anthropic's fight with the Pentagon pushed Claude to No. 1 among free apps on Apple's U.S. App Store. Months later, users seem less sure whether Anthropic can resist attacks from open-source models.
The Big Labs massively subsidise tokens. SemiAnalysis found a $200 plan of the frontier models gives you $8–14K worth in tokens.
Recently, open-source models like Kimi K3, GLM 5.2 and Deepseek V4 have pressured US labs because of those subsidies.
Open-weight models also show that the model itself becomes commoditised, while the harness and application layer gain importance.
Forward-deployed-engineer job postings rose 800% in the first nine months of 2025 and were still up 729% year over year in April 2026. Frontier Labs now pay more than $500,000 in total compensation for people who can turn a model demo into a working system inside a customer's business.
From an execution perspective, model availability has become a business risk. Having options is good.
The author predicted up to 70% of 2024 organic traffic could be gone by 2026 — that was overeager.
The numbers land at ~33% referral loss year over year, with 68% of Google searches now ending without a click.
Publishers are moving the fight to the courts and regulators.
USA Today CEO Mike Reed:
"We're getting close now to the point where we'll block Google as well and abandon the traditional search traffic that we get today."
Google responded by adding an impression-based AI report in Search Console.
Parallel AI and Cloudflare are both trying to build publishing marketplaces for content owners and AI companies.
"Assume there's no search. You have to have your businesses planned as if search is zero." — Conde Nast CEO Roger Lynch
Publishers are trying to replace the content-for-traffic bargain with a content-for-training market. That leaves several questions unresolved:
Measure engines separately. If 91% of citations appear on only one engine, a single blended score is distortion, not an average.
Design tracking like polling. Fix a prompt panel, ask the same questions on a schedule, watch the movement. Build a trend line, not a leaderboard.
Log mention, sentiment and recommendation separately. Appearing tells you nothing on its own.
Reframe trust assets as AI visibility investment — a structure also visible in AI Models Search for Familiar Brands 3.2x More Often.
Do not take the AI layoff narrative at face value. Even where Challenger records AI as the leading cited reason, cited reasons and actual causes differ. Building hiring plans on that narrative means building on a false premise.
Separate token spend from outcomes. Meta's Claudeonomics episode shows usage metrics are not value metrics. Internal AI KPIs should measure output quality, not consumption.
Plan for a zero-search scenario. The Conde Nast CEO's line is not hyperbole but a planning principle in an environment where 68% of searches end without a click.
Check how machines understand your business first — see The AI Entity Footprint Audit — and for organising the metric set, "Do We Show Up in ChatGPT?".
It reached 1 billion monthly active users with queries roughly 3x longer than classic search. Google called it the biggest search box upgrade in 25 years at I/O 2026.
91% of citations appear on only one of ChatGPT, Perplexity or AI Overviews, which is why per-engine measurement is more accurate than a blended score.
Meta's Claudeonomics leaderboard ranked over 85,000 employees by tokens burned, causing waste as staff left agents running on idle tasks. Engineers consumed 73.7 trillion tokens in 30 days, and CFOs shut it down in April after finding the annual budget spent in four months.
February 2026 saw hundreds of billions in market cap wiped out and the SaaS vertical fall over 30% annually. The decline tracked perceived AI robustness rather than performance, with the bottom quartile dragging the whole sector down.
The author argues not. Although Challenger recorded AI as the leading cited reason, many layoffs offset capital expenditures, pandemic overhiring and economic turbulence — and companies citing AI have been rehiring.
ChatGPT fell from 78% in July 2025 to 56% in July 2026, while Gemini rose from 15% to 30% and Claude from 2% to 10%.
Around 33% referral loss year over year, with 68% of Google searches ending without a click. Publishers are responding through litigation and regulators while trying to replace content-for-traffic with a content-for-training market.
To apply what you just read to your own site, start with a free audit of where things are now.
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