u/Icy-Drawer5856

We have advertised on Google for nearly two decades, going back close to the beginning of AdWords. It has created enormous value for us.

At the same time, my own use of traditional Google Search has fallen dramatically over the last three years, particularly this year. Restaurants, local services, B2B vendor discovery, product research and general information increasingly start with AI.

I am curious whether SEO practitioners are seeing a change not just in the amount of Google traffic, but in its composition.

My hypothesis is that AI displacement is not random.

A simple navigational query does not benefit much from AI. Complex research does. Evaluating software, professional services, financial products or a major purchase benefits substantially from synthesis, comparison and follow-up questions.

That raises the possibility that Google could retain huge volumes of simple searches while losing a smaller number of economically valuable research sessions.

For sites losing organic Google traffic, what is disappearing first?

Are informational queries taking most of the damage, or are commercial investigation and high-consideration queries declining too?

Are you seeing differences between low-value traffic and visitors that historically generated meaningful leads or revenue?

Are B2B, financial services, legal, healthcare, software or other high-value categories behaving differently?

Has traffic fallen while conversions remained resilient, or are conversions and lead quality beginning to deteriorate as well?

The dataset I would love to see is Google traffic since 2022 or 2023 segmented by query intent, commercial value, conversion rate and industry.

Aggregate traffic decline tells us very little if the mix is changing.

The question I keep coming back to is: which traffic is disappearing, who generated it, and what was that traffic economically worth?

Equally interested in evidence that disproves the hypothesis.

Prior discussion: https://www.reddit.com/r/ValueInvesting/s/z3Ig03i7Qd

Every large payday in dating apps came from a liquidity event, not from operating the business.

Tinder's founders held options on roughly 20% of the company. Match consolidated at a $3bn valuation in 2017, about $600m for that stake, after an internal estimate a year earlier reportedly put Tinder at $12bn. They sued for over $2bn and settled mid-trial in December 2021 for $441m across ten plaintiffs, paid from cash on hand.

On the Bumble side, Andrey Andreev sold his entire stake in MagicLab to Blackstone in November 2019 at a $3bn valuation and stepped down. Blackstone took the business public fifteen months later at $8.6bn. Whitney Wolfe Herd's retained stake was worth roughly $1.5bn at that IPO.

A consolidation, a settlement, a sponsor buyout, an IPO. Meanwhile Bumble is down 96% from its peak and Match 78%. The people who made money sold the story. The people who bought it did not.

WHAT WAS ACTUALLY BEING SOLD

The product monetizes two things: impulsive spending and impulsive time allocation. A boost or a super-like is bought in a moment of frustration, delivers no durable good, and produces no measurable outcome. Subscription tiers are priced for search volume: unlimited swipes, see who liked you, more visibility. Everything you buy makes the search bigger.

That is the most cycle-sensitive revenue in consumer. It requires a customer with surplus discretionary cash and surplus discretionary attention at the same time. From 2020 to 2022 the US had a historic abundance of both, through stimulus, zero rates, remote work, no commute and low unemployment. Venture funding went from $60bn in 2012 to $643bn in 2021, and roughly a third of that went into consumer brands chasing exactly this customer.

Then the rate cycle ended, and the marginal impulsive purchase went first everywhere.

Direct-to-consumer. CNBC found more than half of 22 public DTC companies down 50% or more from IPO. Allbirds, Casper, Rent the Runway, ThredUp: same cohort, same funding source, same customer.

Peloton. Roughly $50bn peak market cap, down about 95%. Subscription fitness sold as identity.

Luxury. The aspirational shopper withdrew. Placer.ai documented a large pullback in the second half of 2025, with luxury visit growth slowing while ultra-wealthy traffic held up. The wealthy customer stayed. The aspirational one, the ZIRP one, left. Even Nike is down roughly 77% from its November 2021 all-time high.

The pattern is identical. Businesses that sold optionality or identity rather than utility, priced against a customer whose surplus has since compressed. Dating apps are the purest expression of it, because the good being sold is the search itself.

WHY THIS ONE DOES NOT COME BACK

Here is the part I think is underappreciated, and it is not a swipe-app problem.

Matchmakers charging $20,000 a client bill retainers and per-introduction fees. If the client marries, the matchmaker has been paid. If the client does not, the matchmaker has been paid. Nobody in the business of introducing people has ever been paid for the introduction working. Not at $20 a month, not at $20,000.

So this is not venture capital corrupting a previously aligned model. There was no aligned model. The entire category, across four orders of magnitude of price and two completely different labor models, prices activity rather than outcome.

Which means the demand recovery people are waiting for requires the customer to resume paying for search intensity, and the whole ZIRP unwind is the customer deciding they will not.

THE COMP SET IS WRONG

Most people have no idea how to comp this, so the screen decides. And the screen puts Match next to Pinterest, Snap, Spotify and Duolingo. Consumer internet, subscription revenue, recognizable brand. Against that set at fifteen to twenty-five times, nine times looks cheap and the buy case writes itself.

Every one of those companies sells indefinite consumption. You never finish listening to music. You never complete Pinterest. Duolingo is engineered so the streak never ends. Their retention curves flatten into a loyal base that stays for years, and that is precisely what a subscription multiple pays for.

Match sells a terminal good. The customer's objective is to stop being a customer. That is not a variant of the subscription model, it is the inverse of it, and no multiple derived from indefinite-consumption businesses tells you anything about what it is worth.

The businesses that actually rhyme sit in completely different sectors.

WeightWatchers. A subscription sold against a goal the customer wanted to achieve and leave. Revenue depended on the goal not being reached, or being reached and then relapsing. Peak market capitalization around $6.7bn, with the stock above $100 a share in 2018. Members fell from 4.9m in 2021 to 3.6m in 2024. Revenue was about $811m in 2024. It filed Chapter 11 on 6 May 2025 carrying roughly $1.6bn of secured debt, wiped out $1.15bn of it in a 42-day prepackaged plan, and emerged private.

Note what killed it. Not a better weight-loss subscription. Something that actually worked.

Chegg. A subscription sold against a terminal academic need. Record close of $113.51 on 12 February 2021, roughly $14.5bn of market value. Revenue peaked at $776m that year. Management warned in May 2023 that ChatGPT was suppressing new sign-ups and the stock fell nearly 50% in a day. Q4 2025 revenue was $72.7m, down 49% year over year. Q1 2026 was guided to $60m. The company has cut 45% of its workforce, received an NYSE delisting notice in April 2026, and trades near a dollar. Down roughly 99% in five years.

Both were mature businesses with real brands, real cash flow and a debt load. Both went from a defensible multiple to near-zero inside three years. Neither lost a single customer to a competitor. They lost them to the problem being solved.

That reframes the downside here. The risk to Match is not that Hinge takes share from Tinder, or that Bumble executes better. It is that the category's premise gets solved by something that is not a dating app, at which point the incumbent does not get competed with, it gets obsoleted. And the balance sheet matters in that scenario the way it mattered at WeightWatchers: $2.97bn of net debt against an EBITDA line that has to keep servicing it.

THE TWO VARIABLES

Payers and revenue per payer are the only health metrics for either business. Everything else, MAU, DAU, Sparks, six-way conversations, engagement, is an input the company defines and can re-cut.

Match payers: 16.55m peak in Q3 2022, 13.3m in Q2 2026, down 20%. Revenue per payer over the same window: $16.02 to $21.13, up 32%.

The cleanest way to see it is to take Match and Bumble combined, Q1 2025 against Q1 2026. Payers fell 8.4%, from about 18.2m to 16.7m. Combined total revenue was flat, down 0.2%. Every subscriber lost was paid for by a price increase on the subscribers who stayed. Bumble's payers fell another 16% year over year last quarter.

That trade has a floor. You cannot raise price into a shrinking base forever, and the price increases accelerate the exit.

My forecast: 2026E revenue $3.46bn, in line with company guidance and already a decline. Then negative 2%, negative 6% and negative 9.5% through 2029, with EBITDA at $950m on a 33% margin.

Match trades at 9.0x trailing adjusted EBITDA at $38.69. Bumble, same mechanism and same category, trades at roughly 2.3x. Nearly seven turns of gap between two companies whose payer bases are declining together, one of which is in a sale process.

At 4.5 to 5.0x 2029 EBITDA against $2.97bn of net debt, that is $8 to $11 a share, roughly 72% to 79% below the current price.

THE CARETAKING PROBLEM

Note what is not in that. The multiple does about 80% of the work. Hold EBITDA completely flat at the 2026 estimate, assume no further buyback, and at 4.0x the stock is $9.75, inside the target range with zero deterioration in the business.

Which is why the more interesting observation is not about the model. It is that the founders, the sponsors and the early holders extracted their value years ago through liquidity events. What is left is a board, a management team and a shareholder base administering an asset whose economics were harvested by people who are no longer in it. That is not a turnaround. It is custody.

WHAT WOULD CHANGE MY MIND

Not a better app. A different revenue model. Outcome-contingent pricing, a bounty paid on the exit rather than on the search, is the only structure that inverts the incentive rather than mitigating it. The obvious objection is verification, but matchmakers know their clients for years and still do not price on it. The real constraint is that underwriting an outcome means knowing the base rate, and the base rate is the number nobody publishes.

It is also why that model is the falsifier rather than a product feature. A business paid on the exit is the only structure that survives its own category being solved, because it gets paid by the solution instead of displaced by it.

Cleaner and nearer term: payers and revenue per payer growing together for four consecutive quarters at either company. Right now they move in opposite directions, and that is the whole thesis.

Disclosure: no position, intend to build a short in stages over twelve months.

I think we should start with the obvious point: a lot of AI-generated content is slop.

It is generic, repetitive, thin on facts, light on numbers, poorly sourced, and often contains nothing particularly original. Someone types a broad prompt, gets 1,500 words back, pastes it into Reddit, and may not even understand the argument well enough to defend it.

We all know what that looks like. I do not think anyone wants more of it.

The question I am more interested in is where serious securities analysis falls on this spectrum.

I recently published a long-form short thesis on Match Group. I spent roughly 30 hours on it.

If I treated it like an initiating coverage report on a sell-side desk, back when I did this work manually, I think it would have taken a three-person team roughly 300 total hours to assemble something comparable. That includes gathering and checking source material, going through filings and transcripts, building the historical framework, working through the capital structure and valuation, testing the thesis, writing, editing, footnoting, and repeatedly checking the numbers.

I also used GPT and Claude extensively.

AI did not give me the investment judgment or originate the worldview behind the analysis. What it did was collapse an enormous amount of research, synthesis, drafting, organization, and checking time.

For context, my background includes M&A, leveraged finance, and sell-side research. That framework is central to how I look at companies and securities. My GPT and Claude workflows have also accumulated a lot of that context over time. When I analyze a stock, I am constantly pushing toward the things I was trained to care about: capital structure, cash conversion, incentives, consensus expectations, operating leverage, downside cases, variant perception, and what actually has to happen for the equity to work or fail.

So this is not a blank prompt asking an LLM whether a stock is good or bad. It is much closer to having extremely fast research assistance operating inside an analytical framework I already bring to the problem.

I posted the MTCH thesis in r/SecurityAnalysis. It generated substantive discussion and Reddit showed it as the #1 post in the subreddit today. A moderator subsequently characterized it as "AI slop" and banned me. As far as I can tell, there was no posted rule prohibiting AI-assisted research. Interestingly, the thesis itself remained up.

That experience made me wonder whether "AI-generated" versus "human-generated" is even the useful distinction.

To me, the real spectrum looks more like this.

At one end is actual slop: generic output, no sourcing, few facts or numbers, no original work, no accountability, and an author who cannot defend what was posted.

At the other end is original research where AI materially accelerates collection, synthesis, checking, organization, and writing, but the author originates the thesis, exercises judgment, verifies the work, and stands behind the conclusions.

Securities analysis seems like an especially interesting test case because the end product is supposed to be facts, numbers, synthesis, judgment, and a differentiated view. We already use enormous amounts of tooling to reduce mechanical labor. Nobody thinks a DCF becomes intellectually illegitimate because Excel performed the arithmetic.

So I am genuinely curious where people here draw the line:

1. What should actually qualify as "AI slop"?
2. If a research piece contains original analysis, specific numbers, primary-source facts, footnotes, forecasts, valuation work, and falsifiable conclusions, does extensive AI assistance meaningfully diminish it?
3. Is the relevant question who typed the sentences, or whether the author understands, verifies, and owns the analysis?
4. Should AI assistance simply be disclosed as part of the research process?
5. If AI can compress 300 hours of traditional research production into 30, is that a problem, or is that simply technological progress?
6. As these tools improve, does the moat in securities analysis move away from the labor of assembling information and increasingly toward judgment: choosing the right question, identifying the variable that matters, understanding what consensus is missing, and knowing when the machine is wrong?

I completely understand the backlash against AI slop. There is a lot of it, and it makes the internet worse.

I am much less convinced that serious AI-assisted research belongs in the same category.

Curious how r/ValueInvesting thinks about the distinction.