The pattern nobody’s naming: everything is “dying,” nothing is actually dead
Scan those headlines and you see the same script on repeat:
- The death of organic reach.
- Brand-led growth “beats” performance marketing.
- AI search will replace SEO as you know it.
- TV is dead. TV is back. TV is shoppable. Pick your year.
Meanwhile, operators quietly keep making money on:
- Search after yet another Google core update.
- “Dead” email that still prints CAC payback.
- Organic content that ranks because someone fixed cannibalization and title tags.
- Linear and CTV that look boring on Twitter but crush in MMM.
The real issue in 2026 isn’t that channels are dying. It’s that channel volatility is now permanent, while your growth system is probably still built like it’s 2018: one or two hero channels, a couple of dashboards, and a lot of faith.
If you’re a performance marketer, media buyer, or growth lead, the job now is simple to describe and hard to do:
stop building channel strategies, start building a channel-agnostic growth system.
Why the “end of X” narrative is so persistent (and so dangerous)
Those headlines exist for a reason. They’re not totally wrong; they’re just framed in a way that’s useless to operators.
1. Platforms are structurally unstable now
Look at the mix of stories:
- Core updates, AI Overviews, entity-based SEO, localized SEO for LLMs.
- The death of organic reach and “how to win Stories / Reels / whatever’s next.”
- TV and CTV “trends” that flip every upfront cycle.
Translation: the surface area of change is bigger than your team’s attention span. If your plan is “keep up with everything,” you’ve already lost.
2. “Brand vs performance” is a fake fight
You see it in the Little Spoon coverage and the “brand-led growth beats performance” takes. At the same time, you’ve got hardcore CRO case studies, 8,000-title-tag rewrites, and 37% lift from conversion strategy.
The tension isn’t brand vs performance. It’s:
- Short-term, easily-attributed spend vs
- Longer-term, fuzzier-but-real compounding effects.
If your system forces you to choose, you don’t have a system. You have a bias.
3. AI is amplifying both the upside and the chaos
The AI headlines fall into three buckets:
- “AI actually delivered” case studies.
- AI search, AI Overviews, entity-based SEO, LLM-localized content.
- AI’s trust problem and the cost of outsourcing your message.
AI is making it cheaper to:
- Produce mediocre content at massive scale.
- Ship more tests than you can interpret.
- Automate campaigns you don’t really understand.
That doesn’t kill channels. It just makes signal extraction the real skill. Not “using AI,” but deciding where AI should sit in your stack and where it absolutely should not.
The job now: build a system that survives channel drama
Instead of asking “Is SEO / paid social / email / TV dead?” the better question is:
“If this channel dropped 50% in efficiency next quarter, what would we actually do?”
If your honest answer is “panic and slash budgets,” your system is fragile. Here’s how to make it less so.
1. Start with a portfolio, not a hero channel
Most teams still behave like:
- “We’re a Meta brand.”
- “We’re search-first.”
- “We’re influencer-led.”
That identity is comforting and dangerous. A channel-agnostic system behaves more like a portfolio manager:
Define your channel classes
Instead of listing platforms, define classes:
- Harvest: high-intent, high-ROAS, low scale (search, branded terms, remarketing).
- Hunt: mid- to upper-funnel performance (prospecting on paid social, programmatic, YouTube, affiliates).
- Plant: brand, content, PR, TV/CTV, organic social, community.
Then ask:
- What % of budget sits in each class today?
- What % should sit there given our payback window and margin?
- Which classes are dangerously concentrated on a single platform?
You’re not “a Meta brand.” You’re overexposed in one Hunt channel.
2. Build an attribution stack that assumes it’s wrong
With AI search, privacy changes, and platform black boxes, the only guaranteed thing about your attribution is that it’s wrong. The question is: wrong in which direction, and by how much?
Run multiple attribution views on purpose
At minimum, you want:
- Platform-reported: for optimization inside the walled garden.
- First-party analytics: last-click / data-driven in GA4 or equivalent.
- Incrementality tests: geo splits, holdouts, or PSA tests on key channels.
- Directional MMM or lightweight media mix: even if it’s scrappy, not a 9-month PhD project.
The point is not to find “the one true number.” It’s to:
- Spot where a channel looks good in-platform but flat in incrementality.
- Catch channels that look bad in last-click but move the MMM needle.
- Decide where you’re willing to be “wrong” in favor of speed.
Codify how you’ll react to signal changes
Most teams improvise. Better: write simple rules.
- If platform ROAS improves but incrementality tests don’t, cap spend growth at X% per month.
- If a channel’s blended CAC worsens by Y% and you can’t explain it in 2 weeks, move Z% of budget to your best Hunt backup.
- If a Plant channel shows lift in branded search or direct traffic, commit to a minimum 2-3 quarter runway before judgment.
You’re pre-deciding behavior before you’re emotional. That’s how you avoid overreacting to core updates or “organic reach is dead” weeks.
3. Treat AI as an operator, not a strategy
The AI headlines split into “AI is magic” and “AI will ruin everything.” Both are lazy. AI is just another operator on your team: fast, tireless, and occasionally very wrong.
Where AI belongs in a channel-agnostic system
Use AI aggressively for:
- Volume work: keyword expansion, negative keyword mining, initial audience ideas.
- Variant generation: ad copy, hooks, subject lines, creative concepts.
- Ops and QA: spotting broken links in ecommerce emails, checking tracking parameters, surfacing anomalies.
Be cautious or hands-off with AI for:
- Positioning and messaging: that’s where “AI’s trust problem” shows up. You can’t outsource your core story.
- Strategy: AI can summarize options, not own the tradeoffs.
- Attribution decisions: don’t let AI “auto-allocate” budget without human guardrails.
Use AI to compress feedback loops, not to guess the future
The real value for operators:
- Faster test design: AI drafts 10 test ideas, you pick 2 that actually map to your growth thesis.
- Faster analysis: AI summarizes performance by cohort, creative theme, or query cluster.
- Faster iteration: AI generates new variants based on winning patterns you specify.
That’s how you survive a world of constant “Top 10 trends for 2026” without chasing every one.
4. Fix the boring stuff that compounds across every channel
The headlines about 8,000 title tag rewrites, cannibalization, and 37% conversion lifts are the quiet tell: ops discipline still beats trend-chasing.
Conversion and retention are your real risk hedge
If your site converts 2% and your competitor converts 4%, it doesn’t matter who “wins” AI Overviews. They can afford to pay twice as much to acquire the same user.
Make a short, ruthless list:
- Top 5 landing pages by spend.
- Top 5 flows by revenue (signup, trial, checkout, lead form, demo request).
- Top 5 lifecycle journeys (welcome, cart/browse abandon, trial, winback, post-purchase).
Then:
- Run structured tests on those pages and flows before you launch yet another channel.
- Audit basic technical issues: load time, tracking, broken states, mobile UX.
- Use AI to surface friction (e.g., “explain this form like you’re a confused customer”).
Every 10-20% lift in these areas makes you more resilient when a channel’s CPMs spike or a core update hits.
Content structure that survives AI search
With AI Overviews and LLMs, the game isn’t “rank for every keyword” anymore. It’s:
- Be the clearest, freshest, most structured source on a topic.
- Own entities and topics, not just individual phrases.
- Keep content updated so publish dates and freshness signals work in your favor.
That’s why you’re seeing “fresh content,” “publish dates,” and “entity-based SEO” everywhere. The operators who win will:
- Consolidate cannibalized content into stronger hubs.
- Standardize templates that LLMs can parse easily (clear headings, FAQs, definitions, examples).
- Maintain a simple refresh calendar instead of endlessly shipping net-new fluff.
5. Put real constraints around “trend work”
You can’t ignore trends. But you also can’t run a business on “Top 10 PPC stories of 2025” and “2026 predictions.”
Create a trend budget
Treat trends like speculative investments:
- Allocate a fixed % of media budget (say 5-10%) for “experimental / emerging.”
- Allocate a fixed % of team time (say 10-15%) for research, testing, and post-mortems.
- Define a simple bar: what does “keep” vs “kill” look like after 60-90 days?
Then when AI search, a new social format, or a shiny CTV product shows up, you’re not scrambling. You’re pulling from a pre-agreed bucket with pre-agreed expectations.
Document your “we do not care” list
This is underrated. Write down:
- Channels you will not touch this year, and why.
- Metrics you will not optimize for (vanity engagement, view-through-only conversions, etc.).
- AI use cases you explicitly ban (e.g., auto-sending AI-written sales emails without human review).
This gives your team permission to ignore 90% of the noise and go deep on the 10% that matters.
What this looks like in practice
A channel-agnostic growth system in 2026 might look like this:
- Budget split across Harvest / Hunt / Plant, not “Meta / Google / Other.”
- Multiple attribution views and a simple playbook for how you’ll react when they disagree.
- AI embedded in ops, QA, and iteration, not in your positioning or strategy.
- A ruthless focus on conversion, retention, and content structure that compounds regardless of which channel is “hot.”
- A small, explicit trend budget and an even more explicit “we do not care” list.
Channels will keep “dying” in headlines. Your job is to build a system that doesn’t die with them.