Searchlight is Aztek's marketing news roundup that brings together the week’s most relevant developments in marketing, search, AI, and digital strategy, all in one place. We update this article throughout the week with news we think is worth your time, along with context to help you understand what changed, why it matters, and what it could mean for your business.
This week's topics:
05/11: Ground Control to Claude: What the AI Compute Arms Race Means for Your Marketing Stack
Last week, Anthropic signed a deal to grab every watt and GPU inside SpaceX’s new Colossus 1 data-center cluster. The ink was barely dry before Anthropic doubled Claude Code’s usage caps and removed peak-hour throttling for Pro and Max users.
A day later OpenAI ended its years-long Azure lock-in and landed GPT-5.5 on Amazon Bedrock less than 24 hours after the exclusivity clause expired. This was an equally loud signal that top-tier models now chase whichever cloud can feed their appetite fastest.
If the “space race” metaphor ever felt tongue-in-cheek, it’s looking pretty literal right now. Compute capacity has become rocket fuel, and the launch window is always now.
Why Compute Is the New Battleground
Wall Street analysts think U.S. data centers will need about 74 gigawatts of power by 2028, but our current grid can only spare roughly 24 gigawatts. In other words, demand may run 50 gigawatts ahead of what’s available. The companies that lock down those chips will decide how quickly the rest of us get new AI features.
What does that mean for marketers? Two things you’ll notice right away:
- Usage limits can change overnight. Claude might raise your cap today, but another platform could slam on the brakes tomorrow if it runs out of capacity.
- Prices won’t sit still. When power and chips get tight, vendors pass the extra cost along likely as a surprise “fuel-surcharge” on your bill.
What the AI Compute Arms Race Means for Marketing Teams
- Latency & Reliability: SEO research, AI-assisted content drafts, even paid-media modeling all slow down when a model starts rate-limiting. Build workflows that can switch over to a second provider when your main one slows down or goes offline.
- Budget Volatility: When GPU spot prices jump, vendors pass it through. If your 2026 plan assumes flat per-token costs, rewrite it now. Treat AI spend like cloud hosting: monitor, forecast, and cap.
- Vendor Lock-In Risk: The bigger the arms race, the less incentive any one provider has to stay cheap and open. Contracts should spell out minimum throughput, notice periods for price hikes, and clear exit ramps.
- Sustainability & Reputation: Using an “in-space” data center sounds futuristic; it also invites scrutiny about energy footprints. Clients will ask. Have an answer.
Your Flight Plan
- Audit Your Dependencies
List every place ChatGPT, Claude, or any other model touches content, analytics, or dev workflows. Highlight single-points-of-failure.
- Budget for Turbulence
Add a 30% swing factor to AI line items. If the market loosens, great, you’ve found money. If it tightens, you’re not scrambling.
- Build a Multi-Model Stack
Most teams already mix Google, Meta, and LinkedIn ads. Treat generative tools the same way. Claude for code, GPT for ideation, open-source for quick lookups. AKA, whatever combination keeps you moving when one engine sputters.
- Negotiate SLAs Like Your Uptime Depends on Them (Because It Does)
Ask for guaranteed token throughput, rate-limit notice windows, and price-change caps. If a vendor won’t put it on paper, that’s a data point.
- Keep an Eye on Power Politics
New regulations around energy use and recycling credits are coming. A future RFP might require your AI vendor to prove its renewable mix.
Chips & Power: The Real AI Bottleneck
The AI compute arms race isn’t just a spectacle of billionaire handshake deals and gargantuan data centers. It directly affects how fast your content team can ship, how steady your ad-modeling stays, and how predictable your budget really is. Treat it less like sci-fi and more like cloud infrastructure: plan for failure, diversify providers, lock in terms, and stay alert.
In other words, keep your feet on the ground…even when your GPUs are headed for orbit.
05/12: AI Agents 101: What They Are, How They Work, and Why They Matter
An AI agent is software that uses a large-language model (or several models) to pursue a goal on your behalf, not just answer a question. It can reason through multi-step tasks, call external tools or APIs, learn from feedback, and decide what to do next without waiting for the next prompt.
Many people still assume an AI agent is just a fancy chatbot, but that's not the case. A classic chatbot reacts only to the words you type, while an agent uses its autonomy to pursue a goal, decide the next step, and finish the job for you.
Levels of Autonomy: From Reactive Bots to Goal-Seeking Agents
Think of autonomy on a sliding scale, from rigid scripts to self-directed digital coworkers.
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Level
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What It Does
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Practical Example
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0 — Scripted Bot
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Follows a rigid decision tree with no real AI. Delivers fixed answers and cannot adapt.
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“Track my package” support widget that returns status based on a tracking number.
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1 — Conversational Assistant
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Generates human like responses with an LLM but needs constant prompts and has no tool access or long-term memory.
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A GPT style writing aide that drafts paragraphs when asked.
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2 — Task Agent
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Accepts a single objective, plans a handful of steps, uses one or two tools, then stops when done.
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Pulls last week’s analytics data, builds a chart, emails it to the team.
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3 — Workflow Agent
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Orchestrates a multistep process across several apps, loops on errors, and stores short term context to improve results.
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Cleans a CRM list, drafts personalized outreach emails, schedules the sends, then logs performance metrics.
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4 — Continuous Agent
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Runs around the clock, monitors the environment, triggers workflows when conditions change, and refines its plan over time.
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Google’s experimental “Remy,” which is currently being tested to plan errands, update schedules, and message contacts on your behalf.
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Most business use cases today fall around Level 2 or 3. They are powerful enough to eliminate grunt work while still operating within clear guardrails.
Under the Hood: Core Components of an AI Agent
- Brain (LLM or multi-model stack): Generates plans, interprets results.
- Memory: Stores context so the agent doesn’t start from scratch each time. Anthropic’s new “dreaming” technique literally lets agents review prior runs and self-improve.
- Toolbox: Secure APIs, databases, and apps the agent can call (e.g., Google Ads, HubSpot).
- Orchestrator / Framework: Code that decides when to ask the model, when to call a tool, and how to loop until the job is done (popular options include LangChain, Vertex AI Agent Engine).
- Guardrails: Permissions, rate limits, and audit logs so the agent can’t go rogue.
Real-World Examples Already at Work
- Paid Media Automation: Google Ads’ new journey-aware bidding draws on agent-like logic to adjust spend toward pipeline, not clicks.
- Personal Productivity: Google’s forthcoming Remy watches your inbox, drafts replies, and even orders supplies when it notices the recurring task.
- Compliance Monitoring: Finance firms are spinning up niche agents that scan transactions for policy breaches in real time.
Watchouts: Security, Governance, and Brand Safety
Autonomy cuts both ways. An agent that can post on social or move budget can also go off the rails. Before going live, consider:
- Limiting scopes: Grant the smallest set of permissions that still lets the agent work.
- Logging everything: Keep a trail of decisions for audits and post‑mortems.
- Setting human escalation rules: Define when the agent must pause and ask.
- Starting in the sandbox: Run simulated data or low‑risk tasks first.
Why AI Agents Matter for Your Business
AI agents are not just chatbots with a few more tricks. They’re goal‑oriented coworkers that can plan, act, and learn within clear boundaries. For most businesses, the smartest move today isn’t to chase sci‑fi aspirations; it’s to pilot a modest agent that frees humans for higher‑value work, measure the results, and scale from there. Get the fundamentals right, and the hype turns into hard numbers.
05/13: TikTok’s Pay-Or-Consent Test Puts A Price On Privacy: What Marketers Need To Know
TikTok is testing a £3.99-per-month ad-free tier for UK users who are at least 18 years old. Paying users won’t see ads and TikTok will no longer use their data for advertising. Users who stay on the free version are treated as having agreed to personalized ads by default.
TikTok is calling this a small-scale subscription test, but the setup looks a lot like the pay-or-consent pilots the company ran in other markets last year. The price point is also telling. At about the same level as TikTok’s annual ad revenue per UK user, the model doesn’t need mass adoption to make financial sense.
TikTok usually doesn’t experiment with its ad business for fun. If the numbers work, the same basic model could show up wherever privacy pressure gets harder to ignore, including the United States. For marketers, that makes this less of a “UK-only platform update” and more of a preview of how privacy pressure could reshape the way paid social campaigns are planned and measured.
Why U.S. Marketers Should Pay Attention
This trend is bigger than TikTok. Meta already moved in this direction across the EU in 2023, offering paid ad-free versions of Facebook and Instagram. TikTok’s test adds another major platform to the pay-or-consent pile, which suggests this may become a go-to response when regulators push platforms to give users a clearer choice.
The regulatory pressure is not staying neatly contained overseas, either. Federal privacy proposals like the SECURE Data Act are still moving through the political process, while state laws are already creating new obligations for businesses. Oregon’s 2026 updates, for example, will require companies to honor universal opt-out signals. That kind of rule can quickly shrink the data pool marketers have relied on for targeting and retargeting.
The economics matter, too. At £3.99 per month, TikTok is putting an actual price on a user’s advertising data. It’s not an abstract privacy debate anymore; it’s a revenue model. If the UK pilot protects revenue without hurting engagement, TikTok or another platform could decide the same trade-off is worth testing in the U.S.
Even if this exact fee never makes it across the Atlantic, the ripple effects still matter. Cross-border brands may see smaller retargeting pools in the UK and EU, lookalike models could get less reliable, and attribution may get a little fuzzier. None of that means paid social stops working, but it does mean marketers need to plan for a world where platform data is less complete than it used to be.
What To Pressure-Test In Your 2026 Plan
Start with reach and CPMs. Model what happens if 10% of your UK or EU audience disappears from TikTok and Meta targeting pools. Then look at how that affects budget, frequency, and performance expectations. This does not need to be perfect forecasting. It just needs to show where your plan gets shaky.
First-party data also becomes more important. When platforms lose visibility into certain users, brands with stronger customer data have more ways to understand demand, build audiences, and measure what’s working. That doesn’t mean collecting everything you possibly can, but it does mean collecting what you can actually use, with a clear value exchange that makes sense to the person handing over the information.
Creative deserves another look, too. If privacy-minded users are the ones most likely to pay for an ad-free experience, the remaining ad-supported audience may be more tolerant of ads, but not more forgiving of bad ones. Stronger hooks, clearer offers, and more useful creative will matter more as targeting gets less precise.
Policy calendars should be part of your planning rhythm. Watch state bills that mention universal opt-out signals, keep an eye on federal privacy proposals, and pay attention when major platforms test new consent models. The next change that affects your addressable audience may show up as a compliance deadline before it shows up as a platform announcement.
Paid Social Needs A Privacy Backup Plan
TikTok’s pay-or-consent test is another sign that ad-funded targeting is getting more expensive, more regulated, and less automatic. Marketers who treat privacy as a box to check will spend a lot of time reacting while marketers who treat privacy as a planning constraint will be in a better spot.
That means building around stronger first-party data, clearer measurement, and creative that can work without perfect targeting. Not exactly glamorous work, but very much the kind of work that keeps paid media from getting knocked sideways every time a platform or regulator changes the rules.
