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Ahrefs Brand Radar [5] tracks your brand across 471M+ real search-backed prompts. * EVERY MAJOR PLATFORM – ChatGPT, AI Overviews, AI Mode, Gemini, Perplexity, Copilot * BENCHMARK COMPETITORS side by side, on any domain * SEE WHAT AI CITES – the pages, videos, and Reddit threads behind each answer * CUSTOM PROMPTS – add your buyers' exact questions, checked daily
Brands are favoring mid-tier creators as engagement and measurable performance matter more than follower count. Creators with 50K–500K followers can deliver lower CPVs, while historical views, watch time, and audience fit increasingly shape pricing decisions. Mega creators still command premiums when they offer mass reach or cultural relevance, but brands are pushing for fees that better reflect expected campaign performance.
OpenAI can build an effective ad model if it keeps ads separate from answers and clearly labels them, similar to early Google AdWords. ChatGPT Ads reached $1B in annualized revenue in under 200 days, suggesting strong advertiser demand before the format has fully matured. Running shoes, travel, and price comparisons are especially promising because users now ask ChatGPT to research these commercial decisions instead of using Google.
Customer proof lands harder as a story or a stunt than as a logo row. Deel lists Snoop Dogg as a customer, Hinge commissioned novels from real couples' stories, and Shopify lit up the Las Vegas Sphere with every merchant's first sale. Smaller versions work too, like turning a "too advanced" one-star review into an ad or showing "customer since" dates under logos.
Great products still need strong stories that help people imagine how they fit into everyday life. Lead with the customer experience rather than technical capabilities, and show the human outcome the product enables. VisiCalc gave people a clear reason to buy the Apple II, while Tesla could frame FSD around making long-distance travel less exhausting rather than simply highlighting autonomous driving. Turn complex technology into a relatable vision of what life could look like.
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Ahrefs Brand Radar [11] tracks your brand across ChatGPT, AI Overviews, Gemini, Perplexity and Copilot — from 471M+ real search-backed prompts. Benchmark competitors, see what AI cites, add your own prompts.
Join the ChatGPT Ads team to explore 5 best practices for creating campaigns. Through guidance and live demos, they'll show how these practices come to life in Ads Manager. The free webinar will take place on September 24 at 9:30 AM PT.
This guide provides a step-by-step workflow for using Semrush and Claude Code to find and prioritize third-party pages that influence AI citations and brand visibility. It shows how to export citation data, configure Claude to analyze hundreds of pages for gaps or inaccuracies, validate its recommendations, and turn the findings into targeted outreach. It also covers tracking publisher responses and changes in AI citations over time to measure whether the outreach improves brand visibility.
AI is changing marketing without necessarily reducing demand for marketers. Leaner teams may be offset by the growing number of businesses needing distribution. Chat ads could become a major ad format as LLM usage grows. Shared repositories can preserve institutional knowledge and give AI better context. Employee-generated content can extend brand reach through trusted voices. As AI handles more digital interactions, IRL events may become more valuable for creating human connection and closing deals.
OpenAI's ad pixel uses a one-year __obi cookie tied to ChatGPT accounts and sends data to OpenAI when an advertiser's tag loads. It also collects hashed emails and phone numbers, unencrypted city and postal codes, and page paths containing medical and debt topics. The pixel works for logged-out users but appears limited to Chrome on Android, as iOS browsers block it.
Usage churn drives 26-40% of subscription app cancellations, often because users fail to build a lasting habit rather than because of price. Instead of defaulting to discounts, analyze engagement before cancellation to find where the habit loop breaks. Weak triggers may call for timely re-engagement, while poor onboarding or unclear value requires helping users reach meaningful outcomes sooner. Retention improves when the fix addresses why usage dropped rather than treating “not enough usage” as the root cause.
Growth marketers have an edge over executives through their proximity to the work and understanding of LLMs.
Only 35% of marketers trust AI-generated reports without checks, versus 64% for human reports.
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Moving from shipping features to solving customer problems takes more than reorganizing teams. How do you put the Product Operating Model into practice? “Adopting the Product Operating Model” is a practical guide to making the shift. Learn how successful product teams move from projects to products, empower cross-functional teams, and connect their work to customer and business outcomes.
While AI lowers software development costs, supporting a growing audience demands continuous learning and quality standards. Developers face challenges in managing user diversity and the risks of unchecked customization. Ultimately, maintaining quality requires an ongoing commitment.
The industry overlooks the primary stakeholder: the user. Current evaluation metrics measure machine functionality rather than whether an agent actually reduces effort for humans. Consequently, companies fail to instrument the complete customer journey, leading to hidden inference costs and unmeasured user abandonment.
"Not enough usage" is the primary driver of subscription app churn, often accounting for 26% to 40% of cancellations. This churn reason is frequently due to a broken habit loop rather than a pricing problem. Using discounts to fix usage churn is ineffective because lowering the price does not restore user value or engagement.
Every research question should be assigned a specific price in minutes, days, or an owned risk before any investigation begins. By implementing a five-step routing discipline, product teams can determine whether an answer already exists in organizational memory, warrants a rapid study, or should be bypassed entirely with an accepted risk written down.
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Most AI advice is hype or theory. Niamh Jones's course breaks down what AI changes versus what it just speeds up, plus a system for shipping without losing control. Find it in Pendo's Learning Lab: self-paced, free, with a badge worth sharing on LinkedIn. Explore the
The guide highlights key strategies for defining value propositions, crafting prompt assistance, and optimizing feature design to deliver meaningful user experiences.
Wade Chambers discusses the company's updated AI fluency rubric, outlining expectations for PMs across capable, adaptive, and transformative levels. Zapier expects PMs to operate primarily in the adaptive tier while running occasional experiments or building transformative systems, such as company-wide AI agent frameworks.
TLDR is hiring its first PM to help build the agent-first operating layer used across the company. We're looking for a builder who has shipped real products/systems with LLMs. Click here to learn more [13].
Personality labels can hide how much behavior depends on context. Understanding what causes people to switch modes may tell you more than knowing their “type.”
Applying generative AI to a dysfunctional product backlog masks underlying flaws by polishing artifacts without resolving root causes like missing customer evidence or decision authority. AI-generated user stories obscure a lack of real product discovery. To remain effective, product teams must maintain clear direction, authentic authority, and feedback loops tied to real outcomes.
Tools like Codex Browser Use and the Claude Chrome Extension excel at extracting design and product decisions from existing products.
Writing outcome-based OKRs for AI agent products requires measuring human behavior rather than agent activity.
Apple is set to introduce a smart home screen as early as next month. The display will serve as a hub for the home, bringing together connected-device controls, video calls, intercom capabilities, media, and access to Apple services and apps. It will feature a new operating system built around Siri AI. The device is currently being tested widely in employees' homes.
Anthropic has confirmed that it has a wet biology lab in the Bay Area where it uses AI models to run physical experiments. The lab's main focus is on fundamental biology rather than drug discovery. Anthropic is working on some research in the lab with external partners. The company has launched a Life Sciences Verification program to give vetted bio researchers access to its most powerful models.
Elon Musk's Boring Co. venture is working on a tunnel project in Texas to connect Austin and San Antonio. It has also raised $3 billion to support the development of underground transportation infrastructure across the United Arab Emirates. The company is building tunnels in Bastrop, Texas, to link Musk-owned properties and allow the employees of his companies to travel between them.
China's HL-4 tokamak is designed to achieve steady-state burning operation. It is aiming to become the world's first high-temperature superconducting, high-field steady-state burning experimental platform. The project's goal is to verify the reliability of high-temperature superconducting magnets under complex fusion conditions. Once the core scientific and engineering questions are validated, the program will construct a demonstration reactor to prove the technology commercially.
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Join Mike Cannon-Brookes (CEO & Co-Founder, Atlassian), Guillermo Rauch (CEO, Vercel), and leaders from Lovable, Dropbox, and more at State of AI SDLC, a digital summit for engineering and product leaders. Hear how they're adapting development practices, measuring AI's real impact, and working effectively with agents across the
You never want to be in a situation where people don't know what you are working on. However, instead of talking too much about work, assume everyone around you is operating in good faith and try to be a good teammate. Move away from an outcome-based mindset to a momentum-based mindset - by getting momentum going and fixing your relationships with people, you get stamina. Go for reliability and take the burden off your teammates and manager, because once you build that trust, they will give you bigger work.
AI will make many parts of the current software development environment obsolete. However, the craft of building software will become more important than ever. Models will become so fast that UI will be generated on the fly. It will take a while for this to happen. However, by that time, while people will still be employed to write code, it might not be such a desirable career anymore.
Palantir vertically integrated deployment to build the capabilities and process knowledge required to make building institution-specific economic computers as easy to build as possible. Vertical integration is fine if it's the only way to get permissioned access to the ground truth knowledge needed to help customers and society uncover new, valuable secrets. However, used to rent-seek, concentrate power, or build your customers' or partners' replacement, it jeopardizes the whole promise of economic computing: making progress programmable in a way everyone can contribute to and own. It also reduces institutional fitness and creates a broader neglective selection pressure in society.
People turn off their brains when they use LLMs. People assume the code they write will just work, and then if it doesn't, they ask the LLM to figure out the problem and solve it. Being a meat proxy is surprisingly effective now. If models keep improving, companies will be able to just run LLMs in a loop and lay off the employee.
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Tool calling, a 262K-token context window, and complex reasoning across coding, mathematics, and structured outputs. Meet GigaChat 3.5
Society should refuse to outsource moral responsibility to any class of people who claim a special competence and authority to calculate, accelerate, democratize, liberate, harmonize, or otherwise determine the 'correct' future.
If your arguments can justify anything, then your arguments imply nothing.
AI labs are simultaneously competing for mathematical results, selling mathematical assistance, and controlling most of the information required to evaluate either.
Every Firefox fork needs to commit to working together to maintain a hard fork isolated from Mozilla if the independent, non-slop web has any future at all.
China's views on American AI have grown more cynical, but that doesn't mean we should be pessimistic about safety coordination.
GitHub continues to suffer collateral damage from the AI boom.
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See their unique perspectives on topics such as agentic AI governance, risk management, evaluation, and deployment. Get your