WELCOME TO THE “FIND OUT” STAGE OF AI
As AI enters its "find out" stage, the industry is shifting from experimentation to delivering reliable real-world value for businesses.
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As AI enters its "find out" stage, the industry is shifting from experimentation to delivering reliable real-world value for businesses.
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When you crush a viral trend before 10am…? When one tiny tweak to a landing page sends click-through rates through the roof…? When personalization actually feels easy for once…?
The SEO job market has quietly repriced around AI skills. Roles with AI in the title pay about 27% more at the median. Jobs that mention AI in the description still pay about 25% more, around $100K vs. $80K. Most of this demand is hidden. Only 15.5% of postings include AI in the title, but 59.5% require it in the description. That means 4 out of 5 higher-paying roles are missed if you filter by title alone. The premium shows up at mid-level and above. At senior levels, AI is expected, not optional.
Creator marketing is growing fast, with US ad spend rising 4X faster than the rest of media. People skip traditional ads but spend hours with creators they trust. That makes execution critical. Creators should be involved early to shape the idea, not added at the end. Brands should use their voice and storytelling, not treat them like ad space. Teams also need to fund both proven plays and real experiments, not just label things as tests. Podcasts are a key format, especially video, where 36% of listeners are more likely to trust ads than on social media.
Topical authority is a site-level signal that can outweigh page quality in rankings. Google uses signals like siteFocusScore and siteRadius to evaluate how tightly a site's content clusters around a topic. Off-topic pages can weaken performance even if core pages are strong. Sites that stay tightly focused outperform those with drift because they present a clearer authority signal to ranking systems. It's important to audit all pages, identify outliers, and prune or redirect low-relevance content to improve focus and reduce drift. More content is not better, since unfocused expansion can dilute authority and hurt visibility.
AEO content needs to be far more specific and structured than traditional SEO pages. Instead of broad guides, it should target precise queries with clear context. The answer should come first, followed by supporting details and examples. Structure matters throughout, with headers, bullets, and sections that can stand alone since answer engines pull small chunks of text. FAQs help capture conversational follow-ups. Original data or insights increase the chance of being cited. Every section should tie back to the product so that relevance is clear. Schema markup helps machines interpret the page. Each section must work independently because AI models may only surface fragments, not the full page.
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Use the most powerful AI image models within a visual flow that lets you draw & annotate directly on images, compare results side by side, and keep projects clearly separated. Curious? Watch tutorials [10] to
Agent skills are markdown files that give your AI assistant context about specific frameworks, APIs, and workflows. This website provides 110 usable skills to optimize workflows, marketing tasks, and more. The skills are compatible with major agentic tools, including Claude Code and Copilot.
Google and Kaggle are bringing back their free 5-Day AI Agents Intensive Course, updated with new content focused on "vibe coding" workflows that use natural language as the primary programming interface. The course runs from June 15-19 and covers building autonomous agents with memory, tool integration, multi-agent systems, and cloud deployment.
The idea of “data-driven decisions” is mostly a myth. People don't rely on data alone. They use it to justify choices already shaped by emotion and bias. Emotions are not a flaw. They're necessary to make decisions at all. Data still plays a role, but it needs interpretation. That process is never neutral. It's shaped by experience, assumptions, and what we want to be true. The real skill is not removing emotion. It's recognizing how it influences what we trust and decide.
Spotify is expanding into fitness by turning its platform from a place for workout music into a place for full workouts. It launched a new Fitness hub with video and audio classes from wellness creators and a partnership with Peloton, adding over 1,400 on-demand classes for Premium users. The move builds on existing behavior - nearly 70% of Premium users already work out monthly and there are over 150M fitness playlists on the platform.
Pinterest announced that advertisers can now use its audience data for connected TV ad targeting. This is the first time Pinterest users have been retargetable outside the platform itself. Initial performance tests were strong, with Pinterest audiences delivering 27% better outcomes per $100 of ad spend than other third-party segments.
The X Chat app is now available for iOS, providing a dedicated home for X DMs.
35% of new websites are AI-generated or AI-assisted.
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A vibe coded prototype is nice, but it doesn't let you gather feedback or collaborate. With Miro Prototypes [4], you can validate concepts with your entire team in <1 day:
AI isn't fixing organizational problems - it's exposing them, especially gaps in communication, context, and alignment. Without a strong shared understanding and trust, both teams and AI systems fail.
Products start as wedges, but lasting advantage comes from becoming a control point others depend on. In an AI-driven world, control determines who wins.
Faster output doesn't equal better outcomes. Real progress requires time for coordination, reflection, and alignment. Over-optimizing speed with AI can worsen systemic issues, making thoughtful collaboration more important than ever.
AI can generate outputs, but outcomes require clear goals, context, and measurement. It amplifies good strategy or accelerates the wrong work.
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Most teams can build dashboards. Very few can scale them across users, teams, and products without creating fragmentation. Reveal's new report [10] shows how teams will scale intelligence in 2026. Read it to see how AI is changing & challenging analytics, and why business intelligence needs to be embedded directly into products. Get the
Great AI chatbots succeed through clear capabilities, accessibility, and guided interactions. Small design choices determine whether users find them helpful or frustrating.
Top apps win by building commitment, proving value early, and delaying the paywall.
AI speeds up building, but without strong feedback loops, teams create unverified systems. Real progress requires a harness to ensure fast iteration leads to learning.
Strategy and execution must evolve together, balancing clear direction with rapid learning. Great product work combines discipline with speed.
Data tools today answer questions people already know to ask, but the most important insights come from what they don't.
Early hypergrowth focuses on solving one key problem at a time, while late hypergrowth requires solving many at once for a broader, more skeptical market.
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Email backlog. Slack threads. Project updates. AI prompts. Doc edits. Wispr Flow [5] turns your voice into clean, sendable text in any app, so you clear the week 4x faster.
John Ternus will officially begin his tenure as Apple's new CEO on September 1. He will reveal Apple's biggest new product in a decade, the first foldable iPhone, less than two weeks later. Ternus oversaw the engineering and product development of the device, which makes him the perfect person to be the face of the new era of the iPhone. The launch will give the new CEO a tailwind in both buzz and revenue while letting Ternus tout a product that reflects his style.
X Money is expected to launch to the public before the end of this month. The banking and payments platform will provide free peer-to-peer transfers, a personalized metal Visa debit card, and an AI concierge that tracks spending and sorts through past transactions. It has some competitive perks, including 3% cash back on eligible purchases and a 6% interest rate on cash savings. X Money still lacks payment licenses in several states.
Tesla's Cybercab has officially entered production at the company's Giga Texas production facility outside of Austin. The Cybercab has no steering wheel or pedals. It relies fully on Tesla's advanced vision-only Full Self-Driving system. The start of Cybercab production is a signal that Tesla trusts its progress on Full Self-Driving and that autonomy is near.
Big tech's AI infrastructure buildout depends on a single Dutch company, ASML, which is the world's only supplier of the complex machines needed to make cutting-edge chips at scale. ASML is currently racing to meet the industry-wide surge in demand. It is building new facilities, repurposing existing clean rooms, and working on more advanced machines capable of churning out more chips. It is also hiring more engineers while cutting leadership roles to speed up decision-making. The boom has helped cement ASML's position as Europe's most valuable company.
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Struggling to debug your Next.js apps? This hands-on Sentry workshop [10] covers best practices for logs and tracing. Learn how to write high-context logs, setup querying workflows, and enable distributed tracing. You'll learn how to get full context without switching tools.
The role of the Engineering Manager is shifting. It is moving up an abstraction layer, so managers are delegating more managerial tasks to agents and then synthesizing and selecting where to go deeper. Some managers will do the same work with a bigger scope, but others will shift to more high-impact work. The purely operational manager role is at the most risk of being obsolete. Those who center their role on judgment, relationships, and organizational influence will become more valuable.
Last year, Shopify grew its internship program from roughly 100 interns per year to over 1,000. The company plans to continue the program this year and hire another 1,000 interns. Shopify hires interns to keep the company thinking with fresh eyes and to make sure there are always people questioning the way things are done. The new generation of interns will have grown up with AI - the company wants these types of people to reimagine what it looks like to build with AI.
It's understandable why people use AI. People want to use tools that promise quick, convenient solutions to problems. Many people have jobs only to make money to pay rent and medical expenses. AI is just a symptom of a broken system rather than the cause.
Google plans to invest between $10 billion and $40 billion in Anthropic, depending on whether Anthropic meets certain performance targets. Amazon recently invested $5 billion in Anthropic, with an option to further invest based on performance. Both investments value Anthropic at $350 billion. The investments are aimed at helping close the gap between demand and supply of compute for AI training and inference.
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Viktor is an AI coworker that sends invoices, audits Stripe, debugs checkout, runs AP. Works across 3,000+ tools. 9,000+ teams hired him.
Model capabilities are catching up, making harnesses less of a requirement.
Civilization-scale technological transformation is extraordinarily rare.
Creating a token usage leaderboard incentivizes more AI usage, which means producing a lot more real-world traces that can be used to train next-generation models better.
We're looking for an engineer/researcher at a major AI lab or startup to help write for 1M+ subscribers. Curators have been invited to Google I/O and OpenAI DevDay, scouted for Tier 1 VCs, and get early access to unreleased TLDR products. Learn more [21].
Several recent incidents have caused employees to ask questions in the company's internal Slack - the company responded by removing the conversations.
If Musk does win, the most likely outcome would be a money award well below the headline figure along with targeted governance fixes and guardrails such as independent board seats, mission-protective covenants, and constraints on licensing intellectual property.