<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Timothy Wong | Product, AI &amp; growth</title><description>Timothy Wong writes about building AI products and AI-native companies: company-wide AI adoption, AI-native GTM, and forward deployed engineering. Global VP of Data &amp; AI at Airwallex.</description><link>https://timothywong.co/</link><language>en</language><item><title>The Evolution of Engineering Interviews: A Few Thoughts</title><link>https://timothywong.co/writing/evolution-of-engineering-interviews/</link><guid isPermaLink="true">https://timothywong.co/writing/evolution-of-engineering-interviews/</guid><description>AI should be part of engineering interviews, alongside fundamentals, system design, evaluation, debugging, and production ownership.</description><pubDate>Sun, 27 Sep 2026 00:00:00 GMT</pubDate></item><item><title>The AI-Native Data Warehouse: Keep Truth Atomic, Make Aggregation Disposable</title><link>https://timothywong.co/writing/the-ai-native-data-warehouse/</link><guid isPermaLink="true">https://timothywong.co/writing/the-ai-native-data-warehouse/</guid><description>AI-native analytics needs atomic facts as the source of truth, governed semantic context, and pre-aggregated models as optional accelerators.</description><pubDate>Tue, 22 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Why Evals Matter in AI Analytics</title><link>https://timothywong.co/writing/why-evals-matter-in-ai-analytics/</link><guid isPermaLink="true">https://timothywong.co/writing/why-evals-matter-in-ai-analytics/</guid><description>A video walkthrough of evaluation in AI analytics, from checking a churn metric to explaining its causes and deciding what to do.</description><pubDate>Sun, 13 Sep 2026 00:00:00 GMT</pubDate></item><item><title>The Semantic Layer and Data Modeling Dilemma in AI Analytics</title><link>https://timothywong.co/writing/semantic-layer-data-modeling-dilemma/</link><guid isPermaLink="true">https://timothywong.co/writing/semantic-layer-data-modeling-dilemma/</guid><description>AI is driving self-serve data adoption, but accuracy depends on balancing core data modeling, curated context, and continuous evaluation.</description><pubDate>Thu, 03 Sep 2026 00:00:00 GMT</pubDate></item><item><title>AI Does the Work. People Decide What Matters</title><link>https://timothywong.co/writing/ai-does-the-work-people-decide-what-matters/</link><guid isPermaLink="true">https://timothywong.co/writing/ai-does-the-work-people-decide-what-matters/</guid><description>AI is removing traditional junior work while allowing junior people to contribute earlier. The real challenge is rebuilding judgment, apprenticeship, and accountability around that shift.</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate></item><item><title>Building an AI-Native Organization: Process, Technology, People</title><link>https://timothywong.co/writing/building-an-ai-native-organization/</link><guid isPermaLink="true">https://timothywong.co/writing/building-an-ai-native-organization/</guid><description>Adoption is easy to count. Compounding is what matters. My three-pillar framework for AI transformation explains why investing in just one pillar produces a pilot that plateaus.</description><pubDate>Thu, 09 Jul 2026 00:00:00 GMT</pubDate></item><item><title>What the Knicks&apos; 2026 Title Teaches Us About Analytics</title><link>https://timothywong.co/writing/how-the-knicks-won-the-2026-nba-title-despite-the-analytics/</link><guid isPermaLink="true">https://timothywong.co/writing/how-the-knicks-won-the-2026-nba-title-despite-the-analytics/</guid><description>The Knicks&apos; championship run is a reminder to distinguish player impact, team forecasts and the decisions made inside a series. An unexpected result is a reason to investigate, not proof that a model failed.</description><pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate></item><item><title>The Future of Data Science in an AI World</title><link>https://timothywong.co/writing/the-future-of-data-science-in-an-ai-world/</link><guid isPermaLink="true">https://timothywong.co/writing/the-future-of-data-science-in-an-ai-world/</guid><description>AI didn&apos;t kill data science. It killed the version of data science that was just running queries and writing decks. The work moves up the stack - from analysis to system, from model to harness.</description><pubDate>Thu, 07 May 2026 00:00:00 GMT</pubDate></item><item><title>From ML Models to AI Analytics Systems: What Transfers, and What Doesn&apos;t</title><link>https://timothywong.co/writing/from-ml-models-to-ai-analytics-systems/</link><guid isPermaLink="true">https://timothywong.co/writing/from-ml-models-to-ai-analytics-systems/</guid><description>ML platform discipline transfers to AI analytics systems - feature stores become semantic layers, drift detection still applies. The serving layer is where the bar goes up, and eval is the piece without a shared standard.</description><pubDate>Mon, 20 Apr 2026 00:00:00 GMT</pubDate></item><item><title>The 80% Is Commoditized</title><link>https://timothywong.co/writing/the-80-percent-is-commoditized/</link><guid isPermaLink="true">https://timothywong.co/writing/the-80-percent-is-commoditized/</guid><description>Anyone can build now. The next decade belongs to whoever closes the last 20% with real memory, context, and precision.</description><pubDate>Wed, 25 Mar 2026 00:00:00 GMT</pubDate></item><item><title>Nobody&apos;s Hiring for Output Anymore</title><link>https://timothywong.co/writing/nobodys-hiring-for-output-anymore/</link><guid isPermaLink="true">https://timothywong.co/writing/nobodys-hiring-for-output-anymore/</guid><description>The labor market isn&apos;t disappearing. It&apos;s repricing around who can think, decide, and own outcomes.</description><pubDate>Wed, 18 Mar 2026 00:00:00 GMT</pubDate></item><item><title>We&apos;re in the &quot;Agents as Employees&quot; Phase. It Won&apos;t Last</title><link>https://timothywong.co/writing/agents-as-employees-phase/</link><guid isPermaLink="true">https://timothywong.co/writing/agents-as-employees-phase/</guid><description>Most teams still define agents as human roles. The longer-term shift is toward workflow-native systems, review discipline, and context infrastructure.</description><pubDate>Sun, 15 Mar 2026 00:00:00 GMT</pubDate></item><item><title>AI-Native Is Not AI-Assisted</title><link>https://timothywong.co/writing/ai-native-is-not-ai-assisted/</link><guid isPermaLink="true">https://timothywong.co/writing/ai-native-is-not-ai-assisted/</guid><description>There&apos;s a difference between using AI and restructuring around it.</description><pubDate>Thu, 05 Mar 2026 00:00:00 GMT</pubDate></item><item><title>The real value of AI in analytics isn’t just answering questions. It’s closing the decision loop.</title><link>https://www.airwallex.com/global/blog/the-real-value-of-AI-in-analytics</link><guid isPermaLink="true">https://www.airwallex.com/global/blog/the-real-value-of-AI-in-analytics</guid><description>Decision latency is the real metric, not chat interfaces or dashboards.</description><pubDate>Thu, 05 Mar 2026 00:00:00 GMT</pubDate></item><item><title>OpenClaw Week Two: Four Observations on AI-Native Systems</title><link>https://timothywong.co/writing/openclaw-week-two/</link><guid isPermaLink="true">https://timothywong.co/writing/openclaw-week-two/</guid><description>Capability is no longer the bottleneck. Operational economics, context engineering, and governance are.</description><pubDate>Sat, 28 Feb 2026 00:00:00 GMT</pubDate></item><item><title>The Skills That Survive AI</title><link>https://timothywong.co/writing/the-skills-that-survive-ai/</link><guid isPermaLink="true">https://timothywong.co/writing/the-skills-that-survive-ai/</guid><description>What separates people who thrive in AI-native orgs from those who are stuck.</description><pubDate>Sat, 28 Feb 2026 00:00:00 GMT</pubDate></item><item><title>AI at Scale Isn&apos;t a Model Problem. It&apos;s a Systems Problem</title><link>https://timothywong.co/writing/ai-at-scale-is-a-systems-problem/</link><guid isPermaLink="true">https://timothywong.co/writing/ai-at-scale-is-a-systems-problem/</guid><description>Most companies don&apos;t have an AI problem. They have a data readiness and decision problem.</description><pubDate>Sat, 14 Feb 2026 00:00:00 GMT</pubDate></item><item><title>What AI-Native Actually Feels Like</title><link>https://timothywong.co/writing/what-ai-native-actually-feels-like/</link><guid isPermaLink="true">https://timothywong.co/writing/what-ai-native-actually-feels-like/</guid><description>Memory, tools and reusable workflows turn an AI assistant into an operating layer. The enterprise gap is the environment around the model.</description><pubDate>Sun, 18 Jan 2026 00:00:00 GMT</pubDate></item></channel></rss>