A Painting not a Ladder

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When you look at a painting from a distance, you see a larger, cohesive picture. But as you approach the canvas, you see that there are, in fact, hundreds of separate strokes that make up that picture. Think about your career as a work of art — expansive, independent movements that incrementally reveal a whole.

When we visualize a career ladder, we start putting ourselves in a box. Step back and see the painting — every experience adds a brushstroke to a bigger picture. 

Zainab Ghadiyali quoted in a FirstRound article 

How AI search (GEO) differs from SEO

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AI Overviews and AI Mode are dramatically changing organic search traffic. Content creators are focusing on “position zero” — that is, in the search snippet or AI Overview, which appears at the top of many Google search result pages.  

The process of optimizing your website’s content to boost its visibility to AI-driven search engines (ChatGPT, Perplexity, Gemini, Copilot and Google AI) through GEO (generative engine optimization) has some similarities to increased visibility to search engines (Google, Microsoft Bing) through SEO (search engine optimization). SEO is a sort of guessing game, a digital Jeopardy! in which the person creating web content tries to anticipate the query that will bring users to their content. GEO has the same goal, only toward AI overviews and AI mode.

The game has some similarities for both SEO and GEO. They use keywords and contextual phrasing, prioritize engaging content and aim to connect with conversational user queries. Both consider how fast a website loads, mobile friendliness, and prefer technically sound websites.  

However, while SEO focuses on metatags, keywords and backlinks, AI models are trained to provide quick, direct responses from the synthesized content gathered from multiple sources. GEO is about, not only the query, but information about the user — from their social media footprint to their Google Docs usage. This informs, not only the search at hand, but future searches. AI will evaluate who created the content, its trustworthiness, and how it fits within the broader knowledge graph the AI is using.

Generative search efforts, therefore, attempt to fit into this reasoning process. AI judges the content value, not just on whether it ends up a part of the final answer, but whether it helps the model reason its way toward that answer. This is why, despite performing all the typical SEO common practices, a GEO effort may not make it to the other side of the AI reasoning pipeline. It’s not enough to be generally relevant to the final answer. Your content is now in direct competition with other plausible answers, so it must be more useful, precise, and complete than the next-best option. In fact, the same content could go through the pipeline a second time and yield a different result. And since newer models are rapidly changing right now, the best GEO may be effective when using an older model but not with a more recently trained model.  

There is also a user shift to consider toward longer, more natural queries, from one- or two-word keywords to three- and four-word search terms. Research indicates that queries in AI mode are generally two to three times the length of traditional searches.  

What do AI Overviews avoid? Content that is overly generalized, speculative, or optimized for clickbait over clarity. Vague and generic writing underperforms. So what kind of content does the Google AI Overviews favor?

  • Content that contains the who, what, why

  • Straightforward content offering distinctiveness; AI rewards niche-specific content

  • Is written in natural, conversational terms (AI will attempt to deliver its answer in that same way)

  • Uses strong introductory sentences that convey clear value 

  • Has H2 tags (subheadings) that align with user questions

  • Is structured to match common question structures (open, closed, probing)

  • Answers complex questions

  • Allows for restatement of quires and implied sub-questions, where a main question is broken down into smaller parts; content structured in a way to be easily grabbed — in citable chunks

  • Contains multi-faceted answers

  • Is rich in relationships

  • Has explicit logical structures and supports causal progression

  • Has clear headlines

  • Cites sources and has clear authorship

  • Includes statistics & quotations 

  • Has multimedia integration

  • Content that tells the world something new

  • Uses HTML anchor jump links to connect different sections of content to one another

  • Podcasts that include full transcripts in YouTube video descriptions, which are easily searchable

  • Appears on YouTube (a Google-owned company) based on the titles, descriptions & transcripts of videos

More information:

What is AI reading? Takeaways from a report on AI brand visibility

How AI Mode and AI Overviews work based on patents and why we need new strategic focus on SEO

What is generative engine optimization (GEO)?

How To Get Your Content (& Brand) Recommended By AI & LLMs

Google Ads data shows query length shift post-AI Mode

The winners and losers of Google’s AI Mode

SEO Is Dead. Say Hello to GEO

Stephen Goforth

Loss Aversion

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People hate losses. Roughly speaking, losing something makes you twice as miserable as gaining the same thing makes you happy. In more technical language, people are “loss averse.” How do we know this?

Consider a simple experiment. Half the students in a class are given coffee mugs with the insignia of their home university embossed on it. The students who did not get a mug are asked to examine their neighbor’s mugs. Then, mug owners are invited to sell their mugs and nonowners are invited to buy them. They do so by answering the question “At each of the following prices, indicate whether you would be willing to (give up your mug/buy a mug).”

The results show that those with mugs demand roughly twice as much to give up their mugs as others are willing to pay to get one. Thousands of mugs have been used in dozens of replications of this experiment, but the results are nearly always the same. Once I have a mug, I don’t want to give it up. But if I don’t have one, I don’t feel an urgent need to buy one.

What this means is that people do not assign specific values to objects. When they have to give something up, they are hurt more than they are pleased if they acquire the very same things.

Richard Thaler & Cass Sunstein, Nudge

What a computer science degree should look like now

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Experts suggest that computer science degree requirements should move away from coding and align with the expectations of a liberal arts degree—critical thinking and communication skills, along with computational thinking and AI literacy. The new CS coursework would include basic principles of computing and AI, along with hands-on experience in designing software using new AI tools. AI tools can help with the building of prototype programs, check for coding errors and serve as a digital tutor. 

Computational thinking involves breaking down problems into smaller tasks, developing step-by-step solutions and using data to reach evidence-based conclusions. AI  literacy is an understanding — at varying depths for students at different levels — of how AI works, how to use it responsibly and how it is affecting society. Nurturing informed skepticism should be a goal.

Read more at the NYT: How Do You Teach Computer Science in the AI Era?

The Tyranny of Clock Time

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Clock time is that linear time by which our life is measured in abstract units appearing on clocks, watches, computers, and calendars. These measuring units tell us the month, the day, the hour, and the second in which we find ourselves, and decide for us how much longer we have to speak, listen, eat, sing, study, pray, sleep, play, or stay. Our lives are dominated by our clocks and watches. In particular, the tyranny of the one-hour slot is enormous. There are visiting hours, therapeutic hours, and even happy hours. Without being fully aware of it, our most intimate emotions are often influenced by the clock. The big wall clocks in hospitals and airports have caused much inner turmoil and many tears. 

Clock time is outer time, time that has a hard merciless objectivity to it. Clock time leads us to wonder how much longer we have to live and whether “real life” has not already passed us by. Clock time makes us disappointed with today and seems to suggest that maybe tomorrow, next week, and next year it will really happen. Clock time keeps saying, Hurry, hurry, time goes fast, maybe you will miss the real thing! But there is still a chance.. Hurry to get married, find a job, visit a country, read a book, get a degree…Try to take it all in before you run out of time.”

Clock time always makes us depart. It breeds impatience and prevents any compassionate being together. 

Henri Nouwen, Donald McNeill, Douglas Morrison from the book Compassion

LLMs & Retracted Research Papers

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Large language models should not be used to weed out retracted literature, a study of 21 chatbots concludes. Not only were the chatbots unreliable at correctly identifying retracted papers, they spit out different results when given the same prompts. On average, the 21 chatbots correctly identified fewer than half of the retracted papers. More at Retraction Watch

An experimental mock trial at the UNC School of Law raises questions about AI's role in criminal justice

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In a simulated trial, three AI systems acquitted a Black teenager of robbery charges. However, in the real case on which the mock trial was based, the judge quickly found the defendant guilty. The real conviction was appealed, but the North Carolina Court of Appeals upheld the verdict. More info

Video of the mock trial

An AI to Diagnose Your Sniffles

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Google has built an AI model that uses sound signals to "predict early signs of disease." It can identify subtle changes in your coughs, sniffles, breathing, and more. In places where there is difficulty accessing quality healthcare, this technology can step in as an alternative where users need nothing but their smartphone's microphone. For instance, it has been trained on 100 million cough sounds that help detect tuberculosis. More at Mashable

AI Definitions: Vector databases

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Vector databases – Raw data is converted into lists of numbers (word vectors) so that machine learning models can use them. The vectors are grouped together if they relate to one another. For instance, the word "king" would relate to a man, while "queen" would relate to a woman. A deep learning model (typically a transformer model) will use these vectors to "understand" the meaning of words and their relationships. More than 1,000 numbers can be used to represent a single word. If there are many numbers, then the word vector has a high dimension, making it nuanced. A low dimension for a word vector means the list of numbers is low. While not as nuanced, a low-dimensional vector is easier to work with. Vector data bases is what allows a language model to “recall” previous inputs, draw comparisons, identify relationships, and understand context.

More AI definitions

And how are you mad?

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All of us are crazy in very particular ways. We’re distinctively neurotic, unbalanced and immature, but don’t know quite the details because no one ever encourages us too hard to find them out. An urgent, primary task of any lover is therefore to get a handle on the specific ways in which they are mad. They have to get up to speed on their individual neuroses. They have to grasp where these have come from, what they make them do – and most importantly, what sort of people either provoke or assuage them. A good partnership is not so much one between two healthy people (there aren’t many of these on the planet), it’s one between two demented people who have had the skill or luck to find a non-threatening conscious accommodation between their relative insanities.

The very idea that we might not be too difficult as people should set off alarm bells in any prospective partner. The question is just where the problems will lie: perhaps we have a latent tendency to get furious when someone disagrees with us, or we can only relax when we are working, or we’re a bit tricky around intimacy after sex, or we’ve never been so good at explaining what’s going on when we’re worried. It’s these sort of issues that – over decades – create catastrophes and that we therefore need to know about way ahead of time, in order to look out for people who are optimally designed to withstand them. A standard question on any early dinner date should be quite simply: ‘And how are you mad?’ 

Book of Life