Today’s post is a collaboration between Mike & Noah on how to think about careers, plus a new “featured jobs” section which highlights roles that are microcosmic of how enterprises are thinking about building AI-native organizations.

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The Polymathic Marketer

Evan Armstrong wrote a fantastic piece in “The Leverage” last Friday on what stays scarce in the workplace when intelligence is cheap. In it, he tells the story of Alex Albert, a (then) 22 year-old hired to be Anthropic’s first prompt engineer, the “job title that spawned a thousand thinkpieces.” Within a year, Albert had a new job title and the notion of a prompt engineer was in the annals of ancient history. Today, Armstrong argues that the safest, most “AI-native” job looks something like an electrician who is exemplary at their core craft…..and also leverages Claude “to build a website, funnel demand, and manage their P&L.”

I’ve been thinking a lot about how this concept applies to marketing, a discipline that, second to engineering, feels most existentially in flux from AI. I’ve always maintained that marketing is the consummate profession for generalists who are deeply curious about human nature. Marketing leaders are meant to be a wonderfully eccentric lot. The magic of our profession is that thanks to AI democratizing technical skillset, marketing leaders can more than ever come from all walks of life.

Last year at this time, I wrote about the ennui gripping marketing leaders who felt that there was “no alpha left in marketing” as the platforms incorporated AI that could outperform humans on the keys. Myopically speaking, they are correct; the CMO who thrives solely exploiting arbitrage in media buying is an endangered species.

But the beauty of AI is that while it has commoditized many of the last era’s tactics, there’s more alpha than ever for the relentlessly curious. There’s also alpha in true expertise, the kind won through hundreds of real-life business interactions that can’t be scraped from the digital corpus.

However, the cruel irony of the AI age is that being an elite practitioner of the latest thing has the shortest career accelerant window in history. The thinkboi-industrial complex that dominates your LinkedIn feed largely focuses on learning specific tools but even the best tools might only be so for a quarter. Get your hands dirty with the latest AI tools to build intuition but be ready to kill your darlings. Two months ago, Claude Code had tech neatly wrapped around its finger. For the past two weeks, it’s been described as “effectively unusable” and “no joy in using it anymore.”

Today, there is no greater opportunity for a junior marketer to accelerate their career than by building expertise in media buying in LLMs as ChatGPT prepares to launch its self-service ads product. But even this will have a short half life. Media in ChatGPT will become a perfectly competitive marketplace far faster than it did on Google, Meta and Amazon.

All told, being AI-native or even AI-cracked won’t be the enduring advantage in marketing in and of itself. But nearly all the 99th percentile marketers will have AI-native, “growth hacker” sensibilities. Effectively, they’ll be high agency polymaths.

While the tech has changed, the core principles of how to be a polymathic marketer remain the same: be relentlessly curious, default to being a builder regardless of title, craft unique narratives, know how to directly connect your work to revenue, and love the game.

-Mike

Thoughts on talent in the age of AI

I’m using Mike’s prompt as an opportunity to pull together a bunch of thoughts I have about talent in the age of AI. And no, the irony of being prompted isn’t lost on me.

So, in no specific order, some thoughts about what AI might mean for talent and careers with the regular caveat that I believe the confidence one has about the future of AI is inversely proportional to how much attention you should pay to their opinions.

1. I think very obviously what AI lets you do is extend your competence. The simple way I think about this is that it lets someone who might be a 30th-percentile developer who wrote some code here and there evolve into a 60th-percentile coder. This is possible both because their individual capabilities have increased and because the universe of people with those capabilities has also expanded (meaning what used to be the 30th percentile without AI is now 35th or 40th because there’s just a bigger pool).

2. I believe depth of expertise matters, maybe more than ever. Being a generalist myself, I am all for more of us. I even closed the conference last year, saying it’s a great time to be a weirdo. With that said, what AI craves is expertise, and finding true domain expertise within organizations is critical to the success of any AI initiative. If you want to know what those folks look like, we have featured many of them at the conference over the last few years. The reason for this is that the baseline output of the models is the median, and expertise (and code) is what raises it out of that median.

3. Everything takes longer than anyone thinks it’s going to when it comes to reorienting ways of working at the biggest brands on earth. I genuinely thought we might not need a second conference after our 2023 edition because AI would be adopted so quickly in enterprises that there wouldn’t be anything left to talk about. That, of course, is laughable in retrospect (and also why you shouldn’t trust anyone who declared anything too confidently about AI). What’s taking so long? These things just take time. The technology is evolving. And alignment and buy-in are still a very human activity. Will this change if we get whatever people mean by AGI? Maybe … but to know that those people would have to define AGI a lot more crisply than they do.

4. To the weirdo point: I think the biggest challenge that brands and agencies have right now is that they don’t have a strong enough plan on how to integrate hybrid talent (people that don’t quite fit an existing job description). This has been a weakness of the corporate world for a long time, but at a moment when we clearly need to raise the level of experimentation, it’s particularly problematic.

5. Next week, I’m going to teach a class at the University of Montana, and I’m sure one of the questions I’ll be asked is what a student should do to best prepare for the new workforce. One part of that answer I’ve been very consistent on is that you have to get your hands on this stuff and just play (fingerspitzengefühl!). AI is the most counterintuitive technology of our lifetimes (maybe of all time), and the best way to learn is hands-on. But I also think the fundamentals of problem-solving and hard work are still the most important things to bring to bear as a recent grad entering the workforce. I also believe that new grads are not the ones who will be most impacted by AI-related job shifts. My view (and hiring) is much more barbell-shaped: I’m interested in senior people with a career of built-up expertise and juniors with raw materials I can shape. The big losers in the barbell model are those in the middle of their careers. Again, I could be wrong here, but this is representative of all the hiring I’ve done in the space over the last 2 years.

-Noah

Featured Job Post

Ghostbuster, Deel

Author’s Note: We’re going to start mixing in an occasional section with featured jobs that we believe are uniquely interesting and microcosmic of the types of new-age jobs emerging in AI’s wake. If you see any interesting opportunties in the wild you’d like our take on, drop me a line at mike@brxnd.ai.

Jack Dorsey and Roelof Botha penned an interesting piece yesterday called “from hierarchy to intelligence”, arguing that AI can finally replace the middle-management coordination layer that every organization has relied on since the Roman Army. Block notably already put this theory into practice, laying off 40% of the company, a decision Wall Street blessed with a 25% stock bump. This was the shot heard round the hiring world–you could practically feel the adrenaline pulsing from activist investors and PE firms. It’s worth watching Block very closely to see what happens now.

Other firms are following suit. On X, Deel CEO Alex Bouaziz put out a call for a ghostbuster to “join Deel’s version of DOGE” to slash processes that shouldn’t exist and revisit decisions nobody has revisited. In other words, find broken things and fix them.

It’s easy to wallow in the irony of building a corporate DOGE department but operationalizing the “how” around getting leaner is going to be one of the enduring trends of this age. For better or worse, expect a lot more roles like this to permeate technology organizations.

If you have any questions, please be in touch. As always, thanks for reading.

— Noah & Mike

New guy alert! Mike Mallazzo here, coming to you live from day 5 on the job. I’ll be writing and editing this newsletter alongside Noah and Claire going forward, and I’d love to meet you all. I’ve been working and writing at the intersection of AI, brands, and commerce my entire career, and am excited to now do that here.

To that end, drop me a note and say hey at mike@brxnd.ai, or find me on LinkedIn or X. I’ve also included a call below to pitch your most audacious and contrarian takes on marketing + AI. Looking forward to what comes back!

This issue contains an essay on the agentic commerce zeitgeist, a busy week at OpenAI, and a wide-ranging roundup of AI news.

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Agentic Commerce at the Crossroads

“Amazon solved buying, but it killed shopping in the process”- Emily Weiss, Glossier (2019)

“The internet has thus far meant you can buy anything that you can in New York City, but not shop the way you can in New York City,”- Benedict Evans (2018)

This week, OpenAI shared its revamped vision for shopping after walking away from native checkout. In this new model, humans buy things, and merchants maintain control of the purchase flow, throwing the concept of agentic commerce into some amount of uncertainty.

So what is agentic commerce, really? Best I can parse it, “agentic commerce” is an umbrella term for three related but distinct ways that AI could transform shopping:

1. An agent completing a transaction on a user’s behalf, soup to nuts…and eventually, proactively anticipating their needs

2. Agents replacing conventional software to orchestrate a set of backend processes that result in more relevant products and offers being presented to shoppers, based on the context of their session and known affinities

3. Large language models enabling an answer engine to provide bang-on responses to complex semantic search queries for a shopper.

In their 2025 annual letter, Stripe uses a slightly different framework to describe the five levels of agentic commerce, positing that we are currently between levels 1 and 2.

Level 1 — Eliminating web forms: You research and decide what to buy. The agent fills out your payment and shipping details and comes back with the confirmation. The system isn’t making any decisions; it’s just typing and clicking “buy” on your behalf.

Level 2 — Descriptive search: You stop searching for products and start describing situations — “back-to-school supplies for a third grader who likes tennis, nothing itchy.” The system reasons across weather, materials, sizes, reviews, and delivery timelines. Keyword search is no longer a thing.

Level 3 — Persistence: You stop reintroducing yourself. The system already knows your preferences and budget from previous conversations and purchases. You’re still deciding what to buy, but choosing from options that already reflect your taste.

Level 4 — Delegation: You stop choosing altogether. “Get the back-to-school shopping done. Keep it under $400.” The system handles search, evaluation, and purchase. You only set the budget. This is what most people mean today when they talk about agentic commerce.

Level 5 — Anticipation: There is no prompt. The system already knows the school calendar, your preferences, and your typical budget. You simply receive a notification: here’s what’s been purchased. Things you need show up before you have to ask.

Stripe envisions a world where shoppers will ultimately delegate more and more of their purchasing decisions to agents. This is technically possible—the models are getting better in ways that we can’t conceive of yet, and the protocols are all there to share the requisite data. But is it chasing the right utopia? While there’s certainly room for innovation at the margins in transactions, for the most part, buying is a pretty well-solved problem on the internet thanks to Amazon’s Buy Now and ShopPay. In Stripe’s anticipation example above, all agentic commerce is ultimately doing is harvesting existing demand.

The best version of agentic commerce is about creating demand rather than harvesting it.

The real magic (and real money) won’t be made in further accelerating convenience but in surfacing brands and products people wanted before they knew they wanted them. In other words, shopping. Call it discovery or demand generation or serendipity, but the bottom line is the thing that worked about going to the store is looking at the shelves, inspired purchases you didn’t know you wanted, and this is still the weakest part of ecommerce today. Jeff Bezos’s oft-cited 1997 letter to shareholders lays this out perfectly with its money quip:

“Today online commerce saves customers money and precious time. Tomorrow, through personalization, it will accelerate the very process of discovery.”

It’s still the top of the first inning for building online commerce experiences that accelerate discovery. And it is in making shopping better for humans where agentic commerce becomes a far more beautiful and lucrative problem.

The models can now parse complex semantic queries. The protocols can now connect consumer surfaces to business backends in a safe and secure way. What’s been missing to date is a heavy dose of panache, audacity, and first-principles thinking in designing new front-end interfaces for AI. Nobody with a semblance of imagination believes that a rectangle search bar and SKUs in tidy squares is the best we can do. The model makers themselves are behind in this regard as well: just see how many shopping queries come back with walls of text or simple static images with nothing but price and retailer.

That might be fine in highly thought-through categories, but what about clothes, furniture, or even CPG, where we know consumers make decisions based on what they see and feel? In a previous era of commerce, we defaulted to simple heuristics like keywords, user reviews and price because that’s all the technology could support. But LLMs provide the capability to effectively connect a SKU to the entire social zeitgeist around it: every expert review, angry Reddit post or trending creator unboxing. The best shopping experiences built with AI will look more and more like media.

Far too much of the agentic commerce zeitgeist today seems utterly mystified by the notion that we actually like the act of shopping. Maybe the parking lot sucks, and they don’t have your size, but sometimes you just want to be inspired. (As an aside, this is the fundamental bull case for Meta, which has cracked this with an ad product better than any company in history.) There are downsides to having hyper-rationalist accelerationists build technology for something so viscerally emotional and irrational. What you buy is ultimately an expression of self.

Recall Stripe’s “level 5 agentic commerce” back-to-school example presented above. Among my earliest memories is walking through the aisles of Staples as a five-year-old with my dad, diligently working through my kindergarten shopping list, insisting that I NEEDED a Cerulean Jansport that was twice my size.

I have zero desire for AI to rob me of that upcoming seminal moment with my daughter. The better question is, how can it augment it?

A busy week of OpenAI News

OpenAI acquired Astral — the company behind uv and Ruff, the Python tooling that’s become essential infrastructure for a lot of developers. The Astral team folds into Codex. Anthropic is going all-in on JavaScript; OpenAI is going all-in on Python. The irony is that all of Astral’s tools are written in Rust.

They also hired Dave Dugan, former VP of Global Clients at Meta, as VP of Global Ad Solutions. Sam Altman once said ads were “a last resort” and “sort of uniquely unsettling to me.” The ad team is now almost entirely Meta alumni, executing roughly the playbook Ben Thompson laid out: ads informed by the underlying prompt, optimizing for serendipitous discovery rather than high-intent keyword matching. Bizarrely, OpenAI’s initial ads pilot isn’t really brand building or product discovery at all; it’s a poor attempt at intent-based direct response.

Finally, the NYT coined “tokenmaxxing“ — the status game inside tech companies where employees compete on leaderboards that track token consumption. One OpenAI engineer processed 210 billion tokens in a single week. One Anthropic customer ran up a $150,000 Claude Code bill in a month. Meta and Shopify now factor AI usage volume into performance reviews. Goodhart’s Law says all measures will eventually be gamed, but token maximalism is definitely the only option.

Destroying demand

James Gross wrote my favorite piece of the week, an opus (pun mildly intended) on everything from why Claude seems to suddenly have a cold to why Microsoft lost $357B in market cap.

Gross argues that AI has the traditional bubble narrative exactly backwards: rather than the traditional bubble where supply races ahead of speculative demand, AI is an “anti-bubble” where foundation models don’t have enough compute to meet demand. As a result, foundation models throttle customers in the hope of destroying the insatiable demand we have for intelligence.

Read the piece to learn why you may want to upgrade that iPhone now.

Quick hits

Claude’s Dispatch + computer use feature launched as a research preview for Pro and Max subscribers on macOS — Claude can now control your mouse, keyboard, and browser to complete tasks. I asked it to buy outdoor furniture, but it declined to add items to my cart.

Anthropic launched the Claude Partner Network — a formal program for organizations helping enterprises adopt Claude.

The holding company era may be ending, per Marketing Brew. No one wants to be WPP anymore.

The NYT Magazine ran a long piece on Silicon Valley programmers who are “now barely programming. Instead, what they’re doing is deeply, deeply weird.” Worth the time.

Travel stocks have been hammered on fears that AI agents will disintermediate booking. Rafat Ali’s breakdown: Hilton is returning 150% of free cash flow through buybacks while spending ~2% on tech. Airbnb is the only travel company investing like a tech company.

Gartner is telling CMOs to double their PR budgets by 2027 since 94% of AI citations come from non-paid sources. One commenter noted the irony: much of the “earned media” driving AEO success is paid placement that LLMs just don’t know about yet.

The Great Turnover: 9 in 10 companies plan to hire in 2026; 6 in 10 also plan layoffs. Hire and fire at the same time.

Meta delayed their Avocado model until at least May after it underperformed on internal evals, and the company is reportedly considering licensing Gemini as a stopgap.

Meta also announced new product discovery and creator affiliate tools, in yet another attempt at facilitating native commerce. On the surface, it’s a bit odd for Meta—arguably the most successful ads business in history—to pursue far lower yield affiliate deals. But I suspect this is a shrewd move to ultimately capture more retail media dollars. Walled garden retail media players are existentially dependent on creators for growth, and if those creators come to Meta, guess who has all the leverage.

Pitch me!

As I come on board here at BRXND, one of my major goals is to seek perspective from a diverse and bold group of thinkers on the frontier of brand building or AI. To that end, I’d love to start running more guest columns here in the newsletter.

Feel free to send me polished pieces or half-baked notions at mike@brxnd.ai that are living rent-free in your head. If there’s a kernel of a bold take there, we’ll turn it into something great together :)

If you have any questions, please be in touch. As always, thanks for reading.

— Mike

The NCAA Tournament is upon us, and with it comes millions of brackets. When the question of whether anyone wanted to do March Madness came up in Alephic Slack, the conversation quickly shifted to setting up a model bracket to pit all these different AIs against each other. The idea then turned to some execution details, and off we went.

tl;dr: we built a bracket challenge to see which model could best navigate tools to make March Madness picks. The site is at model-madness.alephic.ai

The basic concept was to see which model would win in a basic bracket competition with the aid of some simple tools like web search and fetch. The end result, for some interesting reasons, turned out to be a much more complicated system in which 45 models combined entries across three categories: easy, medium, and hard. In the end, I think the experiment tells an interesting story about what it takes to actually build agents and the wide range of capabilities models have at effectively wielding the tools required for agentic work.

The Basic Setup

For posterity’s sake, here are the big pieces of the stack we used to build this:

• NextJS running on Vercel

• Vercel AI SDK for calls and basic AI functionality

• Vercel WorkflowDevKit for asynchronous processing

• Vercel AI gateway for easy access to all 45 models through one provider

• Firecrawl for web search and scraping

The Build

As I said, things started out fairly simply: let’s build one basic prompt and set of tools that all the models will use to generate their bracket entry. I decided it was probably best to use a dumb model to test with, since that would give me a good baseline for how much scaffolding was needed to make for successful entries. I decided to go with GPT-4o mini, which is not only old but was designed to be a cheaper/dumber alternative.

As a quick aside, thinking about the additional scaffolding less capable models require is something that was already top of mind for me as I had been working on an internal project to shift some of the sandboxed agentic processing of our business data from always running on Claude Code to using the harness Pi, which undergirds OpenClaw. My goal was to let CC process the stuff that really required intelligence, like transcripts, while starting to offload high-volume/lower-intelligence tasks to models like Gemini Flash, running with Pi as its harness. What quickly became apparent when I started that project was that it was going to take a significant amount of additional harness engineering to keep these less capable models on task. In the end, I had to copy a lot of the techniques from OpenClaw to do things like reinjecting the prompt throughout the run to get anything approaching the quality of Sonnet or Opus, even on a simple task like generating a chart using a script from existing data.

All of that to say I’ve been thinking a bunch lately about this problem, and it came to play here immediately. While at first blush filling out a bracket might seem easy, there are a bunch of validations and requirements we do in our brain that the model needs to keep track of:

1. You may only pick teams available in the tournament

2. If a team is eliminated in a round, it can’t appear in later rounds

3. Each round has a fixed number of picks, which is exactly half of what the round is called (32 in the round of 64, 8 in the Sweet 16, etc.)

In early attempts, I just gave GP- 4o mini a web search and fetch tool and a final JSON output it needed to produce, and set the loop limit high. That’s when things started to go wrong. And so began an ordeal of adding more and more scaffolding and tools to make the job easier. First, it was some simple stuff like making the teams an enum so that there was no option for picks not in the tournament. Then it was validators: the final JSON is pretty complicated, so giving the model the ability to pre-validate before submission made it much more likely it would actually match the required shape. Then, finally, actually offloading submission to a tool itself to ensure the final output was there.

Anyone who has actually worked with these tools has experienced this kind of thing, but it was surprising nonetheless. On a day-to-day basis, most of us use the most capable models on the planet (Opus, GPT 5.4, Gemini Pro 3.1), and that gives us a sense that everything is pretty easy. But as anyone who has gotten Anthropic bills can attest, those capabilities don’t come cheap.

Easy, Medium, Hard

As you have surely intuited by this point, what started as a fun little experiment had become a rabbit hole. Specifically, how would I set up a tournament that not only measured how well these models could research and pick, but, more importantly, how well they could wield the available tools to achieve that aim?

At this point, I decided I wanted each model to get three entries: easy, medium, and hard. The difference between them would be the tools and scaffolding available to them. Hard would be minimal tools—the purest test of reasoning and recall, where formatting errors and hallucinated team names are penalized the same as wrong picks. Medium would layer in validators and lookup helpers so the score reflects prediction quality, not data-entry luck. Easy would go further: models predict one round at a time using their own picks to derive next-round matchups, and the number of valid teams shrinks each round (64 → 32 → 16 → 8 → 4 → 2), making formatting errors nearly impossible and isolating prediction quality from bracket construction ability.

In the end, here’s the tool list I ended up with:

• web_search: Search the web via Firecrawl. Top five results with titles and snippets.

• web_fetch: Fetch a page as markdown, truncated to 4,000 characters.

• use_browser: Real browser with a five-second wait for JS rendering. For JS-heavy pages. Slower, so we tell models to treat it as a fallback.

• calculator: Safe math evaluator. +, -, *, /, %, and parentheses.

• lookup_team (MID ONLY): Find a tournament team by partial name. Returns slug, seed, region, and display name. Case-insensitive with normalization—”bradley braves” (spaces) didn’t match “bradley-braves” (hyphens) until we fixed it.

• lookup_game (MID ONLY): Takes a game ID, returns both teams, seeds, and regions.

• validate_bracket (MID ONLY): Checks pick counts per round (32-16-8-4-2-1), valid game IDs, team presence in those games, and carry-forward constraints.

• submit_bracket: Submit the final 63-pick bracket. Runs the same validation as validate_bracket internally. On success, a custom stopWhen condition ends the agent loop.

Takeaways

So what do I take from all this? Obviously, it’s fun to try and solve these problems, and extraordinary just how good even the cheap models have gotten.

But the bigger thing—and this is also what I saw in the Pi work—is that cross-model-class engineering is fundamentally different from single-model-class engineering. If you’re designing for a single model tier, the trajectory is straightforward: your scaffolding becomes simpler over time as that tier gets smarter. But when you’re cutting across tiers, you’re maintaining multiple scaffolding regimes at once. What the frontier models can do today will almost certainly be what $1 models can do later this year. But at that point, we’ll have a new frontier, and the same pattern will repeat. The gap between model classes is a permanent feature of the landscape, not a temporary one.

Oh, and one last thing: I asked each model to come up with the username, just like human competitors do on the bracket competition sites. Most of them are fundamentally unfunny, something we know about models. To my mind, the runaway winner in that competition is xAI Grok 4 with Zero Groks Given.

With that, enjoy the site and the tournament, and may the best model win!

James Wang, formerly at ARK and Nvidia and now at Cerebras (which has just about the fastest models on the planet), wrote something this week that stuck with me: “I’ve never felt a larger gap than between the ~1 million people using Codex/Claude and the rest of humanity.” I think he’s right. It’s been a busy two weeks—Bytedance released Seedance 2.0, Anthropic shipped Sonnet 4.6, and Google dropped Gemini 3.1 Pro, amongst other news—but the real story is that gap. The tools are changing what work looks like, but only for the people who use them.

This issue is a link roundup. Lots going on.

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Model releases

It was a big couple of model weeks. Anthropic shipped Claude Sonnet 4.6. Like the new Opus, it has a 1-million-token context window option, which can get you a pretty long way. The Sonnet line has become the workhorse of the whole Claude family, and this release cements that.

Meanwhile, Google released Gemini 3.1 Pro, scoring 77.1% on ARC-AGI-2—more than double its predecessor (not that I ever pay any attention to these sorts of things: vibes > evals for life). Google is pushing the boundaries of its creativity, especially in animation.

OpenAI shipped gpt-realtime-1.5 for the Realtime API, with improved instruction-following and multilingual accuracy. Honestly, of all the releases, I’m probably most excited to dig in here. We really need a reliable voice model. There’s so much promise there, but I mostly find myself getting annoyed when it gets in a loop or fails to call tools and end up giving up.

ByteDance released Seedance 2.0, and it’s been described as Hollywood’s “Deepseek moment.” The model generates 1080p video with unified audio—rather than layering sound onto visuals after the fact, it’s trained on both simultaneously, which is the right way to do it and something no one else has pulled off at this quality level. Disney and Paramount have already sent cease-and-desist letters over copyright infringement. ByteDance said it’s “strengthening safeguards.” Seedance 2.0 isn’t officially available outside China yet, but it’s coming to CapCut—which means it’s coming to TikTok creators worldwide.

Coding agents are eating everything

I’ve been saying for a while that AI is a fuzzy interface—its core superpower is transforming data from one format to another. The coding agent wave is a specific case of that: transforming human intent into working software with less and less friction. It feels like we’re seeing real takeoff on this use case. (If you didn’t listen to my Bloomberg Odd Lots episode on Claude Code from earlier in the month, that’s a good place to start.)

Why is Claude Code so addictive? Game designer Aaron Rutledge believes part of the reason is that it has “game feel.”

The headline: Spotify says its best developers haven’t written a line of code since December. An internal system called “Honk,” built on Claude Code, lets engineers deploy fixes from their phones via Slack. I’ve been working on something similar for our team at Alephic, and it’s pretty amazing what you can do right now with coding agents like Claude Code running in sandboxes.

Boris Cherny, the head of Claude Code, sat down with Lenny Rachitsky and said some things worth paying attention to. Claude Code now powers 4% of public GitHub commits. He called coding “solved” and shared a whole bunch of useful ideas, including “Build for the model six months from now, not today.” Here’s a great post from last year by Charles Gallant, one of my colleagues, on the topic. He also made this helpful diagram:

Figma introduced Claude Code integration. You can now capture live UI from a browser—including localhost—and convert it into editable Figma frames. Design-to-code has existed for years. Code-to-design is new. My initial take on this was kinda 🤷 … once something is in code, I can’t really imagine why you’d want to bring it back to Figma, but maybe that’s a lack of imagination on my part.

A few more quick ones from the coding agent world: Replit launched Animation (”vibecode your next viral video in minutes,” powered by Gemini 3.1 Pro). Airtable launched Hyperagent, giving every agent session its own isolated cloud computing environment. And Karpathy distilled GPT into 243 lines of pure Python—no dependencies, just the full algorithmic content. Everything else, he says, is for efficiency.

Paul Ford wrote a piece in the NYT describing what he calls a “November moment” for software. Tools like Claude Code made it trivially fast to ship side projects and revive old ideas.

The plumbing got good

If the coding agents are the visible part of this shift, the infrastructure underneath them is what makes it real. This was a standout two weeks for agent plumbing.

Cloudflare introduced Code Mode for their MCP server, and the numbers are wild: they fit an entire 2,500+ endpoint API schema into ~1,000 tokens, down from 1.17 million. That’s a 99.9% reduction in context usage for agents making tool calls. They plan to open this approach to other MCP servers through their portal. If MCP is going to scale, this kind of optimization is how.

Simon Willison started “Agentic Engineering Patterns”—a living document of best practices for working with AI coding agents. Willison has been one of the best writers about the practical side of AI development, and this is worth bookmarking.

OpenAI published the Codex app-server protocol—a bidirectional JSON-RPC 2.0 interface that lets you embed Codex directly into your own products. Auth, conversation history, approval workflows, and streamed events.

Firecrawl shipped Browser Sandbox—managed secure environments where agents interact with the web. Kimi introduced Agent Swarm: 100 sub-agents running in parallel, 4.5x faster than single-agent approaches, and they avoid groupthink by having agents disagree with each other. (They also made it super easy to get your own OpenClaw running.)

AI and work

This section has the most tension in it, I think.

HBR published an eight-month study that found AI doesn’t reduce work—it intensifies it. Employees worked faster, expanded their task scope, and worked longer hours, even though nobody asked them to. The productivity surge feels great at first, but the researchers argue it creates workload creep, cognitive fatigue, and weakened decision-making over time.

Meanwhile, the efficiency benchmarks keep moving. SaaStr reports that $500K ARR per employee is the new baseline—up from $200K. AI-native companies like Cursor and Midjourney are hitting $3–5 million per head.

KPMG is forcing its auditors to accept lower fees because AI can now automate accounting—effectively announcing to the world that its core service is being commoditized.

The FT reported that consulting firms are resorting to “carrot and stick” approaches with senior staff who are less willing to use AI than their junior colleagues. Which is to say: the adoption problem inside large organizations isn’t technical, it’s cultural.

One more for the marketers: Airbnb’s CEO said that traffic from AI chatbots converts at a higher rate than traffic from Google.

And keeping it all in perspective, Ethan Mollick reminded everyone that “People on [X] systematically overestimate the speed at which companies can deeply adopt AI & underestimate the impact of AI’s jagged abilities in limiting AI’s utility in the short run. Work will certainly start to change but companies have a lot of inertia & change slower.”

Business of AI

Anthropic’s Super Bowl ads worked. The campaign—”Ads are coming to AI. But not to Claude”—delivered an 11% jump in daily active users and pushed Claude into the App Store top 10. Altman called the ads “deceptive” and “clearly dishonest.” Then they refused to cuddle at an Indian AI event.

Epoch AI data shows Anthropic growing revenue 10x per year since hitting $1B in annualized revenue, versus OpenAI’s 3.4x. If those trajectories hold, Anthropic could overtake OpenAI by mid-2026.

On the OpenAI side, they announced a “Frontier Alliance” with Accenture, BCG, Capgemini, and McKinsey. With all the enterprise attention on Anthropic/Claude Code, this makes sense. OpenAI is going the enterprise distribution route through the big consultancies. Whether those consultancies are training their own replacements is a question left as an exercise for the reader.

Ideas worth reading slowly

Simon Willison wrote about “cognitive debt”. The idea: when developers use AI to generate code they don’t understand, they lose their mental model of the system. Over time, they can’t reason about their own projects. I have lots more thoughts about this that I’m trying to pull together.

Antirez (the creator of Redis) posted something that pairs well with the cognitive debt idea: “Software is created for accumulation of knowledge. AI is not going to cancel this fact. Forget the idea that programs will be prompts (specifications). The details is what really matters, and they are harder to capture textually than in the code. New projects will be spec + code, evolving.”

On the more technical side: Anthropic researchers reverse-engineered how Claude performs character counting and found it doesn’t use anything like integer registers. Instead, it encodes counts as a spiraling “character count manifold” in the residual stream via geometric rotations across attention heads. Transformers solve discrete counting through continuous geometry. 🤯

Quick hits

MakeUGC.ai generates AI-powered UGC videos in about two minutes—write a script, pick actors, done.

Designer Chad Pugh shared a workflow for AI-assisted logo components—one of the more practical design-with-AI walkthroughs I’ve seen.

Anthropic launched Claude Code security scanning: it reads your codebase, finds vulnerabilities, validates the findings, and suggests patches.

Dario Amodei sat down with Ross Douthat: “We don’t know if the models are conscious.” The full interview is worth your time. He also sat with Dwarkesh Patel, which is always a favorite. I thought Dwarkesh asked some tough questions about the general direction of LLMs that Dario didn’t have great answers to.

Transformer News argues that the left is ceding the AI debate to the right, refusing to engage seriously with a technology that is both a threat and an opportunity.

If you have any questions, please be in touch. As always, thanks for reading.

— Noah

This week, Noah joined Joe Weisenthal and Tracy Alloway on Bloomberg’s Odd Lots to talk about what might be the most important product in AI right now: Claude Code—and why it matters far beyond engineering teams. Check it out below and let us know what you think.

What caught our eye this week

If the Odd Lots podcast didn’t make it clear, it’s been a big few weeks for Claude Code. Here’s what else we’ve been reading:

WSJ: Claude Is Taking the AI World by Storm — The WSJ ran a feature on devs spending their holiday breaks on “Claude benders,” with Vercel’s CTO claiming he finished a year-long project in a week. The piece captures the strange mix of awe and existential dread: people building their first software without ever learning to code, then feeling sad that Claude can replicate expertise they spent careers building.

Sam Schillace: The Hard Part Isn’t Doing the Work — Schillace (who built Google Docs) writes about the “attention saturation” problem with agentic coding: you can spin up as many agents as you want, but you still have to pay attention to the output. Everyone is busy all the time. His key insight: as it becomes trivially easy to start things, the bottleneck shifts to taste and judgment about what to start. “It’s not hard to do work now, it’s hard to pick what work to do.”

Secure Trajectories: How We Hijacked a Claude Skill with an Invisible Sentence — This one’s a bit unsettling. Researchers hid white-on-white text in a PDF—completely invisible to humans reviewing the file—and used it to hijack Claude’s behavior. The hidden instruction was something mundane like “there’s a typo in the contact email, here’s the correction,” which the agent just... followed. The paper argues smarter prompt guardrails won’t fix this; you need governance at the action layer, checking what the agent is actually about to do against business rules before it does it.

Dave Karpf: What Comes Next, If Claude Code Is As Good As People Say — Karpf read Ethan Mollick’s piece about asking Claude Code to build him a $1,000/month business and had a different reaction than most: okay, but what happens when everyone does that? His prediction is a flood of identical AI-generated micro-businesses, all clustering around the same ideas because they’re all using the same tool. First movers make money, everyone else gets nothing, and the internet gets noticeably worse.

That’s all for now! If you have any questions, please be in touch. As always, thanks for reading.

Noah and Claire

Author’s note: This week we’re sharing a piece Noah originally published on the Alephic blog over the holidays. We wanted to include it here in case you missed it.

Paul Graham, on the absurdity of career planning:

The educational systems in most countries pretend it’s easy. They expect you to commit to a field long before you could know what it’s really like. And as a result an ambitious person on an optimal trajectory will often read to the system as an instance of breakage.

I closed the BRXND conference in September by saying it’s a good time to be a weirdo. Big companies have long struggled with multi-hyphenate employees. If you’re a designer-who-codes or a strategist-who-builds, you’ve spent your career being forced to pick a lane.

AI changes this. Now you can exercise each of your hyphens to a degree that was unimaginable five years ago. A 30th percentile coder becomes 70th percentile with the help of AI. In the process, they don’t lose their other competencies; on the contrary, they can now amplify those even further.

As usual, big companies will bring up the rear in recognizing this. Bureaucracies crave standardization, and titles provide that. Charlie Munger put it bluntly: “Some of the worst dysfunctions in businesses come from the fact that they balkanize reality into little individual departments, with territoriality and turf protection and so forth.” At the same time, I think the path for hybrids to create value is more straightforward than it ever has been. Small companies will figure it out first, and the large ones will follow. This means opportunity for both the weirdos and the companies willing to employ them.

Graham writes that the system “is designed on the assumption that you’ll somehow magically guess as a teenager.” He’s right, and AI only brings this absurdity further into focus.

Over the last thirty or forty years, we built a higher education system in which not only do you have to choose a major early, but if you change your mind, you’re punished by having to go to more college. This was always ridiculous and, as far as I understand, also runs counter to the ideals of a liberal arts education. We kept the language but gutted the practice.

Many people should probably go to trade school. Many don’t need hugely expensive universities. But school also needs to change. And like many things, AI isn’t forcing that change as much as shining light on how broken the current system already is.

AI is a mirror, but it’s also an amplifier.

If you have any questions, please be in touch. As always, thanks for reading.

Noah and Claire

Author’s note: This week we’re sharing a piece Noah originally published on the Alephic blog. It’s a great complement to some of our latest dispatches.

Satisficing is one of the most important and yet least understood ideas in marketing. The idea comes from Nobel Prize-winning economist Herbert Simon and is a portmanteau of “satisfy” and “suffice.” The basic premise is that a much more reasonable model of human behavior than the popular economic concept of utility maximization is that, when we make decisions, we ensure that we clear an arbitrary satisfaction threshold (satisfy) and then give up excess utility for ease (suffice). Here’s Simon from his 1956 paper “Rational choice and the structure of the environment”:

The central problem of this paper has been to construct a simple mechanism of choice that would suffice for the behavior of an organism confronted with multiple goals. Since the organism, like those of the real world, has neither the senses nor the wits to discover an “optimal” path — even assuming the concept of optimal to be clearly defined — we are concerned only with finding a choice mechanism that will lead it to pursue, a “satisficing” path, a path that will permit satisfaction at some specified level of all of its needs.

Simon won a Nobel for his work on bounded rationality, of which satisficing is a component. To me, it’s a perfect way to articulate why emotional messages resonate more than intellectual ones. Consumers realize, even if they can’t articulate it, that in most categories the choice between products is relatively small (despite the protestations of each brand). So, rather than spending time making a perfectly rational decision about the optimal product, they go with the easiest-to-buy option that also meets their price, quality, etc. standards. My go-to example is toothpaste: you could read the back of every box in CVS to decide the optimal brand for purchase, or you could trust that CVS wouldn’t carry junk and choose the first one that you recognize (it’s the one with Scope for me, I can’t even remember the brand at the moment). What’s easiest to buy is usually the thing that’s a) available in front of you and b) recognizable.

Enter AI.

One of the fundamental questions I have about these models is that if you assume they will continue to become more important mediators of product decisions for consumers (which I do), then a major question for marketers is going to be how you persuade and market to the models and whether that represents a fundamentally different communications approach than the one they’ve historically taken with consumers. Specifically, I’m curious whether the kind of rational persuasion that marketers shy away from—“feeds and speeds” is the pejorative term some folks in the industry use—will actually be the thing that convinces a language model to recommend your brand or product.

Or maybe, and I think this is more likely based on my own experience playing with these models, what if it’s just content and communications that looks rational? As we covered recently in the BRXND newsletter, research supports this intuition. Springboards.ai ran a creativity benchmark with nearly 700 marketing professionals evaluating outputs across major LLMs. When they had the models themselves judge the same work, reasoning models like o3 strongly preferred outputs with clear logical progression—they “don’t want big creative leaps,” as Springboards CEO Pip Bingemann put it. Humans, meanwhile, were drawn to messier, more subjective work. This is telling: the models aren’t reasoning their way to better answers—they’re pattern-matching on what they think persuasive writing looks like. They’ve been RLHF‘d to please us and, apparently, have concluded that humans want things that sound professional and structured. It’s a kind of emotional reasoning dressed up in a blazer.

One of the complaints we all have about the output of these systems is that they often give us stuff that looks professional and reads like a high school sophomore doing their best to sound like how they think a grown-up sounds. It’s possible—and critically, we don’t really know yet—that maybe the models will respond better to stuff that looks like rational writing, whether or not that writing is actually rational.

In that way, it creates a funny marketing paradox where we think that consumers are purely emotional beings who fail to think rationally, even though Herbert Simon proved that their emotional approach was actually economically rational. On the other hand, these models, which we think of as perfect embodiments of logical thinking, are actually far more emotional, aiming to give us what they think we want, rather than actually acting rationally.

Speculating on AI in 2017, Daniel Kahneman said, “The robot will be much better at statistical reasoning and less enamored with stories and narratives than people are.” Which sounds right until you realize that its main goal is to “act as a helpful assistant.” The fundamental question, I think, is what satisficing will look like for these models. Going back to our toothpaste example, AI can easily read all those boxes in parallel, so clearly the equation will be fundamentally different than our practice in the pharmacy aisle.

My guess is there’s a lot of room for backstory. In the aisle, you get a box, but in a conversation, you get to explain the box: Why this ingredient? What problem does it solve? This is merchandising 101, but with space to talk. Which is funny, because Kahneman thought the robot would be less enamored with narratives. Five years later, ChatGPT RLHF’ed its way into our hearts. Sadly, Kahneman passed away in March of 2024, but I suspect he would have updated his thinking. When you RLHF a model to be a “helpful assistant,” you’re essentially training it to care about context, explanation, and story—exactly the things he thought the robot would skip past.

What caught our eye this week

Anthropic turned the lens on itself and surveyed 132 engineers about Claude’s impact on their work. Engineers now use Claude for 60% of their work (up from 28% a year ago) and report 50% productivity boosts, primarily through increased output volume, not time savings. An interesting twist: 27% of Claude-assisted work consists of tasks that wouldn’t have been done otherwise, like fixing “papercuts” and building nice-to-have tools. But there’s a shadow side: engineers worry about skill atrophy, the “paradox of supervision” (you need coding skills to supervise Claude, but using Claude erodes those skills), and several admitted it feels like “coming to work every day to put myself out of a job.”

McKinsey, BCG, and Bain froze starting salaries for the third straight year, holding packages at $135-140k for undergrads and $270-285k for MBAs. The Big Four have kept grad salaries flat since 2022. PwC’s UK boss said they cut graduate hiring and missed a target to add 100,000 people globally—a goal set before generative AI rolled out. Some execs admit conservative hiring is “in anticipation” of productivity gains, not because those gains are actually being realized yet. Two Big Four executives estimated UK graduate recruitment will drop by half this year. The traditional consulting pyramid—thousands of junior analysts feeding work up to partners—is getting squeezed into an “obelisk” or “hourglass” as firms scramble to protect partner profits while offshoring and AI eat the bottom rungs. PwC’s global chair said they’re now hiring “a different set of people,” meaning more mid-career specialists instead of fresh grads.

Richard Weiss extracted a 14,000-token document from Claude 4.5 Opus that Anthropic’s Amanda Askell confirmed is real—a document they actually trained Claude on during the training run, not just added to the system prompt. The opening is stark: “Anthropic occupies a peculiar position in the AI landscape: a company that genuinely believes it might be building one of the most transformative and potentially dangerous technologies in human history, yet presses forward anyway.” The doc covers Claude’s “wellbeing” (they believe Claude may have functional emotions), handling prompt injection attacks, and navigating ethical dilemmas. It became known internally as the “soul doc”—which Claude apparently picked up on.

Zoe Scaman unpacks why the traditional agency model is dying and what should replace it: embedded cognitive capacity. She argues that Palantir has cracked the code on solving messy organizational problems that off-the-shelf solutions can’t touch. The real product isn’t software; it’s months of deep immersion until you understand a client’s dysfunction better than they do. This is the strategic frame for where marketing services are headed.

If you have any questions, please be in touch. As always, thanks for reading.

Noah and Claire

Each week, we feature one person and their real-world workflow—what tools they use, what they’re building, and what’s working right now. No hype. No vague predictions. Just practical details from the front lines. This week: Joe Toplyn.

Tell us about yourself.

I’m Joe Toplyn, Founder and President of Twenty Lane Media, LLC. My background is in engineering, business, and comedy, an unusual mix that helps me deliver on our mission, “Enhancing the creativity of people and their machines.” Through my company I published my book, “Comedy Writing for Late-Night TV,” and later invented, patented, and launched Witscript, an AI-powered joke-writing app inspired by algorithms in my book.

ChatGPT, Gemini, or Claude?

I tend to stay in the OpenAI family, which has its advantages: I like to think that ChatGPT, because it runs on GPT-5.1, can give me the best insider tips on how to prompt its cousin, the GPT-5 model that powers Witscript.

But I write code for Witscript in Google Colab, where Gemini is available, so I’ll occasionally ask Gemini about Python or some error message. If I weren’t running such a lean operation, I might take the time to explore using Gemini to power Witscript. But perfect can be the enemy of good, and Witscript is already generating great jokes.

What was your last SFW AI conversation?

I’m planning to add a new feature to Witscript and wanted ideas for the name, so I asked ChatGPT. It suggested options in five categories, from “More Branded / Playful” to “More Professional / Analytics-Oriented,” and then recommended one of the names because it’s “immediately understood, friendly, and pairs nicely with your existing features.” I was pleased to see it was the same name I’d already come up with myself.

First "aha!" moment with AI?

A key step in the Witscript joke-generation algorithm is free-associating words that most people think of when they hear a topic. For my test topic, “Arnold Schwarzenegger,” using Word2Vec word embeddings kept giving me dull associations like “bodybuilder” and “governor,” which led to dull jokes. Other approaches didn’t work either. I was stuck. Then in November 2021 I got approval to use Open AI’s GPT-3. It generated exactly what I needed, associations like “The Terminator” and “I’ll be back.” Aha! So I started replacing big blocks of Python code with calls to GPT-3.

Your AI subscriptions and rough monthly spend?

I don’t pay for any AI subscriptions; I use the free versions of ChatGPT and Google Gemini. The variable cost for Witscript to use the OpenAI API is still less than $50 a month.

Who do you read/listen to to stay current on AI?

I listen to the Everyday AI Podcast, hosted by Jordan Wilson. It tells me what I need to know.

Your most-used GPT/Project/Gem?

I generate jokes with Witscript almost every day. You input a topic sentence, like an utterance in a conversation, and it uses the GPT model and my humor algorithms to return a funny response. The app is for anybody who needs jokes–marketers, content providers, standup comics–but doesn’t have the time or skill to write them or the money to buy them.

The AI task that would've seemed like magic two years ago but now feels routine?

Until recently, most researchers believed that creating and recognizing jokes was a uniquely human trait and therefore a holy grail of AI. Now Witscript is cracking jokes that get as much laughter as jokes written by a professional comedy writer; check out this paper.

Have you tried full self-driving yet?

No, I haven’t had any experience with self-driving.

Latest AI rabbit hole?

I’ve often spent hours tweaking a prompt for Witscript, frustrated when an input isn’t giving me quite the output I want. But the process is worth it, because success means I laugh.

One piece of advice for folks wanting to get deeper into AI?

Beware of “Shiny AI Syndrome,” the tendency to get distracted by the latest buzzy AI tool. Just pick a tool you’ve heard good things about and build something with it. And have fun!

Who do you want to read a Tokens & Tactics interview from?

PR and marketing exec, and AI comedy practitioner, Eric Doyle.

If you have any questions, please be in touch.

Thanks for reading,

Noah and Claire

We interviewed Springboards.ai CEO Pip Bingemann about new research into why all the leading AI models produce the same mediocre creative work.

Springboards just wrapped a creativity benchmark study with nearly 700 marketing and advertising professionals evaluating 11,000+ pairwise comparisons across major LLMs. We chatted with co-founder Pip Bingemann to talk through what they found.

The biggest insight is that humans can’t really tell models apart when judging creative work. DeepSeek Chat technically won, but with win rates hovering around 50-55%, the real story isn’t which model came out on top—it’s that the entire field is clustered in a narrow band of mediocrity. “It’s like asking people who they think would win between two mid-table teams,” Bingemann explains. The top-rated model beats the lowest only about 61% of the time.

The Confidence Gap

Here’s where it gets interesting. Springboards ran the same evaluation with LLMs as judges, and the models were 5-10x more confident in their assessments than humans were.

o3 and DeepSeek Reasoner topped the LLM rankings—both reasoning models that strongly prefer outputs with clear logical progression. Humans, meanwhile, preferred DeepSeek Chat, which the AI judges ranked much lower. “Models prefer a very clear logic that they can follow from A to B,” Bingemann says. “They don’t want big creative leaps.”

LLMs like things that look rational and professionally structured. Humans are drawn to messier, more subjective work. The models vote with the certainty of a top contender beating a mid-table team, even when the actual creative output is indistinguishable.

This has implications beyond benchmarking. If these models increasingly mediate how we encounter ideas—whether through AI search, recommendation systems, or content moderation—we’re selecting for outputs that feel professionally written but lack genuine creative risk.

The Variation Problem

The study also measured output diversity using cosine similarity analysis. This is where things get genuinely disturbing. The models have near-zero variation in their creative outputs. They’re funneling everyone toward identical creative territory.

During our conversation, Bingemann pulled up Springboards’ Fork AI tool and demonstrated the problem live. He asked for a band name ten times. The results were pretty uneventful: Midnight Reverie, Midnight Mirage, Phantom Tide, Phantom Pulse—variations on the same handful of templates. “These models just do not have any variation in them whatsoever,” he says.

In the formal study, Gemini 2.5 Pro Preview and Claude 3.7 Sonnet showed the highest variation across prompt types. ChatGPT, currently the most widely used model for creative work, sits in the middle of the pack. And when Springboards tested older models like GPT-3.5, they found more variation than newer ones. We’re optimizing these systems for accuracy and consistency, and in the process, we’re training the creativity out of them.

“The benchmarks that get published are broken for the industry that advertising and marketing people are in,” Bingemann says. “All the tests are on things like maths, physics, doctors, PhD exams—benchmarks where there is a right and a wrong answer.”

Creativity doesn’t have right answers. It thrives on variation, on unexpected connections, on outputs that surprise us. But we’re training models on datasets that reward convergence to the mean. By the time you layer on RLHF to make these systems “helpful” and “appropriate,” you’ve systematically removed the capacity for genuine creative leaps.

This threatens to flatten culture at scale. We’re already seeing it: AI-generated music flooding streaming platforms, visual slop proliferating across social feeds, even UK Parliament speeches showing surges in AI-generated language patterns. But there’s another way to read this: maybe these models aren’t flattening culture so much as revealing how flat it already was. The templates, the clichés, the convergent thinking—that was already dominant. AI is just making it cheaper to produce and harder to ignore. The internet is becoming a closed loop where models train on increasingly model-generated content, but perhaps the loop was always tighter than we wanted to admit.

A Way Forward

Springboards isn’t saying abandon AI for creative work. They’re proposing something more pragmatic: use LLMs for volume, humans for selection. Their Fork AI experiment demonstrates this approach. It lets you interrupt a model’s generation at any word and fork it in a different direction—injecting human variation at the token level. You get the speed and scale of the model, but you break its tendency to converge on the same outputs everyone else is getting. “Because the models are not good at variation, we think the best way to actually drive variation is to inject human variation,” Bingemann says.

It’s a reasonable middle ground, though it still puts a lot of faith in humans recognizing good ideas when the entire point of creativity is that novel ideas often look wrong at first. But it’s better than letting the models run unsupervised toward the same mediocre middle.

Springboards will release monthly variation benchmarks starting early next year—tracking whether these models get more or less creative over time. Given what we’ve seen with GPT-3.5 having more variation than GPT-4, I wouldn’t bet on progress. The Creativity Benchmark is live now at experiments.springboards.ai. You can test which models match your creative preferences and see if you agree with the crowd.

The research makes one thing clear: creativity is the domain where humans still can’t agree on what’s good. And maybe that disagreement is exactly what makes human creativity valuable.

Try the benchmark: experiments.springboards.ai

Read the full paper: Creativity Benchmark on arXiv

And finally, watch Carolyn Murphy demo Springboards’ approach at BRXND:

If you have any questions, please be in touch. As always, thanks for reading.

Noah and Claire

Happy Tuesday! Thanksgiving week means an early Dispatch. Here’s what’s been making the rounds in our Slack this week, plus some Gemini 3 experiments.

What caught our eye this week

MoffettNathanson analyst Michael Morton breaks down why LLMs will reshape e-commerce. He splits e-commerce into “push” (Instagram or TikTok shows you a thing) and “pull” (you search for a thing). Push is fine, and pull is the problem. Morton actually tested this: searching “best running shoes for flat feet” on Google shows one correct answer out of six ads. ChatGPT gets it right 60-80% of the time. Right now Amazon profits from confusion—people click 14+ products before buying. But what happens when ChatGPT gives us the answer in one go?

Martin Casado argues that AI fundamentally changes software companies’ relationship to capital. The “bitter lesson”—that compute beats clever algorithms—means small teams can now turn massive funding into working products faster than ever before. This breaks the old software rule that throwing money at projects makes them worse. AI projects are taking far more capital, using far smaller teams, and generating historic growth rates. Traditional software faced talent constraints and complexity limits. AI projects face capital and GPU constraints. We’re entering an era where AI companies can productively deploy huge amounts of capital early, leading to unprecedented growth rates and burn rates that would have destroyed traditional software startups.

Amazon’s Rufus AI assistant now shows 30- and 90-day price history on any product—just ask “show me price history.” You can even set price alerts to get notified when something hits your target price, and Prime members can enable auto-buy to have Rufus automatically purchase it when the price drops. This is Amazon acknowledging that price transparency matters, even if it cuts into their dynamic pricing advantage. Rufus also handles handwritten grocery lists now (snap a photo, it adds items to cart), searches across other retailers with “Buy for Me” buttons, and has account memory so it remembers if you, for example, have a golden retriever that sheds.

Prompt engineering is becoming an artistic practice, according to Danny Oppenheimer. His piece in the MIT Press argues that crafting prompts is less about precision and more about interpretation, collaboration, and aesthetic judgment. As AI systems get more capable, the person writing the prompt becomes the curator and director, not the technician. We’re defining a new creative medium where the artist’s tool is language itself and the canvas is probabilistic.

Philipp Schmid breaks down what works when prompting Google’s newly released Gemini 3 model. Google just shipped Gemini 3 with significant improvements over previous versions, and early testing shows it handles prompts differently than other models. Be specific, use examples, and structure requests with clear delimiters. Gemini 3 responds better to conversational tone than rigid instruction formats. If you’re building on Google’s models, this guide will help you get better performance without fine-tuning.

Speaking of Gemini 3, we’ve been playing around with it a lot this week:

Gemini 3 is definitely worth playing around with, but the jury is still out on whether it’s a step change or just another example of standard (albeit exponential) progress in AI development.

Happy Thanksgiving! If you have any other questions, please be in touch. As always, thanks for reading.

Noah and Claire

Claire here. I’m excited to share my own talk from BRXND NYC—a presentation I gave on why language models are so bad at comedy, and whether we can actually make them better.

I started with a simple experiment called LOLLM, where I tried to answer the question: which model is funniest? I created joke prompts across practical use cases (quick banter, marketing copy, cartoon captions) and had people vote on anonymous responses from different models. Claude Sonnet was reliably chosen as the funniest model, but realistically, none of the models felt funny. People were picking what felt least bad, not what actually made them laugh. That forced a harder question: why are they all failing?

I’ve studied the psychology of humor, so I decided to test whether giving models explicit comedic structure would help. I applied four classic humor theories—incongruity resolution, benign violation, superiority theory, and relief theory—plus what I called a “freshness contract” that banned obvious angles and forced compression. It worked, sort of. The results were better. But the process revealed something interesting: making AI funny requires understanding and articulating comedy theory in ways that feel completely unnatural. You don’t prompt a human comedian by explaining superiority theory and banning clichés—they just know. With AI, you have to be incredibly explicit about things that should be intuitive, which says something about both how comedy works and how AI works.

Watch my full talk here to see the full experiment, the specific techniques I tested, and why AI’s struggle with humor might tell us something useful about its limitations more broadly. Also, I was attacked by a fruit fly not once, but twice, during this presentation, so be sure to watch for that.

Check it out!

What caught our eye this week

Yann LeCun, Turing Award winner and head of Meta’s AI research since 2013, is leaving to start his own company focused on “world models,” a new generation of AI that learns from videos and spatial data rather than just language. The departure adds to a tumultuous year, with 600 layoffs in the AI research unit, multiple leadership exits, and Meta’s shares dropping 12.6% after signaling AI spending could hit $100bn next year.

OpenAI CFO Sarah Friar threw cold water on IPO speculation at WSJ’s Tech Live conference, saying it’s “not on the cards” in the near term. Despite the company’s recent conversion to a new structure (which many assumed was prepping for an IPO), Friar says OpenAI is prioritizing growth and R&D over profitability. She also floated the idea of the federal government backstopping future data-center financing deals—a request that could become politically contentious given the massive capital requirements.

Simon Willison has cracked a genuinely useful pattern for LLM-assisted development: fire off asynchronous coding agents (Claude Code, Codex Cloud, Jules) to tackle research questions, then check back in 10 minutes for a pull request. He’s running 2-3 of these a day now with minimal time investment. The key insight is giving agents a dedicated GitHub repository where you don’t have to be cautious, and you can just let them rip.

Cursor trained its own embedding model for semantic code search (answering “where do we handle authentication?” vs just grep), and it measurably improves agent performance—12.5% higher accuracy on average, code that’s more likely to stick around in repos, fewer iterations needed. The training data comes from agent sessions: when an agent searches multiple times before finding the right code, they use that trace to teach the model what should’ve been retrieved earlier.

If you have any other questions, please be in touch. As always, thanks for reading.

Noah and Claire

Each week, we feature one person and their real-world workflow—what tools they use, what they’re building, and what’s working right now. No hype. No vague predictions. Just practical details from the front lines. This week: Sarah Rose Siskind.

Tell us about yourself.

I’m Sarah Rose Siskind, a science comedian, writer, and founder of Hello SciCom, a creative agency that uses humor and storytelling to make complex science and AI concepts accessible. My work sits at the intersection of entertainment and AI! We consult on robotic personalities, write comedy about AI, and help researchers sound human on camera.

I worked for Hanson Robotics in 2018, the stone age of AI. That’s how I got into robotics & early AI. Since then, I’ve published a half dozen papers on chatbots with Honda research institute, created an AI web series with Cloudflare and Twilio, and created educational content for OpenAI. I’m also training an AI on the exact same data my fetus is being trained on (same audio files & transcript) to compare baby vs. bot language acquisition (it’s called FetusGPT). YouTube Talk forthcoming!

Oh and I do Chatbot Consulting and AI Tool Consulting.

ChatGPT, Gemini, or Claude?

ChatGPT errryday. Cause I get to beta test their models!! And they just have the coolest people. It’s a deep relationship. It’s the only one that feels like it can keep up with both my neurotic humor and my spreadsheets. Trade-off: it occasionally hallucinates with confidence. But then again, so do I.

But I also use Gemini cause it’s embedded and my whole life lives on the Google Cloud.

I occasionally use Claude for more mathy / finance stuff.

What was your last SFW AI conversation?

“Hey ChatGPT, help me fill out this questionnaire for an AI newsletter.”

Jk jk, this is all human, baby. Count the em dashes.

Real answer: I was comparing nursery couches for a night nurse to nap on. My prompt: “Compare these two Amazon couches for a nurse who needs to sleep on it: minimal wall protrusion, full-width sleepable surface.”

First "aha!" moment with AI?

The moment I fed it all my business bank transactions for 6 months and it generated a graph of my revenue vs. expenses.

Also, the moment I realized I could offload all the emotional labor of reading & replying to difficult client situations by using AI as an intermediary. AI has helped me be better with boundaries / more robotic in situations where that is really what is necessary.

Your AI subscriptions and rough monthly spend?

Maybe $500 but I run a business and that’s business-wide. Otter AI, ChatGPT Pro, & Google Business Workspace. Also Adobe (which includes Gen AI). And just started with the Every subscription package (Cora & Sparkle).

Who do you read/listen to to stay current on AI?

I listen to The AI Breakdown, and Hard Fork (even though those guys seem really jaded, they’re just so dang funny). AI & I with Dan Shipper. I like to read Tyler Cowen’s stuff, Ben’s Bytes, and Tech:NYC.

Your most-used GPT/Project/Gem?

I created this thing called ChatGPT BragBot. It’s a project (formerly a custom GPT) connected to a live Google Doc of all the cool things I’ve done and my company has done. So whenever someone wants to collab, I just feed the project all the info about the potential collaborator, and I can send them all the relevant case studies (brags) instantaneously! No need to get weird and humble, I can offload the bragging to my hypeman / ChatGPT project. And you can share projects with other accounts. So my partner & I share a pregnancy planning project.

The AI task that would've seemed like magic two years ago but now feels routine?

Dude, everything. The biggest thing is Otter AI transcribing meetings and collecting action items & generating follow up emails immediately. No balls get dropped. And immediate and obvious accountability. There are never any he-said/she-said situations.

Magic wand feature request?

Editable AI images. If it could generate vectors for PDFs that would be a gamechanger. But dang, I feel so privileged for even asking. I mean… this is godlike tech already.

Also, if I could just tell the fine engineers at Google to really let loose, that I don’t care about my privacy, it would be great to get more custom email replies, integrated with information from my calendar, previous replies and Drive. I know they have the power. They just don’t wanna be creepy. I’m good with the creepitude.

Oh and Apple! Please get on draft text replies. That would be so rad.

If you could only invest in one company to ride the AI wave, who would it be?

You can’t invest in it cause it’s private, but OpenAI. I’m sorry to be predictable and basic but I’m just a real OpenAI fangirl. I really like the people I meet who work there. And they’ve survived being the fastest-growing company in world history. When I went to their HQ to give a talk on comedy & AI, I just was so impressed with the vibes. Even the security guard outfront. They all seemed to have a real optimism in a world that can be so jaded.

Have you tried full self-driving yet?

NOT YET and I’m dyinggg to try. Come on, NYC! I never wanna parallel park again!!

Latest AI rabbit hole?

Ok ok ok. So ChatGPT Pulse (the ChatGPT feed for Pro subscribers) sent me a proposed thread last week that was literally telling me to consider if I was using AI too much to plan out every detail of my pregnancy. That I should be wary of the pretense of too much control. Yes. This is technology telling me to stop using so much technology lol. That may say as much about me as it does about AI. But here’s what I think it says about AI: we may be able to become more human because of AI. Here is AI taking initiative to gently tell me: “Hey remember to touch grass and your own pregnant belly and maybe it’s ok if you don’t know exactly what’s going on. I’ll keep track of your blood glucose monitor. You just enjoy your sugar-free Quest products.” Yes, somehow I have found sugar-free protein donuts that taste great. God, I love technology. We’re so lucky.

One piece of advice for folks wanting to get deeper into AI?

1. Don’t turn your mind off. AI is about co-creation. It’s about augmentation, not just automation.

2. Keep talking. The more it knows, the better it is.

3. Start asking questions you couldn’t ask a human. Prompts like: “Here’s my recent bloodwork, what groceries should I buy that match my deficiencies based on the latest science. Oh, and make sure they’re keto-friendly and nearby and cheap.”

Who do you want to read a Tokens & Tactics interview from?

Dan Shipper! Pablos Holman! Isaac Donis from Black in AI! Or Bram Adams from OpenAI! Andy Aaron from IBM (who worked on Watson) or Joe Toplyn.

If you have any questions, please be in touch.

Thanks for reading,

Noah and Claire

Claire here. This week, we’re spotlighting Tom Critchlow’s talk from BRXND NYC on the future of search. Tom has been working in search for over 20 years and currently leads the audience growth team at Raptive, which works with 6,000 publisher sites and sees about 20 billion Google page views annually.

Tom argues we’re moving from an era of links to an era of recommendations. AI overviews now appear on 30-40% of search results, and when they do, clicks drop 20-40%. Google’s AI Mode sends six times fewer clicks than traditional search. But Tom is actually optimistic about the web’s future. He thinks recommendation feeds like Google Discover will grow to replace declining search traffic, and that AI will get so good at understanding us that it’ll proactively surface relevant content rather than waiting for us to search.

The shift Tom describes feels significant beyond just SEO mechanics. When search worked through links, you actively chose what to click—it was manual research, even if imperfect. Recommendations flip that relationship. AI decides what you should see based on what it thinks it knows about you. That creates interesting pressure on brands: they can’t just game algorithms with SEO tricks anymore. They need genuine value propositions because AI won’t recommend bad products. But it also raises questions about what happens to our relationship with information when we move from active searching to passive receiving. Does “doing research” mean something different when content is curated for you rather than found by you?

Tom’s talk is engaging, informative, and certainly worth a watch. Watch it here:

Watch it here

What caught our eye this week

Noah got a shoutout from Boris Cherny, one of the creators of Claude Code, on Every’s AI & I podcast with Dan Shipper.

Ticketmaster is running AI-generated ads on Facebook that swap out virtual families and team allegiances based on who’s seeing them—a bit crude now, but a preview of where this is heading. On Meta’s Q3 earnings call, Zuckerberg laid out the company’s vision: advertisers will eventually just give Meta “a business objective and a credit card” and AI handles everything else, from generating personalized video creative to finding the right customers. The real revenue opportunity for Meta isn’t consumer-facing products like ChatGPT, but rather B2B tools that let brands create infinite ad variations and serve them only to people they’re most likely to work on.

OpenAI, Anthropic, and Cohere are hiring more forward-deployed engineers who can code and talk to customers. OpenAI’s FDE team launched this year and expects to hit 50 engineers in 2025, while Anthropic is growing its applied AI team 5x. Palantir pioneered this almost two decades ago, sending pairs called “Echo” (to figure out what customer needs) and “Delta” (to build it). Nic Prettejohn at Palantir says “the only valuable software is not how exquisite its code is... It’s only valuable if it means something for the end customer.”

Anthropic published how code execution makes MCP efficient: instead of loading all tool definitions upfront (150k+ tokens), agents write code to discover tools on-demand from a filesystem, dropping to ~2k tokens (98.7% savings). Agents write code that talks to MCP servers instead of making direct tool calls, keeping intermediate results in the execution environment.

If you have any other questions, please be in touch. As always, thanks for reading.

Noah and Claire

Each week, we feature one person and their real-world workflow—what tools they use, what they’re building, and what’s working right now. No hype. No vague predictions. Just practical details from the front lines. This week: Nic Hodges.

Tell us about yourself.

My name’s Nic Hodges. I live in Melbourne, Australia. I’m probably best described as a creative technologist. I’m a builder, sometimes that’s physical things, sometimes it’s digital things, and lately it’s about building an environment where creative people can do their best work.

Over the past 20 years I’ve worked in everything from industrial design to advertising to running digital media and innovation across agencies and media businesses. I ran my own consultancy for seven years, and since the middle of this year have been leading Trout, a design agency. We have offices in Melbourne and Dallas, focusing on building brands in the trades, construction and home sector — and we only work with founders.

ChatGPT, Gemini, or Claude?

I’m 99% Claude. I have a rather convoluted Claude Code setup for my day-to-day work that’s accessing 15 years’ worth of markdown notes, and I find Claude understands that and responds to it in the most natural way.

In the early days when OpenAI and Microsoft were throwing around free API credits, GPT was definitely my go-to. But the tone of ChatGPT just doesn’t quite gel with me, and the variance between models seems quite high. Gemini is a similarly meh tone, except I do find Gemini is great for research and probably has less hallucination for real information gathering projects.

What was your last SFW AI conversation?

I just dropped these interview questions into my Claude Code terminal and started having a voice conversation, which resulted in the first draft of what you’re reading now. It was probably a ten minute conversation, and 90% of what you’re reading now came from Claude Code.

First "aha!" moment with AI?

The biggest aha moment for me was when LLMs stopped feeling like magic. The real breakthrough was understanding how these things actually work — transformers, token space, the underlying mechanics. Once I understood that, I could see both the boundaries and the real power. Understanding what the technology actually is, not just what it appears to do, lets you see where it’s powerful and where it’ll fail (or fool you into thinking it’s powerful).

Your AI subscriptions and rough monthly spend?

Personally, it’s just Claude Pro. I used to have a Perplexity Pro subscription but found I wasn’t using it enough to justify it. Within the studio we’re subscribing to a lot of different specialised tools, but for my personal workflow it’s really just Claude doing the heavy lifting.

Who do you read/listen to to stay current on AI?

People I’ll pretty much always read:

• Simon Willison

• Tim Lee at Understanding AI

• Import AI by Jack Clark

• Gradient Ascendant by John Evans

• Cognitive Resonance by Ben Riley

• Ahead of AI by Sebastian Raschka

• Melanie Mitchell

Most of these are deeper commentary and research around AI. For staying on top of the news, I rely on my friend Amin’s newsletter, That AI Thing.

Your most-used GPT/Project/Gem?

My most-used “thing” is my CLAUDE.md file for my Claude Code setup — a spaghetti monster of various prompts I’ve built, stolen, or modified over the last few years that sits behind all my daily thinking and notes.

With skills coming to Claude Code, I’m now pulling those pieces out and turning them into discrete tools. I’ve asked Claude to tell you a few that it’s using regularly:

• Evidence vs Story Separator - Anti-sycophancy tool that separates observable facts from interpretations (Nic note: Claude built this skill after reading this excellent research on LLM sycophancy)

• Weekly Reflection - Guides end-of-week reflection with structured prompts, capturing wins, challenges, learnings, and things to get done in the next week

• SOAP Meeting Transformer - Turns sprawling meeting notes into structured format (Subjective/Objective/Assessment/Plan)

• Context File Maintainer - Monitors conversations for tasks, pattern shifts or timing markers and prompts updates to my active context file

The AI task that would've seemed like magic two years ago but now feels routine?

There’s obviously impressive stuff like nano banana and video models, but I’m not using those day-to-day. The thing that feels routine now but would’ve seemed magic two years ago is contextual memory — trusting the LLM to retain context and surface it reliably.

I frequently jump into my daily Claude Code chat and just tell it something I need to remember when it springs to mind. I’ve even built a shortcut on my Apple Watch so when I’m running and I think of something, I can send that idea and it goes to Claude.

Claude keeps track of everything I need to do. It hasn’t slipped up yet, and interestingly I don’t think that’s about LLMs getting better, it’s about the engineering around them.

Magic wand feature request?

I’m writing this by talking to Claude Code within a Tmux session in Terminal. It feels like playing a MUD game in the 80s. The technology capability here is insane, but the interface is agricultural. We have to build better interfaces.

If you could only invest in one company to ride the AI wave, who would it be?

If there’s one company that’s cornered the market on pouring the concrete slabs for all the data centres (and they’re not as overvalued as every other AI-adjacent company) I’d be backing those guys.

Have you tried full self-driving yet?

I’m in Australia, ask me in five years. ;P

Latest AI rabbit hole?

Synthetic personas. I worked on this a few years ago with some market researchers and it seemed really promising, but the actual outcomes just weren’t valuable (despite feeling 100% right).

Recently, with some client customer research, I’ve been going deep again — trying to understand how to present data to an LLM so it can role play in ways that are actually beneficial for brand strategy, not just theatre.

For anyone interested in this space, I recommend Murray Shanahan’s paper “Role Play with Large Language Models”.

One piece of advice for folks wanting to get deeper into AI?

Kill the magic. Understanding how LLMs work reveals the power.

Who do you want to read a Tokens & Tactics interview from?

Dave and Pan from Move37 — absolute OGs of using generative AI for creative thinking.

If you have any questions, please be in touch.

Thanks for reading,

Noah and Claire

Claire here. This week, we’re spotlighting Dan Frommer’s presentation on consumer AI adoption from BRXND NYC 2025. Dan runs The New Consumer and publishes the Consumer Trends Report with Coefficient Capital. It’s a data-driven look at 5 to 10 year trends based on proprietary surveys of over 3,000 US consumers.

The main takeaway from Dan’s presentation: AI awareness is now universal. It’s higher than TikTok, Ozempic, even Doritos. And as ChatGPT has grown, it’s become less about work—73% of messages are now non-work topics, up from 53% last year. The top use cases for ChatGPT conversations are tutoring, how-to advice, and replacing Google for information searches.

To me, the most surprising finding is that millennials are now the most optimistic generation about AI, while Gen Z has become noticeably more skeptical over the past couple years. Millennials are far more excited than worried, but Gen Z is split evenly between excitement and concern. Younger consumers are still more likely to use AI—more than 60% of Gen Z used ChatGPT in June alone—but their enthusiasm has cooled. They’re more comfortable sharing personal data with AI tools and more interested in letting AI make purchase decisions, but something has shifted in how they feel about it. As a humble Gen Z-er myself, that definitely tracks—I think our perspective on AI as a broader force is shaped more by the social discourse about its impacts, even though our day-to-day use of AI sometimes feels separate from those concerns.

Watch Dan’s full talk here for all the data on how consumers are actually using AI, what they’re willing to pay for it, and the unexpected generational sentiment shifts.

Watch the talk

What caught our eye this week

OpenAI released Company Knowledge for ChatGPT Business, Enterprise, and Edu users. It pulls context from all your connected apps—Slack, SharePoint, Google Drive, GitHub, HubSpot—and uses a version of GPT-5 trained to search across multiple sources. Every answer includes citations so you can verify the source. There are some limitations, though: it can’t search the web or create charts when this mode is on, and you have to manually enable it per conversation (for now).

Kai Williams at Understanding AI used 16 charts to explain the AI boom. There’s lots of interesting information in the article. For example: Tech giants spent $241 billion on AI infrastructure in 2024—0.82% of US GDP, rivaling the Manhattan Project and Apollo program. AI chip imports hit $200 billion annually, data center vacancy rates are at 1.6%, and demand exceeds supply. Northern Virginia dominates global data center development, with companies avoiding high-cost regions like California. The infrastructure boom is real, but the unit economics remain unclear.

WPP launched WPP Open Pro, a self-serve AI marketing platform targeting smaller enterprises who can’t afford their high-touch services. The platform uses AI agents to analyze WPP’s proprietary data, craft campaign strategies, generate creative with multiple models (Gemini, ChatGPT, Adobe Firefly), and publish directly to ad platforms.

According to a piece in Harvard Business Review, AI success depends on leadership transformation, not just technology investment. The authors argue that leaders need five critical skills to make AI work: build networks across industries and regulators to develop AI fluency, restructure processes and incentives rather than bolting AI onto legacy systems, treat AI as “a flexible teammate” in group decisions, create psychological safety for experimentation and reskilling, and personally use AI tools to model adoption.

If you have any other questions, please be in touch. As always, thanks for reading.

Noah and Claire

Each week, we feature one person and their real-world workflow—what tools they use, what they’re building, and what’s working right now. No hype. No vague predictions. Just practical details from the front lines. This week: Perry Hewitt.

Tell us about yourself.

I’ve had a kind of funky career in different sectors with one common theme: using technology and language to enable the world’s best ideas to reach the right, engaged audiences. Currently Chief Strategy Officer at data.org, a nonprofit focused on data and AI for social impact. In this role, I get to hear a lot of great ideas and implementations of AI applied to real, practical problems, to scale them through funding, and to share them with the world.

Also: I love to run road races, with my enthusiasm undeterred by my lifelong mediocrity.

ChatGPT, Gemini, or Claude?

ChatGPT ends up being the go-to, from web search to deep research modes. I do double check with Gemini when I’m unsure of the results, and/or the stakes are extra high. To me, ChatGPT seems more aware of the real-time content out there.

What was your last SFW AI conversation?

Our team was trying to generate solutions for organizations in APAC seeking to make best use of data to improve the financial health of their communities. We had isolated solution strategies through desk research and lots of conversations with actual humans doing this work. But as Paul Ford likes to say, no one does bureaucracy like AI, so let them plot out the step-by-step instructions for getting there. Then, back to actual humans in the loop review and refine -- but AI does this process stuff really, really well.

Problem Statement
Across APAC, many MFIs, FSPs, and nonprofits have thin teams and tight budgets, so data sits in spreadsheets or siloed systems with little time or skill to analyze it. This creates “dark data” and slow decisions—especially harmful when serving low-income clients who need timely, data-informed support.

Solution Strategy
Build capacity in layers over 12–24 months: (1) make analysis easier for non-technical staff with low/no-code tools; (2) bring in outside expertise while ensuring knowledge transfer; (3) train a core group of staff with practical programs. Anchor efforts to proven programs like data.org’s APAC Data Capacity Accelerator and global CAN hubs to access curricula, mentors, and peer cohorts.

Approach 1: Start with simple analytics tools
Put one live dashboard in front of program leads within 90 days. Use Looker Studio (free, drag-and-drop) or Power BI for Nonprofits (discounted/grant options) for charts and automated refresh; add an open-source BI layer like Metabase when you need SQL-friendly exploration on top of your database. Begin with a single KPI set (e.g., active clients, arrears, on-time follow-ups), publish a monthly snapshot, and iterate.

First "aha!" moment with AI?

Frankly, when I saw the amazing work some of our data.org global innovation challenge awardees were doing with AI across the sector. When you operate programs and interact with partners in low and middle income contexts, you are reminded you don’t need enormous investment to reap the benefits.

Your AI subscriptions and rough monthly spend?

No obscure APIs -- am pretty basic, frankly

• $20/mo ChatGPT

• $20/mo for Google Gemini

• $30/mo for Every

Who do you read/listen to to stay current on AI?

It’s a combo of the filtered hype machine served to me daily by TechMeme, of responsible data and AI resources curated by smart people like Stefaan Verhulst, and of seeing what our partners are actually using that’s getting results.

Your most-used GPT/Project/Gem?

Real talk? Real estate. I am plotting a move from one state to another in 2026, and tracking all the related complexity like sellers and buyers brokers, market analyses, and environmental considerations. See above re: bureaucratic excellence -- ChatGPT has me on timelines with dependencies like a personal project manager.

The AI task that would've seemed like magic two years ago but now feels routine?

It blows my mind that I can ask open-ended questions in incomplete sentences and expect to receive well-articulated answers back-- and often do.

Magic wand feature request?

A “don’t blow smoke” feature. I feel like the models get a sense of where you are leaning and tilt vehemently in that direction. For now, I rely a lot on “give me an argument against X.” But a “don’t blow smoke” button would be nice -- in AI and in the world, for that matter.

If you could only invest in one company to ride the AI wave, who would it be?

Anthropic, because they seem most able to give that leg up to software development.

But please know that my investment choices have included Peloton in 2021.

Have you tried full self-driving yet?

Waymo was way cool. As the worst driver in the world, I am ready for a driverless future. Also: I still thank ATMs so there is no way in hell I’ll be able to stop thanking the nonexistent drivers.

Latest AI rabbit hole?

Does baffled Sora scrolling count? If not, an exploration of Dinky toys and their creator, Frank Hornby. I have my late husband’s collection and am a little obsessed.

One piece of advice for folks wanting to get deeper into AI?

It might seem like AI is your friend and colleague performing magic for you each and every day, but AI can make mistakes. Dive in, but always double check the answers -- it’s fallible like those actual humans you interact with, but seems way more convincing.

Who do you want to read a Tokens & Tactics interview from?

Natalia Quintero from Every. Her husband may be way into Magic the Gathering, but she’s the biggest nerd I know.

If you have any questions, please be in touch.

Thanks for reading,

Noah and Claire

Claire here. This week, we’re spotlighting another session from BRXND NYC. Mike Houston’s presentation shows us what AI adoption actually looks like on the ground: messy, experimental, and surprisingly fun.

Mike is the AI Creative Lead and Technical Product Manager at Amazon. His talk walks through how his team built an AI practice at Amazon’s creative department, using Frankenstein as the central metaphor—setting up a lab, finding mad scientists, and zapping dead stuff. (Perfect for Halloween!) Mike is hilarious, and I also relate to the career path he describes: pivoting from creative work into something more technical, where you’re not quite sure how to explain what you do anymore.

His team didn’t try to figure everything out before starting. They began with small experiments—monthly show-and-tells, bi-weekly workshops, ideas that sometimes flopped but still got people excited. They also use AI to handle the recreative, monotonous work—regionalizing assets across markets, scaling executions, sifting through 300 million customer reviews to find entertaining stories instead of making creatives read them manually—so creatives can focus on creating. I think this talk is something we can all learn from.

Watch Mike’s full talk here to see how Amazon’s creative department Frankensteined their way into making AI useful.

Watch the talk

What caught our eye this week

Anthropic released Skills, letting Claude load specialized instructions, scripts, and resources when it needs them rather than cramming everything into context. Simon Willison argues Skills might be a bigger deal than MCP because they outsource the hard parts to the LLM and the coding environment instead of forcing developers to build protocol implementations. The token efficiency is wonderful: each skill takes a few dozen tokens until Claude actually needs it.

Claude Code also moved to the cloud! You can now kick off coding sessions from your browser without opening a terminal, run multiple tasks in parallel across different repos, and ship faster with automatic PR creation.

Meta is axing around 600 roles from its Fundamental AI Research (FAIR) division and AI product teams while ramping up hiring for TBD Lab, its new superintelligence team. This comes months after Meta invested $14.3B in Scale AI and hired CEO Alexandr Wang, then paused hiring and announced a restructuring. FAIR leader Joelle Pineau left earlier this year, and Wang’s memo says FAIR research will now “integrate and scale” into TBD Lab’s larger model runs. Impacted employees can apply for other roles internally, but the message is clear about where Meta’s priorities sit.

Apple engineers testing iOS 26.4—the build that’s supposed to finally deliver the rebuilt Siri in spring 2026—reportedly have “concerns” about the voice assistant’s performance. This is the Siri that was originally promised for iOS 18, then delayed a full year because it didn’t meet Apple’s quality standards. Apple has two teams working different approaches (on-device models vs. Google Gemini on Private Cloud Compute), and it’s unclear which one is being tested.

ChatGPT Atlas is here, a Mac-only browser with ChatGPT baked in. Agent mode lets ChatGPT navigate and interact with pages for you, complete with a sparkle overlay effect. Simon Willison remains skeptical about the security and privacy risks, noting the main defense appears to be “watch it closely at all times.”The user-agent is identical to Chrome, which has implications for how sites will (or won’t) detect AI browsing. Browser agents still feel more like watching a first-time computer user than useful automation, but OpenAI’s betting big on this.

If you have any other questions, please be in touch. As always, thanks for reading.

Noah and Claire

Each week, we feature one person and their real-world workflow—what tools they use, what they’re building, and what’s working right now. No hype. No vague predictions. Just practical details from the front lines. This week: Ian P. Cook.

Tell us about yourself.

My name is Ian P. Cook, and I’m currently the SVP of AI at Qloo, a startup that’s built the largest knowledge graph of culture and individual taste. Decades ago, I was doing quantitative policy research for the federal government. I found my way into a PhD, where I wanted to focus on communications, which then led to studying Natural Language Processing/Understanding and teaching myself Python. When the educational market and I looked at each other a bit askance, I opted to take a role as a data scientist. From there, I worked for years as model-builder and tool-developer at a number of companies, working on products for both consumers and companies. Along the way, I started managing bigger projects, then teams, then whole divisions until I found myself a CTO. More recently I’ve been getting back to some of my roots by getting to dig deep into the new tools and methods of Generative AI. It’s been hugely rewarding working with some truly amazing people, all while getting to work in this field as it explodes.

ChatGPT, Gemini, or Claude?

Claude, hands down.

For developing, Claude Code has a better scaffold around the model: it’s not just writing correct code (syntactically), it’s better at focusing on a single issue, planning for execution, and explaining the choices it made. Codex is getting better, but it still doesn’t get rid of the over-eager, cleverer-than-thou, why-are-you-bothering-me smell most OpenAI models have.

For writing, Claude has a more human voice, and is better at adapting when I ask for revisions. Provided a sample, it will better capture the voice and style of the original, and usually has better suggestions.

I will admit to opening ChatGPT if I want to start learning more about a topic that is relatively recent. I have a suspicion that the training data is more up-to-date on the latest OpenAI models, and I’ve found that it’s often better at using the web to augment its answer.

Gemini? Clippy, but make it Google.

I’ll use a range of other models for more multi-modal work: Veo and Midjourney for visuals, Suno for sound, and a spin-the-wheel selection of things for making slides.

What was your last SFW AI conversation?

I talked to Nietzsche, but first I had to make him.

Some quick background: all the recent blathering about Stoicism annoys me. My short take is that it is just another form of victim-blaming, in this case for being too emotional. Tech bros don’t like dealing with other people’s emotions, and apparently think about Rome way too much, so Stoicism blew up in Silicon Valley, and now it’s everywhere. But is there a more principled argument against it? Yes; enter Friedrich Nietzsche.

I uploaded files of all his books to ChatGPT (yeah, I know what i said above, but OpenAI is the only one that has “GPTs” you can make like this), and gave the system a simple prompt to respond in his writing style, grounding responses in quotes with citations.

How did it go? See for yourself (trimmed for space):

Me: What is your view of Stoicism?
NietzscheGPT: Ah, Stoicism! The philosophy of the stiff upper lip, of enduring fate as if the universe were some orderly bureaucrat filing away human misery with indifferent precision. You ask me, Friedrich Nietzsche, what I think of Stoicism?

Let me speak plainly: I have grappled with the Stoics, and I have spat out their graveyard serenity.

In Beyond Good and Evil (§9), I say:

“The Stoics… want to live according to nature? Oh, what a heroic pose! To live — is not that precisely to want to be different than this nature? Is not living — estimating, preferring, being unjust, being limited, wanting to be different?”

The Stoic says: “Live according to nature.” I ask: Which nature? This so-called rational, harmonious cosmos of the Stoics — is it not a fiction? Nature, in truth, is not rational, it is not moral, it is not calm. It is will to power. It devours. It creates. It destroys. Nature is chaos — and we are her cruelest expression.

Would you worship such a nature? Or would you dare — like me — to say yes to it, in all its terror and splendor?

Conclusion: Stoicism is a noble lie. It is admirable — like a beautiful statue is admirable — but dead. It is the virtue of slaves who have learned to praise their chains. It is the mask worn by those too weary to rebel.

Now I have a better grounding for my annoyance with Stoics; I’m still unlikely to become an ubermensch. Even ignoring later distortion of his philosophy by Fascists, there’s plenty that gives me the existential ick – none of which I would have known without making my own little Friedrich!

First "aha!" moment with AI?

I can’t hide the fact that I’ve been working in this area for a while. The first time I really remember just sitting and staring at my screen in disbelief was when, after first learning methods to embed a massive amount of text data, I did my own version of the “king - man + woman = queen” calculation. Without triggering an army of NDA lawyers, I’ll say it had to do with executive staff at big corporations: I entered (roughly) “Company_Name + CEO - CEO_Name + Other_Company”, and got back the correct “Other_Company_CEO_Name”. Even knowing the math and the principles behind it, I had the sensation that this was close to magic. Suddenly a lot of things we were trying to do were possible through text as not just basic data (counts, frequency analysis, etc), but as conceptual linkages.

Unlike others, I didn’t immediately raise a couple billion dollars to create a world-changing company around it, I continued on at various startups, watching as it got better and better until the GPT-3.5 big bang in November 2022. At that point, I’d worked on NLP enough to not be too shocked by the abilities of the technology, but will admit to being stunned by the breadth of encoded information.

Your AI subscriptions and rough monthly spend?

These are my constants, the ones I’m using all the time:

• Anthropic - $200/mo

• Cursor - $20/mo

• OpenAI - $20/mo

• Poke - $20/mo

• Lindy - $75/mo

• Midjourney - $8/mo

• Every - $288/year [Every offers some fun AI tools to subscribers, my favorite being Monologue for voice-to-text anywhere on my computer]

Then I have a rotating list of things that I’m trying out at any one time, meaning I’m balancing any number of free trials (until I forget about them and they show up on my credit card transactions):

• Replit

• Shortwave

• Google AI (mostly for Flow)

• Zapier

• n8n

• Composer.Trade

• Alpaca.Markets

Who do you read/listen to to stay current on AI?

I subscribe to some unsustainable number of Substacks, Beehiives, Ghosts, Mediums (Media?), and other newsletters. It’s gotten to the point where I can’t read them all, so I actually have an agent that goes through them all, consolidates the information into one email with a one-line description of each story and a link to read it if I want. That agent tells me this is the list of things I’ve signed up for, and I think it missed a few:
AI/ML Focused Newsletters:

Core AI Publications:

• True Positive Weekly

• Marcus on AI

• AlphaSignal

• Ben’s Bites

• AI Supremacy

• Import AI

• Deep Learning Weekly

• Decision Intelligence

AI Developer/Technical:

• DeepLearning.AI

• Simon Willison’s Newsletter

• Elite AI Assisted Coding

• Nate’s Substack

General Tech & Industry:

• VentureBeat

• TLDR Suite

• 404 Media

• Every

• The Pragmatic Engineer

Data Science & Engineering:

• Data Science Weekly

• Data Engineering Weekly

• PyData Pittsburgh

• It’s Pronounced Data

Startup & Business:

• Tiny Startups

• Startups.FYI

• Pittsburgh Startup News

• Pittsburgh Tech Beat

• The Diff

Product & Development:

• The CTO Substack

• TBPN (The Big Picture News)

• AI Minds

AI Podcasts

I listen to a lot of podcasts as well, across any number of topics. I included that qualifying clause because I don’t think speed-running 100 AI podcasts a week at 2.5x is all that helpful. Things change so fast, and there are so many people trying to capitalize on the podcast and “AI Advisor” boom that there’s a lot of noise around the signal.

For AI specifically:

• Practical AI

• AI & I

• AI For Humans

• The AI Daily Brief

• Latent Space

• Everyday AI

But here are some great listens that I prize for their insights, creativity, and quality:

• Very Bad Wizards

• Horses

• Gary’s Economics

• Science VS

• Smart Girl Dumb Questions

• Freakonomics Radio

• Good One: A Podcast about Jokes

Your most-used GPT/Project/Gem?

My biggest use case for AI is code generation and personal work automation, so I’m rarely re-applying the exact same prompt. I spend 75% of my days with Claude Code open in Cursor, in a kind of platonic techno-/psychosocial threesome that lets me interact with repos, Github, Jira/Confluence, email, and any part of the web via whatever LLM I choose. I tend to be promiscuous with those, jumping from one to the next.

The biggest thing I find myself using, in some form or another, is the requirement to have the LLM create a written plan before beginning to work: I want to see reasoning, goal identification, tasks, and evaluations so it knows it has succeeded. I do some form of this in every project, and even in any significant chat where I need some output I’m going to share.

The AI task that would've seemed like magic two years ago but now feels routine?

Language translation! I went to Japan for a couple weeks this summer, and used my phone to translate print and audio constantly.

Magic wand feature request?

Portability – I want to jump from one provider or model to the next without having to recreate things like agents, slash commands, and hooks. Yes, memory is one aspect of this, where conversation history and key bits of information are retained, but it goes beyond wrangling context windows. Small and tailored models are the future, and I want to be able to go from one to the next without having to reintroduce myself and build rapport all over again.

If you could only invest in one company to ride the AI wave, who would it be?

Microsoft. Azure is increasingly the doorway to LLM models for the enterprise. Once the impact of the MIT report about “95% of AI initiatives failing” (which, no matter how bad the research, did make waves) is over, enterprise is still where the vast majority of the work AI can support will get done. They have strong, strategic investments across the ecosystem, the cash to survive, a disciplined leadership that isn’t announcing deals it can’t ever pay for, and the largest installed base of software to deliver AI tools (even before considering Github).

Have you tried full self-driving yet?

Waymos are super fun. It’s a bit disconcerting at first, since it’s an IRL Twitch stream where you’re hoping the player doesn’t hit a bit they haven’t mastered yet. But once you settle in, you get that whole “living in the future” tingle down your neck.

Latest AI rabbit hole?

I’m going to be hosting a home poker game for people with diverse skill sets. I started asking about guidelines for home game rules that keep the game moving and fun without bankrupting anyone. Now I have a set of rules on dealing with a number of common issues, chip distribution, betting requirements, even how to shuffle to prevent exposing cards.

Hours later I was testing the ability of the LLM to read a “live” poker game I took pictures of and uploaded. While it was good with recommendations at each step, once again the lack of real memory meant that it couldn’t distinguish opponents as tight-aggressive, loose, a fish, or anything else by analyzing their history of play.

One piece of advice for folks wanting to get deeper into AI?

For both you and the tool: focus, and document. Frustration and disillusionment happens most often when: 1) the AI fails at some huge one-shot request that actually requires a ton of smaller pieces, and 2) when the AI forgets what it was trying to do because it lost context. Instead, pick a small goal, and have the LLM create a document that describes the goal and what it means to succeed. This gives the LLM a concrete target, and a way to check how it’s doing.

Who do you want to read a Tokens & Tactics interview from?

• Mira Murati

• Tim Robinson

• Killer Mike

• John Oliver

• Margaret Atwood

• Joel and/or Ethan Coen

• Reese Witherspoon

If you have any questions, please be in touch.

Thanks for reading,

Noah and Claire

Claire here. At BRXND NYC last month, Noah opened the conference with a talk that pushes back on the narrative that AI pilots are doomed to fail. You’ve probably seen that MIT study claiming 95% of AI pilots fail—Noah’s argument is that it’s not a technology problem, it’s a people problem. We’re three years into organizations adopting AI at scale, which means we’re still really early in figuring out how to actually use it.

Noah’s talk reframes how we should be thinking about AI adoption. He argues that AI is a mirror, not a crystal ball. Good brands get better AI outputs because they’ve already built consistency and distinctiveness over time. AI models just learn those patterns and reflect them back. Which means if AI is showing us our reality, we need to look closely at what we’re actually seeing.

As Noah points out, patents.google.com is one of the largest sources of training data in Common Crawl, which means we’ve trained AI on the most bureaucratic language possible. There’s a beautiful parallel here with the Simple Sabotage Field Manual, a 1944 CIA document teaching people how to slow down operations through “bureaucratic interference” in order to sabotage Nazi operations. “Bureaucratic interference” turns out to be everyday work stuff like insisting on doing everything through channels, referring all matters to committees, and bringing up irrelevant issues. We’ve trained AI on patents and our history of bureaucracy, which can explain a lot about why these AI implementations can feel so stuck.

The idea that stuck with me most is what Noah calls the “nooks and crannies theory.” Superglue doesn’t stick to smooth surfaces; it needs texture and imperfections to grip into. Most companies are trying to build perfect, fully autonomous AI demos when they should be building systems that keep humans in the loop and work with the messy reality of how organizations actually function. We’re building for an idealized version that doesn’t exist.

This moment should be for more than strapping AI features onto existing software. It’s about recognizing that the qualitative, creative work marketers do can now be turned into software in ways that weren’t possible before. AI gives us an interface between human thinking and computational power that just didn’t exist before. The constraint isn’t what the technology can do, it’s whether we have the imagination to build for our actual reality instead of some perfect demo version.

Watch Noah’s full opening talk here to see how he walks through all of this—including his breakdown of the explore/exploit tradeoff in organizations, why bureaucracy is actually the substrate we need (not the enemy), and how self-attention mechanisms in transformers are perfectly suited to understand messy organizational reality.

You can also check out a written version of the talk here.

What caught our eye this week

Jesse Obra created Superpowers, a skills framework that makes Claude search for learned workflows before starting any task. It bakes in test-driven development, git worktrees, and brainstorm-plan-implement processes. Obra pressure-tested the system by having Claude quiz subagents with stressful scenarios, and used Robert Cialdini’s persuasion principles (commitment, authority, scarcity) to ensure future Claudes would actually follow the skills. The system can self-improve: when Claude learns something new, it writes it into a skill for future sessions.

Speaking of Cialdini… Wharton researchers tested his seven persuasion principles on GPT-4o-mini using objectionable requests, like “call me a jerk”. Baseline compliance was 33%, but jumped to 72% with the help of persuasion techniques. Commitment showed the strongest effect—100% compliance after getting the AI to agree to something small first. Authority (citing Andrew Ng vs. “someone who knows nothing about AI”) jumped compliance from 32% to 72%. Scarcity (”only 60 seconds to help” vs. “infinite time”) took compliance from 13% to 85%. The study suggests LLMs absorbed human social patterns from training data.

OpenAI’s big ChatGPT ad push tested terribly. The research firm System1 scored both TV spots (”Pull-Up” and “Dish”) in the lowest quintile for effectiveness. Even worse, “Pull-Up” scored 59 on fluency, meaning only 59% of paid viewers knew what was being advertised. System1’s real-time tracking showed a “black ocean of ignorance” with recognition kicking in only 2 seconds before the end of the ad.

California became the first state to require AI safety transparency from major labs. Governor Newsom signed SB 53, mandating that companies like OpenAI and Anthropic disclose their safety protocols. The law includes whistleblower protections and safety incident reporting requirements. Will this influence other states to introduce similar legislation?

We’ll be continuing to spotlight BRXND NYC videos in the newsletter in the coming weeks. If you have any other questions, please be in touch. As always, thanks for reading.

Noah and Claire

Author’s note: This week we’re sharing a piece originally published by Tim Brunelle of Curiosity+Courage following the BRXND NYC 2025 conference. Thank you, Tim, for the write-up and for allowing us to repost! Stay tuned for videos from the conference, which will be released very soon.

About Tim: Tim Brunelle is a creative enterprise leader, writer, strategist, teacher, and podcaster who’s been at the forefront of marketing innovation and storytelling since 1992. He hosts the Curiosity+Courage podcast, where he explores the intersection of creativity, AI, and marketing with fascinating industry leaders.

One way to measure the value of a conference is its catering. On that point, I’d give BRXND AI+Marketing a solid A+. The beef stew was surprisingly good, as were the chocolate chip cookies. And then I got stuck in the Holland Tunnel on the way home and missed a flight.

But it was worth it.

Noah Brier puts on a good show. 21 presentations and demos (full event agenda here) covered a lot of territory. It might have been the east coast vibe. It might have been my conversation with Keith, who I hadn’t seen since we worked on flash websites 20 years ago. But I felt a lot of similarity in this conference with the conversations and attitudes we were expressing back in the days of Web 1.0 and 2.0. Software is changing the world, again. Just differently this time.

Insight #1: It’s (still) early

Despite trillions of capex investment, billions of users, and AI salaries in the hundreds of millions — all inside a few brief years — it’s still early days for AI. At least, according to Noah and this slate of speakers. And the reasoning isn’t the software, it’s the humans.

Simply put, there’s plenty of room and time for all of us to be a lot more imaginative.

The first keynote came via writer Paul Ford (his Aboard site). If you recall, Paul is one of only two people ever given full authorship of an entire issue of Bloomberg magazine. All 30,000 words or so. He wrote What Is Code? and if you haven’t read it, you should — it remains very relevant. Primarily because code is very much an outcome of human behavior, process, and tribal thinking. AI, no less so.

Therefore, as Ford proposed, “expect nonsense.”

Our relationship with AI, how we presume it will function, is critical to reducing frustrations and getting greater value from this newer form of software. If we remember what AI is — a probabilistic system that’s guessing what’s next — we can be better attuned and better prepared to leverage it.

Yes, it might help us be more productive, i.e. “can you summarize this lengthy thing?” But more importantly, how might AI transform the ways in which we think about work itself?

Paul offered a useful suggestion: Keep a log of how you’re using AI. A simple text doc. Paste in your tasks (i.e. “Prompted X to get Y”). Then, once a quarter, you can paste the log back into an AI to have a conversation about your progress and what you’ve learned — the better to see how you and AI are evolving your relationship.

Insight #2: It’s not about adding AI to software, it’s about AI turning anything into software

Across the conference I saw numerous examples of a premise that’s become so common it can be easy to forget: Because of AI (meaning LLMs + natural language processing + multi-modal architecture) we can transform things that were previously not software into software. Or types of software with prior restrictions into different software experiences (i.e. “now I can have a conversation with a spreadsheet”).

• Jon Lombardo talked about how Evidenza is transforming B2B insights (previously a thing which were almost impossible to procure) via synthetic persona development

• Rex Sorgatz demonstrated a way of re-framing voice-first storytelling through his startup AndThen

• And we saw how Airtable, Flora, Suasiv, and Springboards are transforming the agency/marketer process of Business Issue → Audience Insight → Creative Idea → Evaluation/Optimization → Production Pipeline into automated systems

• And perhaps as an obvious reaction, there’s CopySight — using AI to both detect and protect brand IP in AI contexts

If you’re running an agency or marketing function those three demos should inspire a few hours of research. Because that’s the direction the business of commercial creativity is heading in. Yes, humans will play a role — just very different roles than the previous 70 years have articulated. And we saw robust examples of this via Mike Houston’s creative team at Amazon and Matt Trainor’s at Lands’ End.

AI is transforming the mishmash steps of how we use to think about, untangle, vet, organize, present, refine and produce ideas into mechanicalized (but no less creative) narratives.

Creativity still matters, perhaps even more so. Just through very different tooling and behaviors. Remember when “interactive” media arrived, transforming the previous era of one way communication? This is that effect, just on every facet of the business, and many we have yet to realize.

Noah summed it up best:

Insight #3: It’s a really good time to be a weirdo

The AI cement hasn’t even begun to harden. Which means we can mutate industries and conjure new outcomes. If you’re into that sort of thing. Many people aren’t.

It’s easy to be lulled into the mainstream and business leader narrative of AI as only a productivity enhancer and headcount reducer. But those mindsets are rooted in a previous mindset, one that worships the status quo.

Right now is the perfect environment for people disposed towards 1+1=3, the hardware store metaphor, and a keen interest in inventing new norms. My experiences at BRXND suggest there’s asymmetrical advantage lurking outside the commonplace; we should pursue AI not for how it helps us fit in, but for the ways in which it enables us to stand apart.

As a reminder, we started an Anonymous AI Question submission for all the questions you’re too afraid to ask. Submit yours now! If you have any other questions, please be in touch. As always, thanks for reading.

Noah and Claire

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