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  • Jeremy Giffon on the Billion Dollar PDF, Peak Guy, and How Attention Became the New Capital

    In his second appearance on Invest Like the Best, investor Jeremy Giffon sits down with Patrick O’Shaughnessy for a wide-ranging conversation about how power, status, capital, and attention are being redrawn in real time. The organizing idea is the “billion dollar PDF,” the notion that a single well-timed document or post can crystallize a narrative and pull billions of dollars of capital toward it. From there the two range across the mechanics of the X timeline as market infrastructure, the decline of the billionaire class, the rise of the “poaster,” the economics of software in the age of compute, and what the next era of finance looks like when its founding act is seed investing rather than the leveraged buyout.

    TLDW

    Giffon argues that in private markets the real great filter for funds is storytelling, because the actual product (realized cash returns) takes a decade, so narrative is what you sell in the meantime. He and O’Shaughnessy unpack the “billion dollar PDF,” the way X functions as a single global newspaper (the uni-feed) that prices securities, dictates policy, and builds businesses, and how power laws now mean breaking containment on the timeline is worth more than steady performance. They discuss “peak guy” and the exhaustion of billionaire worship, the idea that the poaster has become the new priestly class, net worth as a surprisingly modern invention, and attention as the genuinely scarce asset. The back half turns practical: why AI job fears meet Giffon’s view that most white collar work is invented, why software is shifting from selling zero-marginal-cost strings to selling compute with thin margins and huge scale, why beating the market is easier for amateurs than professionals, how to underwrite emerging managers by studying the person, the feudal economics of SPVs and allocations, simplicity over complexity in investing, hiring through divisive job descriptions, and the hidden philosophers (from effective altruism to Curtis Yarvin and Nick Land) shaping Silicon Valley. Topics span venture capital, private equity, cap tables, SaaS, the Mag 7, Buffett and Bogle, East Coast versus West Coast finance, and the search for vocation.

    Thoughts

    The strongest thread in this conversation is that scarcity has moved. For most of the modern era, money was the scarce thing and attention was the byproduct of having it. Giffon flips that. Capital is now abundant, inflationary, and desperate for somewhere to go, which is why he can describe businesses and asset categories as “sponges” that get created downstream of capital rather than the other way around. What is actually scarce is a fixed slice of human attention, and whoever can command it (the “billion dollar PDF,” the breakout post, the person every billionaire wants to sit next to at dinner) captures the resource that money is now chasing. That reframing explains a lot of otherwise strange behavior, including why founders who already have wealth turn to posting, podcasting, and fame. They are not being vain. They are hedging out of a depreciating asset into the one that still appreciates.

    The most uncomfortable and clarifying claim is that narrative is not a distortion of markets, it is the market. Giffon walks through how the algorithm, driven by AI, selects which stories get shown, those stories set the consensus among the small group of posters who move capital, and securities get priced off that consensus. If you take that seriously, the efficient market hypothesis looks quaint. The marginal price of a security is being set, in part, by what an entertainment-optimizing model decided to surface to a few hundred thousand influential readers that morning. His line that “every other day someone writes some pornographic fanfic about AI and it moves the public markets” is a joke that is also a fairly precise description of 2026 price discovery.

    His software thesis deserves more attention than the culture commentary that will get clipped. The old SaaS miracle was selling copies of a string at near-zero marginal cost, which mechanically produced high gross margins. Giffon’s point is that the AI era sells compute, and you cannot write the prompt once and resell the output, so the marginal cost is no longer zero. The consequence is a structural regime change: lower gross margins, thinner net margins, and returns that accrue overwhelmingly to scale. He calls it a Walmart effect in software, and if he is right, a lot of the current sell-off in SaaS names is punishing the business model rather than the businesses, which is exactly the kind of nuance-free repricing he says markets specialize in.

    The optimistic surprise is his stance on AI and jobs, which cuts against the doom consensus without being naive about the short term. He concedes the near and medium term could be genuinely bad, but he refuses the “we will run out of jobs” framing because he thinks most white collar work is already invented to absorb our attention and capital, not to meet basic needs. Work-from-home Fridays, in his telling, are a quiet admission that many people have two or three hours of real work a day. If that is true, then automating the invented work is liberation rather than catastrophe, provided the transition does not crush people in the process. It is a bracing counterweight to the standard displacement panic, and it pairs well with his more personal note that the antidote to a priestly-class culture of looking outward for permission is the duty to steward your own gifts.

    The one place to push back is the tidiness of the “poaster as new priest” story. Giffon is careful to say he is describing, not endorsing, but the argument that status simply passes from scientists to billionaires to posters is cleaner than reality usually allows. Attention is scarce, yes, but it is also fickle and lotteryified in his own telling, which makes it a shaky foundation for a durable priestly class. Still, the underlying observation is sharp: when money becomes a “state of mind” label rather than a hard number, and when net worth itself is revealed as a recent invention (his Pride and Prejudice aside about Mr. Darcy’s income being cash flow, not a valuation, is the best illustration in the episode), the leaderboard everyone is actually competing on is real estate in other people’s minds.

    Key Takeaways

    • The great filter for private-market funds is storytelling ability, because the real product (realized cash returns) takes a decade, so narrative is what a fund actually sells in the interim through updates, events, and LP conversations.
    • The same business can be “cold” at seven years and $8 million in revenue but “hot” if you reset the clock and retell the story, so being flexible on narrative is itself a fix for a funding problem.
    • Insider bridge rounds are often surprisingly hostile (3x liquidation preferences, warrants, ratchets), and being extractive to the downside gets you booed while being extractive to the upside (pro rata rights) gets celebrated, even though both are similarly extractive.
    • In highly volatile times, optionality beats commitment: raise less, raise from investors with a wide mandate, and keep the ability to pivot the business model, run profitably, acquire, or even fire customers.
    • The “billion dollar PDF” is the idea that someone crystallizes a notion at the right time and it becomes the foundational viewpoint of an era, and capital follows it around like ten-year-olds chasing a soccer ball.
    • X is the “uni-feed”: everyone is served the same roughly 500 tweets a day across hundreds of millions of users, making it the global newspaper and a source of truth for capital markets, politics, and technology.
    • Institutions now survive only if they are “timeline native,” meaning reactive to and reflexive with the timeline, which describes the White House, venture capital, and public equities alike.
    • Posting has been lotteryified: a brand-new account can write one good post and get shown to hundreds of millions, so posting is described as the last great meritocracy.
    • Power laws have sharpened. Variance used to be low, but now breaking “containment” on the timeline means briefly taking over the world’s brain, and those few breakout events dwarf everything else combined.
    • Podcasts still underrate serving the algorithm; the video is recorded first for an LLM to review and decide whether to show, and only then do humans judge it.
    • A great post blends comedy, poetry, and writing, and great posters tend to be a bit tortured, closer to writers mixed with comedians.
    • “Peak guy”: society keeps searching for a priestly class, moved from scientists to the billionaire class, and Giffon thinks it has now moved to the poaster class, with billionaires increasingly deferential to posters.
    • Billionaire worship is exhausted partly because billionaires are far less scarce (state-of-mind billionaires have grown maybe 100x in 20 years) and money is less powerful than assumed, as the donor class has underperformed politically.
    • Net worth is a very new idea. In Pride and Prejudice, Mr. Darcy’s wealth is his estate’s annual cash flow, not a valuation, because no one would DCF or margin-loan an estate they would never sell.
    • “Billionaire,” like “millionaire” before it, is becoming a loose political and class label only tangentially related to actual liquid, inflation-adjusted wealth.
    • The most honest way to consume media is to admit it is entertainment, produced, selected, and edited to entertain, not to learn, no matter how productive it feels.
    • Going months off the timeline taught Giffon that you do not really miss anything; the filtered, secondhand version from smart people at dinner may be the most enlightened way to consume it.
    • On AI and jobs, the short to medium term could be bad, but the long-run worry is overblown because most white collar jobs are “made up” and not contingent on shelter, food, or medicine.
    • Work-from-home enthusiasm is evidence that many people have only two or three hours of real work a day, so work-from-home Fridays are a soft launch of the four day work week.
    • We have a moral duty to steward our gifts; the thing you spend most of your time on should spark and utilize your genius, and having fun at your job is a strong signal you have combined the two.
    • The largest finance firms (KKR, Blackstone, Apollo) were founded in a leveraged-buyout culture that is debt-driven and extractive; the next era’s giants may be founded on seed investing, which is equity-driven, optimistic, and qualitative.
    • West Coast venture is “eating” the East Coast: it created the biggest businesses in the world and functions as a civilizational technology, giving young people speculative capital with little downside.
    • Compensation has flipped: Silicon Valley now pays large liquid cash via mature secondary markets and yearly tenders, while Wall Street increasingly pays in RSUs tied to long-term firm value.
    • SaaS is just a business model, and while it is in trouble, that is often not what actually matters to a business being sold off out of fear.
    • Software is moving from selling near-zero-marginal-cost strings to selling compute, which means lower gross margins, razor-thin net margins, and returns accruing to scale, a Walmart effect in software.
    • Capital gets “blocked” when there are not enough great companies to absorb it, so high-capex AI and hardware categories arose in part as sponges for capital with nowhere else to go.
    • Markets lack nuance: the 52-week variance on the biggest companies is nearly 100%, so they are not priced well, and much private-market pricing reflects fund incentive structures rather than business quality.
    • Beating the market is easier for amateurs than professionals. Buffett’s S&P advice is for the average person, while pros are constrained by mandates, customers, and career risk (the Peter Lynch point).
    • A small principal writing a 500k check is the wrong customer for a large growth fund built to serve sovereigns and endowments; emerging managers, tightly aligned to returns, are underrated for that check.
    • Underwrite the person, not just the thesis. A manager’s personal financial situation matters enormously, and whether they are “looking up” or “looking down” at the fund size changes how they behave.
    • Modern finance is recreating a feudal system where lab founders (Elon, Zuckerberg, Dario, Sam) grant allocations like landed estates, and holders charge fees on this synthetic, purely relational, sometimes perpetual product.
    • The most generative activity is conversation, downstream of relationships, and being tolerant of weird, unpredictable people is a media diet advantage; chatbots can feel generative without actually being so.
    • Investors overvalue complexity to look clever; you should either do something so complex no one else will, or keep it simple (be long Elon, buy big companies at their 200-week moving average), and the real gift is selling the simple idea.
    • Richard Rainwater’s test: pitch your thesis on one page and state what percentage of your net worth you will put in, then yes or no. It is hard precisely because it forces clarity and conviction.
    • A job description is a sales pitch and an interview baked into a post; divisive, ambiguous statements (like “an ideological minority at a top 10 school”) self-select the right people and disqualify the wrong ones.
    • Silicon Valley’s hidden philosophy is underrated: a neo-Buddhist utilitarianism feeds effective altruism, and thinkers like Nick Land, Curtis Yarvin, and William MacAskill shape the culture without being named.
    • Where 1980s Wall Street was pagan, hedonistic, and nakedly about money, today’s tech views itself as self-righteous and positive-sum, treating the business itself as the ultimate philanthropy, with no felt need to launder gains through art or culture.

    Detailed Summary

    The Billion Dollar PDF and Narrative-Driven Capital

    Giffon opens with what he has learned in his first 18 months running his own fund: in long-term private markets, the great filter is storytelling. Because a fund’s real product is realized cash returns that take a decade to arrive, what a manager sells in the meantime, through quarterly updates, events, and one-on-one LP conversations, is narrative. He describes situations where an older company that has recently inflected struggles to raise simply because its story (seven years old, $8 million in revenue) reads worse than the same numbers reframed as a two-year-old rocketship. The billion dollar PDF is the escalation of this: a single document or post that crystallizes the notion of an era, does not even have to be right, and pulls billions in capital toward it. Capital, he says, behaves like ten-year-olds playing soccer, all chasing the same ball.

    The Uni-Feed: X as Global Newspaper and Market Infrastructure

    The technological catalyst, in Giffon’s view, is the uni-feed. Everyone on X is served the same roughly 500 tweets a day, and the poster-to-lurker ratio is enormous, so people who do not post cannot feel the impact. X is the Lindy social network, unlikely to reach the scale of the others but filling a vital role as a global newspaper and near-source of truth. The most important people in capital markets, politics, entrepreneurship, and technology read it every morning, and it forms opinion, prices securities, and writes policy. Institutions survive only if they are timeline native, both reactive to the timeline and reflexive with it. Crucially, this is also where narratives get set, and the winning story is not a well-considered book but the most entertaining, novel, somewhat-correct thing, because people are on the timeline to be entertained and the algorithm selects for exactly that.

    Power Laws, Breaking Containment, and the LLM as First Filter

    O’Shaughnessy observes that variance used to be low, with the best performers only modestly ahead of the worst, and that this has changed completely. Now there is a threshold where breaching containment feels like taking over the world’s brain for a short window, and those handful of breakout events matter more than all the rest combined. Giffon attributes this to technology rather than any change in content or audience: RSS gave you a normal distribution, algorithms give you a power law. He notes that podcasts remain naive about serving the algorithm, unlike streamers and YouTubers, and delivers one of the episode’s sharpest structural points: the video is recorded first for an LLM to review and decide whether to show it, and only after that first, largely invisible filter do humans get to judge.

    Peak Guy: Billionaires, Priests, and the Poaster Class

    The “peak guy” segment is the episode’s philosophical core. Giffon traces how God moved from being in and around everything, to a guy above the clouds, to something conceptual and distant, leaving an ongoing search for priests. Society tried scientists, but the scientific project stalled and physics has not delivered meaning since the war, so status passed to a billionaire class treated as the new priesthood: successful at business, therefore smart and hardworking, therefore worth listening to on physics, theology, or health. That worship has now saturated. Billionaires are far less scarce, money looks less powerful (the donor class has underperformed politically), and a billionaire who posts the wrong thing has to resign where Andrew Carnegie could once take up arms. Giffon’s claim is that the priesthood has passed again, this time to the poaster, and you can see it in how the billionaire class defers to posters (his anecdote: billionaire investors fighting to sit next to Tyler Cowen because he was the most interesting person in the room).

    Net Worth as a Modern Invention and Attention as the New Scarcity

    Giffon frames net worth itself as a strikingly recent concept. In Pride and Prejudice, Mr. Darcy’s wealth is discussed as roughly 10,000 a year in cash flow from his estate, not as a valuation, because no one would sell the estate or borrow against it. Wealth as a mark-to-market number is new, and between illiquid private markets, net worth as a concept, and inflation, “billionaire” is becoming a loose label, much like “millionaire” already did. Since time is fixed, the new scarcity is attention you can draw on the screen, which is why founders who accrue wealth so predictably turn to posting, podcasts, and channels: partly to convert wealth into fame, partly because they sense money is depreciating and attention is what is actually scarce.

    Opting Out and Media as Entertainment

    Asked about going months off the timeline, Giffon’s takeaway is that you should not fool yourself that you are seeking anything other than entertainment. All of it is produced, selected, and edited to entertain, and just as Rolex or Nike can convince you a liability is an asset, posts and essays can convince you that consumption is productive. The question is simply how much you want to be entertained. He does not see the death of books as a crisis so much as a swan song for a technology that was the best way to deliver information until better, more compelling ways arrived, though he is careful to note the negative language we use (brain rot, terminally online) betrays a deeper sense that something is off. New media is less forgiving: better than ever for the disciplined, worse than ever for everyone else. His friend Jesse refuses all algorithms and simply lets people tell him what happened, which Giffon half-endorses as the most enlightened, filtered way to consume the radiation secondhand.

    AI, Fake Jobs, and Stewarding Your Gifts

    On AI and white collar displacement, Giffon concedes the short to medium term could be bad (he agrees with a friend who worries about kids in college but not the ten-year-old), but rejects the “peak jobs” panic. Anything that can be automated should be, and the prospect of never having to sit at a computer again strikes him as liberating. Most white collar jobs, he argues, are invented, not contingent on shelter, food, or medicine, and our economy runs on unquenchable desire, so we will simply invent new things to do. Work-from-home attachment is his evidence that many people have only a couple of hours of real work a day, making work-from-home Fridays a soft launch of the four day week. This connects to a more personal theme O’Shaughnessy draws out: the duty to steward your gifts. Waste is aesthetically bad, wasting your gifts is among the worst kinds, and the surest sign you have integrated your work with your genius is that you are having fun.

    The Next Era of Finance and the New Economics of Software

    Giffon notes that today’s largest firms (KKR, Blackstone, Apollo) were founded in a leveraged-buyout culture that is debt-driven, extractive, and financially engineered, and wonders what the next 30 years look like when the founding act of the biggest firms is instead seed investing: equity-driven, optimistic, power-law, and qualitative. He sees East and West Coast finance merging, with the West “eating” the East, and a compensation flip in which the Valley now pays large liquid cash through secondary markets while Wall Street pays RSUs. On software, his central economic argument is that SaaS sold copies of a string at near-zero marginal cost, which is why high gross margins were the norm. The new era sells compute, where you cannot write the prompt once and resell the output, so margins compress and returns accrue to scale, a Walmart effect. He also reframes the high-capex AI buildout as capital markets manufacturing somewhere for blocked capital to flow, with companies created downstream of capital rather than the reverse.

    Beating the Market, Emerging Managers, and the Feudal SPV System

    Giffon argues the myth that you cannot beat the market is overstated: Buffett’s S&P advice is aimed at the average person, and it is professionals, burdened by mandates and career risk, who struggle most, while amateurs who simply held Bitcoin, Tesla, or Apple outperformed. For LPs, he stresses knowing what customer you are. A 500k check is the wrong fit for a growth fund built to serve sovereigns, and emerging managers, tightly aligned to returns, are underrated. He urges underwriting the person over the thesis, paying special attention to a manager’s own financial situation and whether they are looking up or down at the fund size. He then describes the feudal economics of the labs, where founders grant allocations like landed estates, holders charge fees on a synthetic, relational, sometimes perpetual product, and the most egregious setups feature no GP commit, a 10% upfront fee, and carry with no term limit.

    Simplicity, Hiring, and Silicon Valley’s Hidden Philosophy

    On process, Giffon warns that investors prize complexity to look clever, when the choice is really to do something so complex no one else will or to keep it genuinely simple (be long Elon, buy big companies at their 200-week moving average), with the real gift being the ability to sell the simple idea. He praises Richard Rainwater’s one-page-thesis-plus-percentage-of-net-worth test as a brutal clarity forcing function. On hiring, he treats the job description as a sales pitch and a baked-in interview, using divisive, ambiguous statements like “an ideological minority at a top 10 school” to self-select the right people and repel the wrong ones. Finally, he makes the case that Silicon Valley’s underlying philosophy is badly underrated: a neo-Buddhist utilitarianism that flows into effective altruism, with thinkers like Nick Land, Curtis Yarvin, and William MacAskill shaping the culture unnamed. Where 1980s Wall Street was pagan and nakedly about money, today’s tech sees itself as self-righteous and positive-sum, treating the business as the ultimate philanthropy, with none of the old reflex to launder gains through art or culture.

    Notable Quotes

    “Every once in a while someone basically crystallizes a notion right at the right time in the right way that sort of becomes the foundational viewpoint or opinion on a certain era.”

    Jeremy Giffon, defining the billion dollar PDF

    “The capital just follows the billion dollar PDF around the field.”

    Jeremy Giffon, comparing capital to ten-year-olds chasing a soccer ball

    “Everyone gets served the same 500 tweets per day and it’s hundreds of millions of daily active users.”

    Jeremy Giffon, on the uni-feed that makes X the global newspaper

    “Posting changes your life if you’re good at it. That’s still true today, maybe more true than ever.”

    Jeremy Giffon, on posting as the last great meritocracy

    “Andrew Carnegie could take up arms against his workers, but now if you post the wrong thing as a billionaire, you have to resign.”

    Jeremy Giffon, on the shrinking power of the billionaire class

    “It’s this holy conceptual, just points on a leaderboard, truly, because you can’t spend it.”

    Jeremy Giffon, on net worth as a modern invention

    “One should not fool themselves that they are looking for anything other than entertainment in all the media that they consume, because it is produced to be entertaining.”

    Jeremy Giffon, on opting out of the timeline

    “We’re in an era where we’re selling compute. You can’t write the prompt once and then sell copies of the output. You have to do the compute every single time.”

    Jeremy Giffon, on the new economics of software

    “The most important media property won’t be watched. The most important author isn’t read. The most important philosopher is not understood. The most important stock has no fundamentals.”

    Jeremy Giffon, on a world where reputation floats free of the thing itself

    Watch the full conversation with Jeremy Giffon and Patrick O’Shaughnessy here on Invest Like the Best.

    Related Reading

  • How the X For You Algorithm Works (May 15, 2026 Source Code Walkthrough)

    Diagram of the X For You algorithm pipeline showing content signals and user signals flowing through embed, attend, score, rank, and filter stages into a curated feed

    On May 15, 2026, xAI shipped a major update to the open-source release of the X “For You” recommendation algorithm. The repository now includes a runnable end-to-end inference pipeline, a pre-trained mini Phoenix transformer, a brand-new content-understanding service called Grox, and ad-blending logic. This is the most transparent look at how a major social feed actually picks your posts that has ever been published.

    This is the practical, plain-english guide. We read the source. Here is exactly how a post travels from your fingertips to someone’s For You tab, and what you can do to be the post that wins.

    The whole strategy in one sentence

    Write posts people reply to, repost, DM to a friend, linger on, and follow you for. Avoid anything that earns a mute, block, report, or spam flag. Space your posts hours apart. That is the algorithm.

    TL;DR

    • The For You feed is no longer a stack of heuristics. It is a single transformer-based machine learning system that predicts the probability you will like, reply, repost, share, dwell on, or hide a given post.
    • Posts come from two pools: Thunder (people you follow) and Phoenix Retrieval (the rest of X, found by similarity search).
    • A model called Phoenix scores every candidate against your engagement history. The final score is a weighted sum of nineteen predicted actions, with negative weights for “block”, “mute”, and “report.”
    • Almost no hand-engineered features survive. Freshness, verification badges, follower counts, and post type are not directly boosted. They are signals the transformer learns to use from your behaviour.
    • Out-of-network content is penalised by a tunable factor, so in-network posts have an edge by default.
    • A separate service called Grox continuously classifies posts for spam, policy violations, and brand safety. Flagged content gets filtered or de-amplified before it reaches scoring.
    • The best optimisations are still the boring ones: write posts that earn long dwell time, replies, reposts, and follows, and avoid anything that triggers mutes or reports.

    What changed on May 15, 2026

    The January 2026 release gave us the architecture but not a working system. The May update is the one that matters:

    • phoenix/run_pipeline.py replaces the separate retrieval and ranking scripts with a single inference entry point that mirrors production.
    • A pre-trained mini Phoenix model (256-dim embeddings, 4 attention heads, 2 transformer layers) is bundled as a roughly 3 GB Git LFS archive. You can run inference without training.
    • The Grox content-understanding service is now public. It runs classifiers and embedders for spam detection, post categorisation, and policy enforcement.
    • Ads blending is now in the open. So is brand-safety tracking.
    • New query hydrators mean the model sees your followed topics, starter packs, served history, impression bloom filters, IP, and mutual-follow graph at request time.
    • New candidate hydrators add engagement counts, language codes, media detection, quote post expansion, and mutual follow scores.
    • New candidate sources for ads, who-to-follow, Phoenix Mixture-of-Experts, Phoenix Topics, and prompts.

    The For You pipeline in one picture

    Every time you pull to refresh, the same pipeline runs:

    1. Query hydration: pull the requesting user’s engagement history, follow list, topics, served history, and metadata.
    2. Candidate sourcing: gather candidates from Thunder and Phoenix Retrieval in parallel.
    3. Candidate hydration: enrich each candidate with text, media, author, engagement counts, brand-safety labels, language, mutual follow scores.
    4. Pre-scoring filters: drop duplicates, posts that are too old, your own posts, blocked or muted authors, posts you’ve already seen, and posts with your muted keywords.
    5. Scoring: run candidates through Phoenix, combine the predicted action probabilities into a weighted score, attenuate repeated authors, penalise out-of-network content.
    6. Selection: sort by score, take the top K.
    7. Post-selection filtering: final visibility check for deleted, spam, violence, gore, abuse, and de-duplication of conversation branches.
    8. Side effects: cache request info, then return the ranked feed.

    That is the whole story. Every choice the system makes lives in one of those stages.

    Where candidates come from: Thunder vs Phoenix

    Two sources feed the pipeline.

    Thunder is the in-network store. It is an in-memory firehose that consumes post create and delete events from Kafka and serves sub-millisecond lookups for recent posts from people you follow. Posts older than the retention window get trimmed automatically. Thunder is why the feed feels fast.

    Phoenix Retrieval is the out-of-network source. It is a classic two-tower neural network. A user tower encodes your features and recent engagement history into a single embedding. A candidate tower does the same for every post in the global corpus. The system then does an approximate nearest-neighbour search over those embeddings to find the posts whose vectors point most similarly to yours. A dot product between vectors is all it takes.

    How many candidates from each side? The code does not hard-code a ratio. It is set at runtime via parameters (ThunderMaxResults and PhoenixMaxResults). New users get a different retrieval cluster while their account is below an age threshold and a minimum-following count.

    The creator implication is the part most guides miss: follower count is not what gets you into out-of-network feeds. Embedding similarity is. Phoenix knows nothing about how famous you are. It knows that the people who engage with posts like yours have engagement histories that look like the histories of users it is trying to serve.

    How Phoenix ranks posts

    After candidates arrive, every one of them gets a score from the Phoenix ranking transformer. The architecture, per phoenix/README.md, is small by language-model standards:

    • 128-dimensional embeddings
    • 4 transformer layers
    • 4 attention heads
    • 127-position user history sequence
    • 64-position candidate sequence
    • 1,000,000 entries each in the user, item, and author vocabularies (with 2 hash functions per entity)
    • 19 predicted action types

    The transformer’s input is a sequence of your past engagements. Each engagement carries the post you engaged with, the author, the action you took, and the product surface (For You, profile, search). The candidates are appended as a second segment. The model uses candidate isolation masking: candidates can attend to your history but not to each other. This is a deliberate engineering choice. It means a post’s score does not depend on the other posts in the batch, which keeps scoring cacheable and consistent.

    The output is one probability per action type, per candidate.

    The action weights: what positive and negative engagement is worth

    The Weighted Scorer combines those probabilities into a single number:

    “ Final Score = Σ (weight_i × P(action_i)) “

    The exact weight values are not in the open-source repo. They live in an external configuration crate (xai_home_mixer) that xAI tunes continuously. What the repo does show us is the shape of the signal, and that is what matters for strategy.

    Positive weights are applied to these predicted actions:

    • favorite
    • reply
    • retweet
    • quote
    • quoted click
    • click
    • profile click
    • photo expand
    • video view (only counted if the video is above a minimum duration)
    • share
    • share via DM
    • share via copy link
    • dwell (the user lingered on the post)
    • continuous dwell time (how long they lingered)
    • follow author

    Negative weights are applied to:

    • not interested
    • block author
    • mute author
    • report

    A few observations matter for creators. Replies, reposts, follows, and DM shares are listed as their own separately-weighted actions, which is the strongest signal we have that xAI treats them as more valuable than a like. Dwell and continuous dwell time are split into two predictions, which means how long the average person reads your post is its own ranking lever. And the negative actions are not just filters. They actively push the score down for posts that even slightly resemble content that triggers mutes or reports.

    The out-of-network penalty

    Out-of-network content does not start on an even footing with in-network content. The OON Scorer multiplies a candidate’s score by a configurable factor that is less than one when the candidate came from Phoenix Retrieval. This is why following the right accounts still matters.

    There is an explicit override for new users: if your account is fresh and you follow at least the minimum number of accounts, the OON penalty is softened with a more permissive factor. This is the bootstrap mechanism that gets new users a populated For You feed before they have generated enough engagement history for Phoenix to personalise around.

    The author diversity damper

    Once posts are sorted, the Author Diversity Scorer runs. It applies an exponential attenuation to repeated authors:

    “ multiplier(position) = (1 - floor) × decay^position + floor “

    The first post from an author keeps its full score. The second is attenuated. The third more so. A floor value prevents an author from being attenuated below a minimum. The decay and floor are tunable parameters, not constants. The practical consequence is that posting fifteen times in twenty minutes does not produce fifteen top-of-feed impressions. It produces one or two.

    What Grox does (and why it is the most important new piece)

    The grox/ directory is new in May 2026 and is the single most underreported part of the update. It is an asynchronous task-execution engine that runs content classifiers and embedders on every post. Among the tasks present in the repo:

    • Spam detection, including a low-follower reply-spam classifier
    • Safety policy classification across categories like violent media, adult content, hate, self-harm, and platform-policy violations
    • A “post safety screen deluxe” pipeline that re-checks adult content classification with a second pass
    • Media classification for images and video
    • Multimodal post embedding for retrieval and ranking

    Grox does not directly score posts. It produces labels. Those labels are consumed by the visibility filter (VFFilter) at the post-selection stage and by the brand-safety hydrator that ad placement uses. The effect is that posts the system thinks are spam, policy-violating, or unsafe for ads next to do not get removed entirely. They get de-amplified before they ever reach the Phoenix ranking step, or filtered after.

    If you have wondered why a perfectly reasonable post sometimes mysteriously underperforms, this is the likely culprit. A Grox classifier flagged something.

    What the algorithm explicitly does not boost

    This is worth saying twice because the rumour mill keeps repeating the opposite. In the open source code:

    • There is no verified-badge boost in the scorer.
    • There is no follower-count boost.
    • There is no link penalty. Links are not separately weighted.
    • There is no freshness boost. The age filter removes posts above a threshold but does not score newer posts higher.
    • Subscriber status is used for filtering paywalled content, not for boosting reach.

    If verification, Premium, or any of these correlate with reach in practice, it is because the Phoenix transformer has learned to predict that users engage with those posts more, not because a hand-written rule said so. The whole point of the architecture, per the repo, is that every such heuristic has been removed and the model learns the signal from your engagement sequences.

    How ads get inserted

    Ads ride along through the same pipeline. The blender requires at least five organic posts before an ad can be placed. It computes a spacing interval, partitions candidate ads by brand-safety verdict, and caps the number of ads from the safe set to roughly half the safe-set size. A second layer of contextual checks drops ads when neighbouring posts have a weak brand-safety rating, a conflicting handle, or a keyword collision. The result is an interleaved feed that tries to keep brand-safety risk down without starving the auction.

    How creators should post for the 2026 algorithm

    Stop optimising for proxies. Optimise for the actions Phoenix is actually predicting:

    1. Write for replies and reposts, not for likes. A like is one positive weight. A reply, a repost, a quote, and a follow are each separately weighted on top. Posts that ask a question, take a stance, or offer a frame for someone else to argue with consistently outperform posts that close a thought.
    2. Aim for dwell. A long-form thread, a clear photo, or a video that people watch to the end gets two positive signals: dwell and continuous dwell time. A one-line post you scroll past in a quarter-second gets neither.
    3. Earn the follow. Follow-author is a predicted action with its own weight. A post that successfully sells a new viewer on hitting follow scores more than a post that doesn’t.
    4. Do not cluster. Author Diversity attenuates your second and third posts inside the same scoring window. If you have three things to say, space them out by hours, not minutes.
    5. Avoid anything that gets you muted, blocked, or reported. Those carry explicit negative weights. Engagement bait that produces a single block does measurable damage to the score of that post and any signal it sends about the author.
    6. Do not be Grox-flagged. Spam-shaped behaviour (reply-bombing with the same line, posting at high frequency with a low follower count, low-quality media) gets you classified by Grox before you ever reach the scorer.
    7. Follow more accounts in your niche. Phoenix Retrieval is similarity-based, but the OON penalty means in-network candidates still have a head start. The denser your in-network graph in your niche, the more likely your posts surface there.
    8. Build an engagement history that Phoenix can recognise. The user tower encodes your recent engagement. If you want your content to surface to people who like topic X, engage like a person who likes topic X. The model will learn to send your posts to that cluster.
    9. Lean into video and photo. Photo expand and video view are both separately weighted positive actions. They give a single post more ways to score.
    10. DM-share-worthiness is a quiet superpower. Share via DM and share via copy link are each their own weighted action. A post worth sending to a specific friend is, mechanically, a higher-scoring post than a post merely worth liking.

    Can I run the algorithm locally?

    Yes. With the May 15 release, the runnable inference path is phoenix/run_pipeline.py, and the bundled mini Phoenix checkpoint is enough to score sample posts. You can clone the repo, pull the LFS archive, and watch the pipeline rank a batch end to end. This is, as far as we know, the first time a production-scale social recommendation system has shipped a runnable inference path to the public.

    What is next

    Two trends are worth watching. The first is the cadence: xAI has been pushing material updates every few weeks. Expect the action weights, retrieval ratios, and Grox classifier set to keep moving. The second is the architecture: candidate sources for “Phoenix MoE” and “Phoenix Topics” suggest the next direction is multiple specialised ranking experts rather than a single transformer, with topic awareness fed in explicitly. Promptable feeds (telling X in natural language what you want more of) are the user-visible end of that trend.

    The closing point is the practical one. The 2026 For You algorithm is, more than any version before it, a measurement of how people respond to your post. Strategies that try to game routing, freshness, or format are landing in a system that does not care about those things directly. Strategies that earn replies, holds, follows, and shares are landing in a system that is built, end to end, to reward exactly that.