a16z: Predicting the Market, the Emerging Key to Unlocking New Traffic
Original Article Title: Prediction Path Screenshots: a New Kind of Meme
Original Article Author: Alex Danco, a16z
Original Article Translation: Deep Tide TechFlow
In the mid-2010s, a new form of visual content began to emerge in elections, sports matches, and playoff competitions: the "probability over time" chart. These charts were compelling because they told a captivating story: what was initially expected to happen, and what actually happened.

With these images, you can tell many fascinating stories. Solely through probability changes, you can tell stories of collapse, redemption, or the underdog's comeback. (Kurt Vonnegut gave many of these stories a famous name, such as "man in a hole," "boy meets girl," and "downward spiral," each with its own shape.) These images are a "meme": they compress a large amount of information into a small space and fully convey the story when shared.

Despite being highly engaging, these charts had a major limitation: they existed almost exclusively in the realms of politics, sports, or financial markets. The reason is clear: the operation of these charts required widely accepted predictive odds, and these odds had to be legally usable. The financial markets have always had these odds; elections have polling data to leverage, allowing the construction of these probability paths like Nate Silver. And sports seasons (or even individual games) have a clear structure and enough historical data to confidently predict a team's playoff advancement probability. Furthermore, the form of the "story shape" could not extend more deeply into popular culture.
The Slow Arrival of Prediction Markets
Prediction markets address this issue in an obvious way. As long as you can define a contract and its resolution terms, we now have a way to make these "prediction shapes" appear in any ongoing story in the world. Popular predictions—the starting components needed for these stories—have transitioned from scarce to abundant.
In fact, these markets did not emerge overnight, or even initially in this manner. In early 2024, the journal Works in Progress published an article titled "Why Prediction Markets Are Unpopular." The chapter argued that there was "little natural demand for prediction market contracts" because the three types of groups traditionally constituting market participants—savers (seeking wealth accumulation), thrill-seeking bettors (betting for excitement), and savvy traders (trying to profit from market distortions caused by the former two groups)—had no particular reason to engage in prediction markets. Savers might purchase a market index to accumulate wealth in the long term, but they had no reason to bet on the outcome of a presidential election. Thrill-seeking bettors might be more inclined to participate, but they had more engaging speculative ways, such as day trading, meme coins, or sports betting, rather than predicting the outcome of state senate elections. And due to the lower participation of the other two groups, savvy traders also did not see how much money they could make by entering the market.
Due to limited participation from these three groups, the prediction market is destined to suffer from inadequate liquidity and relative uselessness in predicting the future. The poor performance of the prediction market in predicting the mid-term election results in 2022 further validates this viewpoint.
However, in the year and a half since that article was published, an interesting shift has occurred: the prediction market has rapidly entered mainstream pop culture. As predicted by the huge volume of weekly sports game bets, the largest market is in the sports arena. But they have successfully entered mainstream culture—even becoming the theme of an episode of South Park—while also covering markets ranging from the New York City mayoral election results to the Federal Reserve's policy rate path, and even to the timeline of Taylor Swift's wedding.
Breaking the Fourth Wall
What has changed over the past two years? There may not be a one-size-fits-all solution. The 2024 election undoubtedly played a role: Americans have a long history of betting on elections, and the trading volume on the prediction market increased 42 times from early June to the election cycle. However, this momentum did not dissipate after the election.
A key player in this positive feedback loop is a new type of market participant, one that did not exist a few years ago but is now ubiquitous. This participant is akin to promoters in traditional betting activities, like promoters of Las Vegas boxing matches. They are ordinary social media users and a new form of meme—posting prediction path screenshots.
Today, the prediction market is not just about classic market dynamics but also about social media-driven viral dissemination. The key mechanism is to post screenshots when a prediction contract becomes topical, attracting attention and liquidity to the contract.

A good example is a pop culture question contract on the Kalshi platform: "Will Taylor Swift and Travis Kelce get married in 2025?" If you look at the chart, you will notice two important things happening on August 26th, when Swift and Kelce announced their engagement on Instagram. First is the significant increase in odds; second is the substantial increase in liquidity as people start paying attention to the contract. While there will always be some degree of liquidity spike regardless, undoubtedly, these screenshots shared at crucial moments constitute the viral dissemination of the contract itself and serve as the entry point to attract people to place bets. This phenomenon of "breaking the fourth wall" suddenly made a broader audience aware of this meme (or more accurately, aware of the reason for following this contract), adding an interesting new meta-element to the future story.
The New "Protagonist" on the Timeline
**Betting on the Pope** is said to be the "original prediction market" and has recently witnessed the glorious return of this tradition. For Catholics worldwide, this is a great moment as Archbishop Robert Prevost has become the first American pope — Pope Leo XIV. For the betting market, this is also a significant moment as few considered him a competitive candidate: most attention was focused on popular figures like Pietro Parolin and Luis Antonio Tagle.

The day after the smoke cleared, X's @Domahhhh shared the true essence of the timeline: the days leading up to the conclave and the key moments between the decision-making and the revelation, detailing his thought process and bet sizing.

In his own words: "As a directional bet, I decided to place a large sum of money that the next pope would not be [Parolin and Tagle, the two frontrunners]."
After the fourth round of voting, white smoke rose (indicating the successful election of a new pope). Relatively speaking, this was fast. The logical conclusion (which I immediately thought of) was that the front-runners from the first round had consolidated votes and become pope. Parolin's odds rose to around 65%. Tagle held steady at around 20%. These two had an 85% chance of becoming pope, and honestly, even though in hindsight this price was way off, it was hard to feel at the time that it was that off. I was certain I had lost a significant amount of money! I decided not to chase bad money, not to bet more on Tagle/Parolin. I would accept my losses like a good boy."
**But** I did browse the list of other options. When two people are trading at over 85%, everyone else is in the bargain bin. I went scavenging in the bargain bin and found Teksen's odds at 100 to 1, Prevost's odds at 200 to 1. In hindsight, I **should've also bought into Grygik at that time's price**.
I knew one piece of information: a total of four votes. That was too fast for an underdog stock. Toss all underdog tickets in the trash. You need a strong person who can cast two-thirds of the votes in a relatively short time. Those two were the ones I picked. While other traders were focusing on Tagle/Parolin, I bought thousands of shares in each.
A few minutes later, I was surprised to see Prevost — whom I had just bought shares of at 200 to 1 twenty minutes ago — step onto the balcony as the pope."
There used to be a joke that said, "Every day on the timeline has a protagonist, and the goal is to never be the protagonist." This kind of "victory prediction publisher" is a new protagonist, able to become a temporary validated hero.
Placing a Bet
The 2024 presidential election brought a well-deserved redemption story to the prediction markets. It all started with the long process of Biden dropping out of the race, during which the prediction markets provided a useful quantification tool to measure the impact of various events on the probability of a candidate dropping out. From journalists to Wall Street traders, everyone began to rely on prediction markets alongside traditional polling and commentary tools. In the end, despite criticism throughout the election cycle due to the influence of "whale" traders, the prediction markets still outperformed the polls. A year later, Kalshi's daily trading volume now exceeds the levels seen during the 2024 election (at least during football games).

Prediction markets now represent something significant, not just in terms of their utility as financial tools or sources of information. They represent a sense of responsibility and a new role on the timeline: the "hero who makes brave decisions" and the "fool who makes bad decisions." These individuals are now thrust into the spotlight, becoming caricatures reminiscent of Vonnegut's short stories.
In various fields such as politics, business, and culture, we expect our leaders and public figures to truly steer our institutions toward a successful future. This means making bold decisions and proving them right. Over the past few decades, there has been a widespread sense that we have slipped into a culture of leaders lacking accountability, and when individuals step up to resist this trend, we appreciate it even more.
This is perhaps the key mechanism through which prediction markets are set to alter the trajectory of pop culture: not only because betting itself is an information flow that can direct attention where needed, but also because the path of a prediction from start to finish brings new memes to the timeline and thrusts new characters into the forefront.
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Debunking the AI Doomsday Myth: Why Establishment Inertia and the Software Wasteland Will Save Us
Editor's Note: Citrini7's cyberpunk-themed AI doomsday prophecy has sparked widespread discussion across the internet. However, this article presents a more pragmatic counter perspective. If Citrini envisions a digital tsunami instantly engulfing civilization, this author sees the resilient resistance of the human bureaucratic system, the profoundly flawed existing software ecosystem, and the long-overlooked cornerstone of heavy industry. This is a frontal clash between Silicon Valley fantasy and the iron law of reality, reminding us that the singularity may come, but it will never happen overnight.
The following is the original content:
Renowned market commentator Citrini7 recently published a captivating and widely circulated AI doomsday novel. While he acknowledges that the probability of some scenes occurring is extremely low, as someone who has witnessed multiple economic collapse prophecies, I want to challenge his views and present a more deterministic and optimistic future.
In 2007, people thought that against the backdrop of "peak oil," the United States' geopolitical status had come to an end; in 2008, they believed the dollar system was on the brink of collapse; in 2014, everyone thought AMD and NVIDIA were done for. Then ChatGPT emerged, and people thought Google was toast... Yet every time, existing institutions with deep-rooted inertia have proven to be far more resilient than onlookers imagined.
When Citrini talks about the fear of institutional turnover and rapid workforce displacement, he writes, "Even in fields we think rely on interpersonal relationships, cracks are showing. Take the real estate industry, where buyers have tolerated 5%-6% commissions for decades due to the information asymmetry between brokers and consumers..."
Seeing this, I couldn't help but chuckle. People have been proclaiming the "death of real estate agents" for 20 years now! This hardly requires any superintelligence; with Zillow, Redfin, or Opendoor, it's enough. But this example precisely proves the opposite of Citrini's view: although this workforce has long been deemed obsolete in the eyes of most, due to market inertia and regulatory capture, real estate agents' vitality is more tenacious than anyone's expectations a decade ago.
A few months ago, I just bought a house. The transaction process mandated that we hire a real estate agent, with lofty justifications. My buyer's agent made about $50,000 in this transaction, while his actual work — filling out forms and coordinating between multiple parties — amounted to no more than 10 hours, something I could have easily handled myself. The market will eventually move towards efficiency, providing fair pricing for labor, but this will be a long process.
I deeply understand the ways of inertia and change management: I once founded and sold a company whose core business was driving insurance brokerages from "manual service" to "software-driven." The iron rule I learned is: human societies in the real world are extremely complex, and things always take longer than you imagine — even when you account for this rule. This doesn't mean that the world won't undergo drastic changes, but rather that change will be more gradual, allowing us time to respond and adapt.
Recently, the software sector has seen a downturn as investors worry about the lack of moats in the backend systems of companies like Monday, Salesforce, Asana, making them easily replicable. Citrini and others believe that AI programming heralds the end of SaaS companies: one, products become homogenized, with zero profits, and two, jobs disappear.
But everyone overlooks one thing: the current state of these software products is simply terrible.
I'm qualified to say this because I've spent hundreds of thousands of dollars on Salesforce and Monday. Indeed, AI can enable competitors to replicate these products, but more importantly, AI can enable competitors to build better products. Stock price declines are not surprising: an industry relying on long-term lock-ins, lacking competitiveness, and filled with low-quality legacy incumbents is finally facing competition again.
From a broader perspective, almost all existing software is garbage, which is an undeniable fact. Every tool I've paid for is riddled with bugs; some software is so bad that I can't even pay for it (I've been unable to use Citibank's online transfer for the past three years); most web apps can't even get mobile and desktop responsiveness right; not a single product can fully deliver what you want. Silicon Valley darlings like Stripe and Linear only garner massive followings because they are not as disgustingly unusable as their competitors. If you ask a seasoned engineer, "Show me a truly perfect piece of software," all you'll get is prolonged silence and blank stares.
Here lies a profound truth: even as we approach a "software singularity," the human demand for software labor is nearly infinite. It's well known that the final few percentage points of perfection often require the most work. By this standard, almost every software product has at least a 100x improvement in complexity and features before reaching demand saturation.
I believe that most commentators who claim that the software industry is on the brink of extinction lack an intuitive understanding of software development. The software industry has been around for 50 years, and despite tremendous progress, it is always in a state of "not enough." As a programmer in 2020, my productivity matches that of hundreds of people in 1970, which is incredibly impressive leverage. However, there is still significant room for improvement. People underestimate the "Jevons Paradox": Efficiency improvements often lead to explosive growth in overall demand.
This does not mean that software engineering is an invincible job, but the industry's ability to absorb labor and its inertia far exceed imagination. The saturation process will be very slow, giving us enough time to adapt.
Of course, labor reallocation is inevitable, such as in the driving sector. As Citrini pointed out, many white-collar jobs will experience disruptions. For positions like real estate brokers that have long lost tangible value and rely solely on momentum for income, AI may be the final straw.
But our lifesaver lies in the fact that the United States has almost infinite potential and demand for reindustrialization. You may have heard of "reshoring," but it goes far beyond that. We have essentially lost the ability to manufacture the core building blocks of modern life: batteries, motors, small-scale semiconductors—the entire electricity supply chain is almost entirely dependent on overseas sources. What if there is a military conflict? What's even worse, did you know that China produces 90% of the world's synthetic ammonia? Once the supply is cut off, we can't even produce fertilizer and will face famine.
As long as you look to the physical world, you will find endless job opportunities that will benefit the country, create employment, and build essential infrastructure, all of which can receive bipartisan political support.
We have seen the economic and political winds shifting in this direction—discussions on reshoring, deep tech, and "American vitality." My prediction is that when AI impacts the white-collar sector, the path of least political resistance will be to fund large-scale reindustrialization, absorbing labor through a "giant employment project." Fortunately, the physical world does not have a "singularity"; it is constrained by friction.
We will rebuild bridges and roads. People will find that seeing tangible labor results is more fulfilling than spinning in the digital abstract world. The Salesforce senior product manager who lost a $180,000 salary may find a new job at the "California Seawater Desalination Plant" to end the 25-year drought. These facilities not only need to be built but also pursued with excellence and require long-term maintenance. As long as we are willing, the "Jevons Paradox" also applies to the physical world.
The goal of large-scale industrial engineering is abundance. The United States will once again achieve self-sufficiency, enabling large-scale, low-cost production. Moving beyond material scarcity is crucial: in the long run, if we do indeed lose a significant portion of white-collar jobs to AI, we must be able to maintain a high quality of life for the public. And as AI drives profit margins to zero, consumer goods will become extremely affordable, automatically fulfilling this objective.
My view is that different sectors of the economy will "take off" at different speeds, and the transformation in almost all areas will be slower than Citrini anticipates. To be clear, I am extremely bullish on AI and foresee a day when my own labor will be obsolete. But this will take time, and time gives us the opportunity to devise sound strategies.
At this point, preventing the kind of market collapse Citrini imagines is actually not difficult. The U.S. government's performance during the pandemic has demonstrated its proactive and decisive crisis response. If necessary, massive stimulus policies will quickly intervene. Although I am somewhat displeased by its inefficiency, that is not the focus. The focus is on safeguarding material prosperity in people's lives—a universal well-being that gives legitimacy to a nation and upholds the social contract, rather than stubbornly adhering to past accounting metrics or economic dogma.
If we can maintain sharpness and responsiveness in this slow but sure technological transformation, we will eventually emerge unscathed.
Source: Original Post Link

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