Phil Jacobson, a former executive at Vector and current chief business officer at Altitude, said Fomo’s growth has pushed him to rethink what Vector might have become had it taken a different path. Jacobson wrote that Vector, a mobile social trading app for onchain assets, grew from zero to more than $20 million in daily trading volume and about $1 billion in cumulative volume before the company was sold to Coinbase at the end of 2025. He added that in its first months after launch, retention looked closer to that of a strong social app than a trading app.
Since then, Jacobson said, he has kept watching Fomo. In his view, Fomo extended a product thesis that was close to Vector’s but executed it along a different route. He wrote that Fomo has moved beyond crypto Twitter, brought a large number of first-time users into onchain markets, and recently pushed daily spot volume above $100 million.
Vector started from a belief that meme coins could bootstrap social trading
Jacobson traced Vector’s story back to Tensor. Before joining the company as vice president of operations, he participated in Tensor’s first and only funding round as an angel investor. At that time, Tensor had almost no market share. By the time he joined, he wrote, it had become the dominant NFT marketplace on Solana, with peak market share above 80% and cumulative trading volume in the billions of dollars.
He recalled that on roughly his second day at the company, Ilja told him directly: 「We don’t know what happens next for NFTs, but we think the next wave will be meme coins, and we want to build a product around that.」 For the team, the larger opportunity was not just meme coins as an asset class. It was social trading.
Jacobson argued that trading had already become deeply social. He pointed to GameStop and WallStreetBets as clear examples. More people were making their own investment decisions and relying on voices they trusted online rather than asset managers or traditional financial institutions. In crypto, he said, that pattern was even more visible. He cited Ansem as an example of someone on Twitter who was able to identify major opportunities early, adding that Ansem had been strongly recommending Solana when SOL was around $8.
The problem, as Jacobson framed it, was the gap between discovery and execution. A user might see a token mentioned on Twitter or in a Telegram group, decide whether to buy it, and then still need to locate the right contract address. On mobile, that process often meant opening Phantom, then a browser, then Jupiter, connecting the wallet, pasting the contract address, checking the token, entering the amount, and finally placing the trade. In meme coin markets, he wrote, a delay of a few minutes could be enough for the opportunity to disappear.
That led to a firm product view inside Vector: social signals and execution had to sit inside the same app, and the path from seeing a signal to placing a trade had to be cut as much as possible, ideally to one step. Jacobson cited a well-known line from Chris Dixon: 「Everything interesting starts out looking like a toy.」 That was how the team viewed meme coins. In his telling, meme coins were the “toy” that could cold-start a social trading network.
He said the long-term vision went further. As more important assets move onchain, the same social trading network could extend to them. If a product can keep users, build a social graph around trading and alpha, and offer strong execution, then expanding from meme coins into stocks or other assets becomes much easier, especially if more assets themselves are moving onchain.
Jacobson added that stocks are backed by operating businesses while meme coins usually are not, yet the logic behind how both trade is becoming more alike. He described GameStop as an early and extreme example, then pointed to investment themes around memory, new cloud providers, and hyperscale cloud companies as cases where social distribution, market narratives, and price momentum play a large role. He also mentioned Leopold Aschenbrenner, writing that his views on the direction of AI had given him substantial market influence and that investors closely follow his positions in Bloom Energy, CoreWeave, and Micron.
His conclusion was that the behaviors seen in meme coin markets are not unique to meme coins. Meme coins, in his words, simply amplify a trading behavior that is spreading into much broader markets.
How Vector found product-market fit
Jacobson said the easiest way to explain Vector was as a mix of Instagram and Robinhood. If photos are the core unit of Instagram and video is the core unit of TikTok, then charts were the core unit inside Vector.
When users opened the app, they first saw a social feed. If someone shared a trade, other users saw a live chart for that token. People who had traded that token through Vector appeared directly on the chart through profile pictures placed at their own buy or sell points. The algorithm pulled the most important signals from across the network and surfaced them in the feed. Once a user saw a signal, a trade could be placed almost immediately. The goal was to compress the path from social signal to execution from minutes to seconds, and ideally even into milliseconds.
He also highlighted an early design choice: putting user avatars and trades directly on price charts. At the time, he said, no other product was doing that. He remembered his first reaction when he saw it internally: 「This is genius.」 He added that the interaction pattern has since become common across many trading apps.
For Jacobson, the simplest description of product-market fit, or PMF, came from whether users were effectively pulling the product out of the team’s hands faster than the company could keep up. Vector saw that even before the public launch. During testing, users kept pressing the team for more invite codes so they could bring friends in.
Vector launched around the end of November 2024 and quickly spread across crypto Twitter, according to Jacobson. Daily volume soon reached about $1 million. By late January the following year, around the time the Trump meme coin launched, peak daily volume had climbed above $20 million.
Retention was just as striking. Jacobson said he no longer remembered the exact figures, but his recollection was that day-7 retention was roughly 60% to 70%, while day-30 retention was around 40% to 50%. Users opened Vector repeatedly, traded, followed each other, shared investment theses, invited friends, and mirrored trades from accounts they trusted.
The team had fewer than 25 people at the time. As growth accelerated, pressure showed up everywhere: outages, trades that sometimes failed to execute, overwhelmed support, and an endless queue of product requests. Jacobson wrote that this was the clearest PMF experience he had ever had. Real demand, in his view, puts simultaneous pressure on product, trading, support, and engineering, forcing every part of the company to speed up, sometimes faster than the team itself can move.
That experience also reinforced one of his startup beliefs: a small team with very high talent density can produce results far beyond its size, and nothing matters more than staying close to users. He wrote that customer centricity has to be driven from the top, because if the team is not speaking directly with users, handling support issues, and seeing product gaps firsthand, it can drift away from the problems the product actually needs to solve.
Why Vector moved toward professional traders
As meme coin markets cooled, Jacobson said a structural issue became harder to ignore. Retail users often traded less or left after losses accumulated, while professional traders were able to stay profitable, keep trading, and drive a large share of volume.
He put the concentration plainly: roughly 5% of Vector users accounted for about 95% of trading volume. Given that reality, the team made what he described as a rational decision to go after the professional trader market.
That user base had very different needs. Professional traders usually sit in front of multiple screens, watch many charts at once, and move quickly across positions. Vector was a strong mobile product and many professionals used it, but for them, the phone was typically a supplement to their main trading setup rather than the place where most trades happened.
Competition was heating up as well. Jacobson said Axiom had built an excellent product, while Photon, BullX, and others were going after the same users. Because professionals were driving the vast majority of volume, Vector started building a desktop product in hopes of turning it into the main trading interface for that market. At the time, he wrote, this looked like the best path to win.
Even now, Jacobson said, he still believes the strategy was defensible. Vector’s desktop product was strong, early test users liked it a lot, and the team was confident in its go-to-market plan. But the product never launched publicly, so the strategy was never tested in the market.
Looking back, he now sees another side of that decision. The company focused on how to compete for existing users rather than on how to make the whole market larger. It spent far less time asking whether a social trading product could expand the market by bringing in people who had never traded onchain before. He said that was the road Fomo ended up taking.
What Jacobson thinks Fomo got right
The most interesting part of Fomo’s story, in Jacobson’s view, is the type of user it chose to serve.
When Vector was building desktop, professional traders were the clearest opportunity in the market. Axiom was growing fast, pros drove most of the volume, and a rising number of products were battling for the same group. Much of the industry’s attention was fixed there.
Fomo went the other way. Jacobson wrote that it targeted channels outside crypto Twitter, including TikTok and Instagram, and focused on a large population that had never participated in onchain trading. Instead of competing for the same mature traders, Fomo aimed at what he described as a huge consumer market that the industry had largely ignored.
Timing mattered too. He said Fomo started to grow after the peak of the meme coin boom had already passed, when the market was less frenzied than the period Vector operated in and short-term speculation had cooled. Jacobson said he did not know whether the same strategy would have produced the same outcome at the top of the cycle, but he credited Fomo with choosing a different user at the right time and executing well.
He wrote that Fomo found a way to reach people through channels outside the traditional crypto audience, bring them into the product, and push them into their first onchain trade. That is not easy to do. It requires strong distribution and strong product design at the same time: the product has to make onchain trading understandable to people unfamiliar with it, get them to start using it, and give them a reason to come back repeatedly.
Jacobson also argued that Fomo understood the product details that actually matter to this group. Simply distributing Vector through the same channels would not have produced the same result, he wrote. Serving that user segment would have required redesigning the product around its needs.
For him, the lesson is not that Vector was wrong to focus on professional traders. He said he still believes the desktop strategy may have worked very well. The more important point is that a much larger market existed outside the one Vector was optimizing for, and the company never spent enough time exploring it. Fomo did.
He summed it up this way: the users who help a product find PMF are not always the users who take it to much larger scale. The initial market can be the right starting point, while still representing only a small slice of the eventual opportunity. Once a company finds something users really want, it still has to answer another question: which new users can this product serve, and which new markets can it enter?
Jacobson acknowledged that this is easier to say in hindsight than while operating the company. All the available data comes from the market a team is already serving. That data can help a company win where it is, but it struggles to answer whether untouched users will respond to the product or whether untested channels can create new growth.
In Vector’s case, he said, the data showed that professional traders accounted for most of the onchain meme coin market’s volume. It could not answer what would happen if a social trading product were placed in front of people who had never traded onchain before. Fomo, in his view, has now answered that through scaled growth.
Why he thinks the social trading opportunity is getting bigger
Watching Fomo has also made Jacobson more confident that Vector’s original high-level thesis on social trading was right, and that the opportunity is expanding faster than he expected.
He wrote that the world is becoming more financialized. More people are investing and trading independently, and markets are discussed openly in public spaces. Investment views spread through social networks, people come to trust certain traders, investors, and creators, and capital then follows those information networks and shared beliefs. Trading and investing, in his words, have already become distinctly social.
He argued that this cuts across asset classes. It is visible in meme coins and crypto assets, in prediction markets, and increasingly in equities as well.
He also expects that trend to accelerate. The world is more connected, information moves faster, and AI will materially improve people’s ability to find and synthesize information. At the same time, more assets are moving onchain. He listed stocks, prediction markets, options, and real-world assets, or RWA, along with financial products that may not even be imagined yet, as categories that are gradually migrating onto a more global, always-on financial infrastructure.
Under that framework, a product that captures a high-value social network organized around trading and alpha, while also delivering strong execution, has a chance to become a key gateway to onchain trading. Meme coins can be the entry point, he wrote, but the product does not have to stop there. As more financial assets move onchain, new asset classes can be added almost like modules.
Jacobson said this had always been part of the Vector vision. What Fomo changed for him was the clarity around the size of the opportunity and the speed at which it may arrive. Users are already willing to trade onchain and to use financial products with social features, he wrote, and Fomo has shown that this experience can reach a much broader audience than crypto-native users alone.
He added that Fomo now appears to be in a strong position because it is expanding beyond meme coins through perpetual futures. If the team maintains its execution, he said, the growth runway could widen further. The clearest comparison, in his view, is Robinhood, except Fomo started from day one with a social network inside the product and direct exposure to onchain assets.
A look back at the road Vector did not take
Jacobson closed with a counterfactual. If Vector had remained independent, could it have become a company worth several billion dollars? His answer was yes, absolutely possible, and perhaps even more. He cited the quality of the product, the strength of the team, and a strategy that he still believes could have worked.
At the same time, he allowed for multiple outcomes. Vector might eventually have moved toward a broader consumer market. Fomo might still have beaten it. Or Vector could be larger than Fomo today. He added, in a lighter line, that maybe quantum technology will one day make it possible to run that simulation in a parallel world.
What interests him now, though, is not the hypothetical itself. It is seeing another strong team explore a path Vector never really took. He wrote that he enjoys watching from the stands as Fomo enters a market Vector never truly tried to serve and scales social trading far beyond what Vector achieved.
For Jacobson, that observation has settled two convictions. Financial markets are already deeply social and will become even more social. At the same time, more of the world’s assets will move onchain. Vector, he wrote, offered an early sketch of that future. Fomo is showing how large that future might become.