#319: Square Integrates With Apple Tap-To-Pay, & More

Square––Block’s seller business––recently announced that it will integrate with Apple’s new Tap-to-Pay feature, allowing merchants to accept contactless payments using its Point-of-Sale (POS) iPhone app and providing “new commerce experiences” such as serving customers on the spot and accepting contactless payments on the go.

Companies such as Square and Toast sell POS hardware to merchants, including small handheld tablets equipped with a Near-Field Communication (NFC) chip that can accept cards by tapping, swiping, or inserting a chip. Tap-to-Pay could reduce the need for portable POS devices, lowering Square’s losses on hardware sales. Importantly, through its POS app, third-party POS devices such as the iPhone would be integrated with Square’s suite of software and financial solutions upon which many Square merchants rely in running their day-to-day businesses.

For its part, Apple could tap into a new revenue stream. Since 2014, the company has collected 0.15% of each Apple Pay credit card transaction from the consumer’s issuing bank. Tap-to-Pay could enable them to collect a toll on the merchant side of the transaction as well, charging POS companies like Square a few basis points per transaction.

Meta AI is regarded widely as a best-in-class research organization alongside its peers, Alphabet’s DeepMind and startup OpenAI. Meta AI publishes valuable, cutting-edge research on a wide range of topics from graph neural networks to vision transformers and multi-language translation.

For the last four years, Meta AI has functioned as a centralized R&D organization siloed from the product development teams across Facebook, Instagram and other Meta properties. Following COO Sheryl Sandberg’s departure, Meta AI will shift to a decentralized structure, integrating AI researchers with product teams to leverage AI across its product lines.

In the same way that a public company publishes quarterly financial statements disclosing growth rates and earnings, public blockchains provide a real-time, global ledger that publishes data about the network’s activity and inner economics.

Considering the market’s fast pace of change, ARK is introducing The Bitcoin Monthly, an “earnings report” that details relevant on-chain activity and showcases the openness, transparency, and accessibility of blockchain data. While the analysis of these reports will be focused on Bitcoin initially, we expect to expand our commentary to the broader crypto markets in the near future.

Please subscribe here to download The Bitcoin Monthly May Report and see below for the topics we address:

• Bitcoin closed the month of May down 17.2%, declining from $38,480 to $31,835.
• Bitcoin still faces an uncertain macro environment, as the global economy shows signs of a recession.
• Terra blowup marks one of the largest fiascos in crypto market history, sending a warning to crypto markets.
• Bitcoin’s on-chain fundamentals remain strong, as short-term holders capitulate.

This week, Ultima Genomics claimed that its first instrument, the UG100, will sequence a human genome for under $100. The news surprised and perhaps delighted the life sciences community, as the price to sequence a human genome using Illumina (ILMN) technology essentially plateaued once it crossed the $1,000 barrier in 2014.

Since its creation in 2016, Ultima has raised more than $600 million from marquee investors including General Atlantic, a16z, D1 Capital, and Khosla Ventures. It also has published several pre-print manuscripts covering high-throughput (HT) applications with renowned collaborators like the Broad Institute and the Whitehead Institute. Among them are large single-cell sequencing projects, epigenetic sequencing, and liquid biopsy testing—applications that should be supercharged by Ultima’s low-cost profile.

According to ARK’s research, Ultima’s UG100 platform is slightly faster than Illumina’s flagship NovaSeq and generates data for $1/GB, less than 20% of NovaSeq’s $6/GB, and may have room to become even cheaper. Caveat emptor: these figures are based on consumables only, not hardware amortization or other costs like labor and computing. In addition, Ultima’s platform seems prone to systematic errors near repetitive genomic stretches called homopolymers, which could limit its utility in certain applications. Finally, while UG100’s 300 base-pair (bp) read lengths are somewhat longer than NovaSeq’s 250 bp reads, they are a long way from true long reads, which are thousands of times larger. As such, Ultima’s system seems incapable of deciphering larger, structural mutations and tough-to-read genomic regions like tandem repeats.

Presently, Illumina seems to be maximizing gross profitability over lower price points, perhaps because it has not faced competition in high-throughput (HT) sequencing where its price per genome has been the lowest for the last decade. While a new cohort of short-read sequencing platforms from Element Biosciences, Singular Genomics (OMIC), and Pacific Biosciences (PACB) has cropped up, none targets the HT market. Ultima focuses singularly on the HT market.

We don’t place outsized emphasis on the $100 genome price point, as the peripheral costs of sequencing, such as sample prep and compute, often dominate the economics of an experiment. Illumina’s informatics tools are accessible and robust, an advantage for HT users who generate enormous amounts of sequencing data. Our conversations with HT users also suggest the importance of the training and support services offered by more established vendors.

That said, in our view, Ultima’s UG100 is a viable and economically tantalizing alternative to Illumina’s NovaSeq. Now that it owns Grail, Illumina is competing with several of its large, diagnostic customers who, for that reason, might consider a switch to Ultima. Given its high consumables margins, Illumina could respond by lowering prices.

Whatever the outcome, lower sequencing costs will be a tailwind to the basic and applied life science industries that are enjoying substantial secular growth. We look forward to learning more during next week’s AGBT Conference now that Ultima Genomics is a Silver Sponsor.

On May 18, the Lens Protocol social graph went live on the Polygon blockchain, prompting a fresh look at the potential impact of decentralized social media.

Founded by Stani Kulechov, Lens aims to power the infrastructure layer that enables the creation and use of decentralized social graphs. Those graphs, in turn, enable:

1. more open and free social media experiences without censorship or curation by the platform providers;
2. exit optionality that allows them to port all owned assets––such as profiles, friends, networks, and content––from one decentralized application (dApp) to another.

Lens Protocol enables the construction of social graphs via profile NFTs that function as the main primitive of the protocol, allowing users, at least theoretically, to own their social profiles. All other core actions––following, publishing content, sharing, and commenting––require the minting of new NFTs that reference profile NFT IDs to ensure that all assets are “owned”. Users own all related assets if the profile NFTs remain in their wallets, and they can sell their content by allowing others to mint and collect it at a price. Lens also incentivizes curation, as users who repost content can receive a share of the revenue. Lens even enables governance, allowing users to reward their followers with votes.

Users have built more than 50 front-end applications on Lens including Lenster, a blockchain-based version of Twitter, and Iris, a social platform like Patreon capitalizing on the network effect of the underlying platform. As of this writing, nearly 16,000 profile NFTs have been minted since Lens launched on May 18.

Questions about this protocol are proliferating. Can developers rely on Lens’ shared userbase to bootstrap growth as the protocol grows and competition among the apps within the ecosystem increases? Will Polygon, Ethereum, and Lens scale to such an extent that porting assets outside Ethereum-compatible chains no longer matters, or will Lens be forced to accommodate a multi-chain future? Will scaling technologies improve enough to support the throughput required to handle all social media interactions with mass adoption? Even if Lens can scale, will consumers spend money to engage on social media? Does Lens risk a single point of failure by using a custom, off-chain indexer? Can it compete against Web 2.0 giants without the centralized recommendation algorithms and censorship mechanisms that created TikTok?

We are watching closely this attempt to decentralize social media. According to ARK research, online expenditures enabled by Web3 could grow at a 28% compound annual rate, reaching $12.5 trillion in the next ten years. The Lens Protocol could become a meaningful contributor to that potential growth.