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Some gen AI vendors say they’ll defend customers from IP lawsuits. Others, not so much



A person using generative AI — models that generate text, images, music and more given a prompt — could infringe on someone else’s copyright through no fault of their own. But who’s on the hook for the legal fees and damages if — or rather, when — that happens?

It depends.

In the fast-changing landscape of generative AI, companies monetizing the tech — from startups to big tech companies like Google, Amazon and Microsoft — are approaching IP risks from very different angles.

Some vendors have pledged to defend, financially and otherwise, customers using their generative AI tools who end up on the wrong side of copyright litigation. Others have published policies to shield themselves from liability, leaving customers to foot the legal bills.

While the terms of service agreements for most generative AI tools are public, they’re written in legalese. Seeking some clarity, I reached out to vendors about their policies on protecting customers who might violate copyright with their AI-generated text, images, videos and music.

The responses — and non-responses — were enlightening.

Regurgitating data

Generative AI models “learn” from examples to craft essays and code, create artwork and compose music — and even write lyrics to accompany that music. They’re trained on millions to billions of ebooks, art pieces, emails, songs, audio clips, voice recordings and more, most of which came from public websites.

Some of these examples are in the public domain — at least in the case of vendors that trawl the web for training data. Others aren’t, or come under a restrictive license that requires citation or a specific forms of compensation.

The legality of vendors training on data without permission is another matter that’s being hashed out in the courts. But what might possibly land generative AI users in trouble is regurgitation, or when a generative model spits out a mirror copy of a training example.

On the top are images generated by Stable Diffusion from random captions in the model’s training set. On the bottom are images that the researchers prompted to match the originals.

On the top are images generated by Stable Diffusion, an image-generating AI, from random captions in the model’s training set. On the bottom are images prompted to match the originals.

Microsoft, GitHub and OpenAI are currently being sued in a class action motion that accuses them of violating copyright law by allowing Copilot, a code-generating AI, to regurgitate licensed code snippets without providing credit. Elsewhere, thousands of writers have signed an open letter decrying generative AI technologies that “mimic and regurgitate” their “language, stories, style and ideas.”

The cases keep coming.

Authors in California and New York have sued OpenAI for alleged IP theft of their works. Image-generating tool vendors including Stability AI and Midjourney are the subject of lawsuits brought by artists and stock image sites like Getty Images. And Universal Music Group is seeking to ban AI-generated music mimicking the style of musicians it represents from streaming platforms, sending takedown notices to have the songs removed.

Perhaps it’s no surprise, then, that in a recent survey of Fortune 500 companies by Acrolinx, nearly a third said that intellectual property was their biggest concern about the use of generative AI.

The threat of running afoul of copyright with a generative AI tool hasn’t stopped investors from pouring billions into the startups creating those tools. One wonders, however, whether the situation will remain tenable for much longer.

A question of indemnity

In the midst of the uncertainty, you might think that generative AI vendors would stand behind their customers in the strongest terms — if for no other reason than to their allay their fears of IP-related legal challenges.

But you’d be wrong.

From the language in some terms of service agreements — specifically the indemnity clauses, or the clauses that specify in which cases customers can expect to be reimbursed for damages from third-party claims  — it’s clear that not every vendor’s willing to chance a court decision forcing them to rethink their approach to generative model training, or in the worst case their business model.

Anthropic, for instance, which recently inked a deal with Amazon to raise as much as $4 billion and is reportedly seeking another $2 billion investment from Google and others, reserves the right to “hold harmless” itself and partners from damages arising from the use of its generative AI — including those related to IP.

Point blank, I asked Anthropic, which offers strictly text-generating models, whether it would legally or financially support a customer implicated in a copyright lawsuit over its models’ outputs. The company declined to say.

AI21 Labs, another well-funded generative AI startup building a suite of text editing tools, also declined to give an answer. So I looked at its policy.

A21 Labs says that it might “assume exclusive defense and control” of a lawsuit against a customer if the customer chooses not to defend or settle it themselves. But it won’t pay for the privilege; it’ll be at the customer’s own expense.

OpenAI — arguably the most successful generative AI vendor today, with over $10 billion in venture capital and revenue approaching $1 billion — pointed me to its terms of use, which limit the company’s liability to “the amount [a customer] paid for [an OpenAI] service that gave rise to [a] claim during the 12 months before the the liability arose or $100.” That’s the best-case scenario for customers; OpenAI’s policy makes it clear that the company, in many if not most cases, won’t be a party to or defend against copyright lawsuits targeting its users.

Vendors building image- and video-generating AI, where the potential copyright violations tend to be a bit more obvious, aren’t much more supportive contractually their text-first rivals.

Stability AI, which develops music-generating models in addition to image- and text-generating ones, referred me to the terms for its API. The company leaves it to customers to defend themselves against copyright claims and — unlike some other generative AI vendors — has no payout carve-out in the event that it’s found liable.

Midjourney and didn’t respond to my emails — but I found their terms. Midjourney’s policy releases the company from liability for third party IP damages.’s does as well.

Fine print

Now, some vendors — perhaps becoming more attuned to the concerns of enterprise customers considering adopting generative AI, or looking to position themselves as a “safer” alternative — aren’t shying away from committing to protecting customers in the event that they’re sued for copyright infringement. To a point.

Amazon, which recently launched a platform for running and fine-tuning generative AI models, called Bedrock, says that it’ll indemnify (i.e. defend) customers against claims alleging model outputs infringe on a third party’s IP rights. But Amazon’s indemnification policy only applies to the company’s in-house family of text-analyzing models, Titan, as well as Amazon’s code-generating service, CodeWhisperer.

The CodeWhisperer indemnity is broader and applies to all IP claims, including trademarks. However, it requires at least a CodeWhisperer Professional subscription with copyright-defending filtering features enabled. Free users of CodeWhisper aren’t afforded the same protections. And customers must agree to let AWS control their defense and settle “as AWS deems appropriate.”

IBM also provides IP indemnity for its generative AI models, Slate and Granite, available through its Watsonx generative AI service.

“Consistent with IBM’s approach to its indemnification obligation, IBM doesn’t cap its indemnification liability for IBM-developed models,” an IBM spokesperson told TechCrunch via email. “This applies to current [and] future IBM-developed Watsonx models.”

Google wouldn’t respond to my emails. But from the company’s terms, it’d appear that Google offers some defense for customers against third-party allegations of IP infringement arising from its text- and image-generating models. However, Google says that it might suspend a customer’s use of the allegedly infringing model if it can’t find “commercially reasonable” remedies.

Google-backed Cohere, too, has a provision in its terms suggesting that it’ll “defend, indemnify and hold harmless” customers facing third-party claims alleging that Cohere’s models infringe on IP. Given Cohere’s heavy enterprise focus, that’s not surprising.

Microsoft recently made a splashy announcement that it’ll pay legal damages on behalf of customers using its AI products if they’re sued for copyright infringement — so long as those customers use “guardrails and content filters” built into its products.

Which products does it pertain to? That’s where it gets tricky.

Microsoft says its indemnity policy covers paid versions of its portfolio of AI-powered “Copilot” services (including the Microsoft 365 Copilot for Word, Excel and PowerPoint) and Bing Chat Enterprise, the enterprise version of its chatbot on Bing. It also extends to GitHub Copilot, Microsoft’s code-generating service co-developed with OpenAI.

But in its Azure policy, Microsoft clarifies that customers using “previews” of generative AI features powered by its Azure OpenAI Service are responsible for responding to third-party claims of copyright infringement.

Kate Downing, an IP lawyer based in Santa Cruz, takes issue specifically with the Copilot indemnity provision, arguing that — given the vagueness of the provision and its exclusions — the upfront costs of enforcing might be too high for a business to swallow.

By contrast, Adobe claims to offer “full indemnity” protection for users of Firefly, its generative AI art platform, asserting its models are trained on stock images for which Adobe already holds the rights. Users must be enterprise customers, however, and are subject to Adobe’s same liability cap that applies to other tech-based IP claims.

Adobe sometime rival Shutterstock also provides indemnity to all enterprise clients, a policy the company introduced late this summer. So does Getty Images. (Getty Images and Shutterstock, like Adobe, train their models on licensed images.)

The road ahead

It seems likely that, as generative AI vendors, particularly startups, face investor pressure to acquire enterprise customers, indemnification protections will become commonplace. Those customers want the assurance that they won’t be sued over copyright claims, after all.

But if the current state of things is any indication, the policies won’t look similar. And some will have exceptions that’ll make them more attractive in theory than in practice — in other words, more marketing ploy than a legitimate protection.

As a recent article from U.K. law firm Ferrer & Co puts it, indemnities don’t offer a “get out of jail free card” — nor are the a panacea.

“Our key message is, don’t see the offering of provider indemnities as a complete answer to the risk of third-party infringement claims,” the firm writes on its blog. “Instead, weigh the offering of such indemnities in the balance when determining whether to use that provider’s generative AI tool for a project.”

Gen AI customers would do well to remember that.

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MoneyHash raises $4.5M for its payment orchestration platform serving merchants in MENA




The payment landscape in the Middle East and Africa (MEA) region is marked by significant fragmentation, with numerous payment providers and methods in each country, evolving regulations and diverse customer preferences. This complexity is further compounded by challenges such as payment fraud, low checkout conversion rates and high transaction failure rates.

Although the COVID-19 pandemic accelerated the adoption of digital payments in the region, infrastructure development remains inadequate. Payment failure rates are three times higher in the MEA region than the global average, and fraud rates and cart abandonment exceed those of other regions by more than 20%. This presents a challenge for merchants, who often perceive payments as a cost and risk center rather than a strategic enabler.

Payment orchestration platforms streamline payment processes for merchants through unified payment APIs. Egyptian fintech MoneyHash, one of such in Africa and the Middle East, has raised $4.5 million in seed investment, money it plans to use to further invest in its technology and growth across the region. This comes two years after the startup secured $3.5 million in pre-seed.

Nader Abdelrazik, co-founder and CEO of MoneyHash, highlights that 10% of all payments processed in the MEA region are digital, placing MoneyHash uniquely for a growth phase that the region will inevitably experience over the next decade. However, navigating this burgeoning payments market will demand patience and a commitment to continuous learning.

As merchants or companies launch their platforms, they often start by collaborating with one or two payment processing providers. As their operations grow and expand into multiple regions, they onboard additional payment providers to meet their evolving needs. However, integrating different payment stacks presents significant challenges. Besides the operational inefficiencies and technical complexities, in-house tech teams may take several weeks to complete these integrations. In Africa and the Middle East, these challenges are amplified by variations in payment methods, currencies and the isolation between countries.

MoneyHash payment integration catalogue. Image Credits: MoneyHash

MoneyHash’s product includes a unified API to integrate pay-in and pay-out rails, a fully customizable checkout experience, transaction routing capabilities with fraud and failure rate optimizers and a centralized transaction reporting hub. This is complemented by tools enabling various use cases such as virtual wallets, subscription management and payment links. Fintechs such as Revio, Stitch, Credrails and Recital are similar players in the payment orchestration space.

In an email interview with TechCrunch, Abdelrazik shared insights into MoneyHash’s collaboration with merchants over the last four years. For one, he claims that payment failure rates across the region vary significantly, and relying solely on averages can be misleading. While the typical figures are around three out of 10 payments failing on average, the reality differs widely among businesses, he said. For some, it may be as low as one out of 10, while for others, it could be as high as five or six out of 10. Additionally, these figures do not include customers who abandon the checkout process voluntarily before making a payment. The CEO also noted that most of its customers don’t know much about the complexity of payments and, many times, are not aware that most leakages they have in payments are fixable.

Furthermore, merchants are expanding much faster than their partner payment service providers (PSPs). These PSPs operate under stringent regulations, making the rollout of new products and customizations slower than the merchants’ growth trajectory. As a result, MoneyHash has intensified its collaboration with PSPs, particularly those catering to enterprises and prioritizing customer requirements.

“Businesses appreciate the large network of integration we have not just for coverage but for expertise. When they know that we executed all these integrations in-house, they appreciate the team’s expertise and depth of knowledge and leverage our team to navigate difficult questions in payments. They know that working with us makes them future-proof,” noted Abdelrazik, who founded MoneyHash with Mustafa Eid.

“That means team expertise is key for us. Most of the time, we hire exclusively with payments and/or tech backgrounds, even in non-technical positions. We saw massive effectiveness in building a team where customers trust their knowledge and expertise in something specialized and critical like payments.”

Following a beta launch in 2022, which garnered the participation of key regional players like Foodics, Rain and Tamatem, MoneyHash introduced its enterprise suite last October, targeting large enterprises. Over the past year, the fintech, which integrates with various payment gateways and processors, including Checkout, Stripe, Ayden, Amazon Pay, Tap and ValU, claimed to have expanded its network of integrations, tripled its revenue and increased its processing volume by 3,000%.

At present, MoneyHash boasts 50 active paying customers. It does not offer free tiers; most customers accessing its sandbox without payment are potential clients in the assessment stage, numbering over 100. The payment orchestration platform levies a combination of SaaS and transaction fees, commencing at $500 + 0.4%. SaaS fees increase while transaction fees decrease significantly for large enterprises due to volume, Abdelrazik explained.

MoneyHash’s seed round was co-led by COTU Ventures and Sukna Ventures, with participation from RZM Investment, Dubai Future District Fund, VentureFriends, Tom Preston-Werner (GitHub’s founder and early Stripe investor) and a group of strategic investors and operators.

Speaking on the investment, Amir Farha, general partner at COTU, said his firm believes that the full potential of digital payments in MEA is yet to be realized and MoneyHash’s platform can catalyze the growth of digital payments across the region, enabling both global and local merchants to tap into new revenue streams. “We are thrilled to renew our support to a team that has consistently demonstrated superior execution, not just in securing top mid-market and enterprise customers, but also in expanding value across the entire chain, even under challenging market conditions,” he added.

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Snowflake CEO Frank Slootman stepping down — and Wall St hates it




Apparently Frank Slootman, the veteran tech executive, was popular with investors, at least judging from their reaction that he will be stepping down as CEO of Snowflake. The company stock price has plunged more than 24% in after-hours trading on the news.

Slootman will retreat into the role of chairman of the board, while Sridhar Ramaswamy, former head of Google Ads, who came to the company when it bought AI search engine Neeva last year, will take over as chief executive.

Lost in today’s executive shuffle was the company’s earnings. It reported revenue of $738 million, up a healthy 33% year over year with guidance for the next quarter of between $745 and $750 million, with growth of 26-27%.

Slootman came on board in 2019, taking over for veteran executive Bob Muglia, and was charged with taking the company public the following year. Over the last year, the stock has done well, up around 50% (the exact amount is hard to tell with this afternoon’s downward spiral), as many tech stocks recovered from 2022 doldrums.

He was famously well-compensated, with a base salary of $375,000 and a rather attractive stock option. In fact, Fortune reported that the chief executive was making an eye-popping $95 million a month at one point.

He raised eyebrows in 2021 when he told a reporter that diversity shouldn’t override merit, and eventually walked back those comments after a negative reaction from industry peers.

Prior to coming to Snowflake, he spent six years as chairman and CEO at ServiceNow. With all that cash, perhaps he’ll retire and enjoy his money.

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Fintech giant Stripe’s valuation spikes to $65B in employee stock-sale deal




Payments infrastructure giant Stripe said today it has inked deals with investors to provide liquidity to current and former employees through a tender offer at a $65 billion valuation.

Notably, the valuation represents a 30% increase compared to what Stripe was valued at last March when it raised $6.5 billion in Series I funding at a $50 billion valuation. But it is also still lower than the $95 billion valuation achieved in March of 2021.

While Stripe declined to comment beyond a written statement, a source familiar with the internal happenings in the company told TechCrunch that Stripe and some of its investors agreed to purchase over $1 billion of current and former Stripe employees’ shares.

The company, which counts the likes of Alaska Airlines, Best Buy, Lotus Cars, Microsoft, Uber and Zara as customers, had noted at the time of its last raise that the proceeds would go to “provide liquidity to current and former employees and address employee withholding tax obligations related to equity awards.” That, it added, would result in the retirement of Stripe shares that would offset the issuance of new shares to Series I investors.

A Stripe IPO has been long anticipated and was widely expected to happen in 2024. But with this deal, it appears that an initial public offering may not take place until next year.

In January, TC’s Rebecca Szkutak reported that — in anticipation of that IPO and according to secondary data tracker Caplight — there had been “an absolute flurry of buyers looking to get shares in the company in recent months.” On January 2, a secondary sale closed that valued Stripe shares at $21.06 apiece and valued the startup at $53.65 billion, according to Caplight data.

While Stripe did not name the investors participating in the latest deal, Sequoia Capital Managing Partner Roelof Botha was quoted in Stripe’s announcement and The Wall Street Journal cited Goldman Sachs’s growth equity fund as another backer.

The WSJ also reported that the transaction “is part of a commitment by the Collison brothers to provide liquidity annually to longtime and former employees.” Sources familiar with internal happenings at the company said that commitment is more to provide liquidity “regularly,” and not necessarily annually.

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