Lydia is splitting itself into two apps — Lydia for P2P payments and Sumeria for those looking for a mobile-first bank account.
© 2024 TechCrunch. All rights reserved. For personal use only.
Reddit — now a publicly-traded company with more scrutiny on revenue growth — is putting a big focus on boosting its international audience, starting with francophones. In their first-ever earnings call, CEO Steve Huffman confirmed the company is now working on automatic translations of the site’s content, in real-time, into French, thanks to advances in large language models.
He also touched on ecosystem expansion by way of a couple of u-turns. It wants to court developers with new tools — a surprise given the company’s history on this front; and it is planning a reintroduction of Reddit Gold — another surprise considering the company canned its virtual currency efforts less than a year ago.
Moves like these are anticipating what the future, rather than the present, might look like for Reddit. Today, its news is relatively encouraging, with Reddit’s revenue in the last quarter jumping 48% year-over-year to $243 million, and unique users growing 37% to 82.7 million. (That figure includes both logged-in and logged-out users, similar to how Twitter — now called X — used to count its audience when it was public.)
Huffman, speaking on the earnings call, said that half of Reddit’s audience is U.S.-based, which points to the company putting more focus on how to increase the international proportion.
“We’re still 50/50 U.S. versus non-U.S., but our peers are more than 80% to 90% non-U.S.,” he said. “I think there’s a huge opportunity there.” He went on to describe automatic, AI-powered translation as “one of the big unlocks for us in the near to medium term.”
“So we’re translating our entire corpus today that is mostly in English into the other languages and hope that will help accelerate international growth,” he said.
The site-wide translation effort is still a test, in his words, although there is a lot of resource being put into it. Huffman noted that this content is also being indexed on Google results for the French language, driving more traffic to the site, and the company next wants to tackle Spanish.
Reddit been offering post-based translation since last year with support for eight languages.
It was surreal to hear Huffman talk about developer tools on the call. It was only in July 2023 that the company found itself embroiled in a massive feud with third-party client developers over API changes — resulting in the blackout of hundreds of subreddits in protest of API changes.
Now the social network is in play-nice mode. Huffman said there are plans for tools that could “push the boundaries of what a subreddit can be.” He gave examples of some ongoing experiments like live scores on some sports subreddits and a live stock ticker on r/wallstreetbets, the subreddit known for the Gamestop stock saga.
A few hundred developers are already testing these new experiences, he said. Reddit aims to include more developers from the waitlist this summer and enable monetization features later in the year.
Earlier this year, Reddit signed a deal with Google to let the search engine company use the social network’s data. In answering a question from the Reddit community, Huffman said that the company plans to license data to other companies as well. This has been a big issue, and in many cases controversial in light of the fact that users may not want their data being used for, say, AI training or some of the other common purposes these days. For what it’s worth, Huffman, you might have guessed, insisted that the company is being “considerate and selective” in how it selects partners.
The name of the game for the company right now is building more infrastructure for revenue generation. So while Gold and another currency effort, blockchain-based community points, were both canned last year, virtual currency is going to getting another airing on the platform because it represents an opportunity for Reddit to diversify its business model. During the earnings call, the company noted that it plans to launch a “revamped” Gold product, with programs for users to spend and earn money on the platform.
Last month, Reddit CPO Pali Bhat mentioned some of these new initiatives in an interview with TechCrunch, and now the company is making moves to leverage those growth instruments.
Samsung Medison, a medical device unit of Samsung Electronics that specializes in developing diagnostic imaging devices, said on Wednesday it plans to acquire Sonio, a Paris-based startup that makes AI-powered software for ultrasound workflows, for about $92.7 million (KRW 126 billion).
The French startup’s AI assistant is aimed at helping obstetricians and gynecologists with the evaluation and documentation of ultrasound exams, and it has also received regulatory clearance in the United States (FDA 510(k)) for Sonio Detect, a product that uses deep learning algorithms to improve the image quality of ultrasound scans in real time.
Samsung Medison said Sonio’s software would help it bring better AI-driven imaging workflows to the market. Samsung Electronics, which owns a 68.45% stake in the medical device unit, acquired Medison for $22 million in 2011.
Samsung said in a statement that following the acquisition, Sonio will remain an independent company and continue to grow commercially and offer products and services in France.
Co-founded by Cecile Brosset (CEO) and Remi Besson (CSO) in 2020, Sonio most recently secured $14 million in a Series A led by Cross Border Impact Ventures in August 2023. The company has raised a total of $27.2 million, according to Tracxn, and its investors include Elaia, Bpifrance French Tech Seed, OneRagtime, and a few angel investors.
“Through the acquisition of Sonio, Samsung Medison will continue to deliver upon our promise to improve the quality of people’s lives with technology,” said Yong Kwan Kim, CEO of Samsung Medison. “Collaboration with Sonio will bring together best-in-class ultrasound AI technology and reporting capabilities to bring a paradigm shift in the prenatal ultrasound exam.”
“Samsung Medison’s established global ultrasound business combined with Sonio’s advanced AI creates an exciting growth opportunity for both sides,” said Brosset, CEO of Sonio. “We have found in Samsung Medison an amazing, trusting partner to pursue and accelerate our roadmap and mission. In addition to close collaboration with Samsung Medison, as an independent company, Sonio will continue to advance medical reporting technology and diagnostic software globally, including for underserved areas in healthcare.”
A French startup has raised a hefty seed investment to “rearchitect compute infrastructure” for developers wanting to build and train AI applications more efficiently.
FlexAI, as the company is called, has been operating in stealth since October 2023, but the Paris-based company is formally launching Wednesday with €28.5 million ($30 million) in funding, while teasing its first product: an on-demand cloud service for AI training.
This is a chunky bit of change for a seed round, which normally means real substantial founder pedigree — and that is the case here. FlexAI co-founder and CEO Brijesh Tripathi was previously a senior design engineer at GPU giant and now AI darling Nvidia, before landing in various senior engineering and architecting roles at Apple; Tesla (working directly under Elon Musk); Zoox (before Amazon acquired the autonomous driving startup); and, most recently, Tripathi was VP of Intel’s AI and super compute platform offshoot, AXG.
FlexAI co-founder and CTO Dali Kilani has an impressive CV, too, serving in various technical roles at companies including Nvidia and Zynga, while most recently filling the CTO role at French startup Lifen, which develops digital infrastructure for the healthcare industry.
The seed round was led by Alpha Intelligence Capital (AIC), Elaia Partners and Heartcore Capital, with participation from Frst Capital, Motier Ventures, Partech and InstaDeep CEO Karim Beguir.
FlexAI team in Paris
To grasp what Tripathi and Kilani are attempting with FlexAI, it’s first worth understanding what developers and AI practitioners are up against in terms of accessing “compute”; this refers to the processing power, infrastructure and resources needed to carry out computational tasks such as processing data, running algorithms, and executing machine learning models.
“Using any infrastructure in the AI space is complex; it’s not for the faint-of-heart, and it’s not for the inexperienced,” Tripathi told TechCrunch. “It requires you to know too much about how to build infrastructure before you can use it.”
By contrast, the public cloud ecosystem that has evolved these past couple of decades serves as a fine example of how an industry has emerged from developers’ need to build applications without worrying too much about the back end.
“If you are a small developer and want to write an application, you don’t need to know where it’s being run, or what the back end is — you just need to spin up an EC2 (Amazon Elastic Compute cloud) instance and you’re done,” Tripathi said. “You can’t do that with AI compute today.”
In the AI sphere, developers must figure out how many GPUs (graphics processing units) they need to interconnect over what type of network, managed through a software ecosystem that they are entirely responsible for setting up. If a GPU or network fails, or if anything in that chain goes awry, the onus is on the developer to sort it.
“We want to bring AI compute infrastructure to the same level of simplicity that the general purpose cloud has gotten to — after 20 years, yes, but there is no reason why AI compute can’t see the same benefits,” Tripathi said. “We want to get to a point where running AI workloads doesn’t require you to become data centre experts.”
With the current iteration of its product going through its paces with a handful of beta customers, FlexAI will launch its first commercial product later this year. It’s basically a cloud service that connects developers to “virtual heterogeneous compute,” meaning that they can run their workloads and deploy AI models across multiple architectures, paying on a usage basis rather than renting GPUs on a dollars-per-hour basis.
GPUs are vital cogs in AI development, serving to train and run large language models (LLMs), for example. Nvidia is one of the preeminent players in the GPU space, and one of the main beneficiaries of the AI revolution sparked by OpenAI and ChatGPT. In the 12 months since OpenAI launched an API for ChatGPT in March 2023, allowing developers to bake ChatGPT functionality into their own apps, Nvidia’s shares ballooned from around $500 billion to more than $2 trillion.
LLMs are pouring out of the technology industry, with demand for GPUs skyrocketing in tandem. But GPUs are expensive to run, and renting them from a cloud provider for smaller jobs or ad-hoc use-cases doesn’t always make sense and can be prohibitively expensive; this is why AWS has been dabbling with time-limited rentals for smaller AI projects. But renting is still renting, which is why FlexAI wants to abstract away the underlying complexities and let customers access AI compute on an as-needed basis.
FlexAI’s starting point is that most developers don’t really care for the most part whose GPUs or chips they use, whether it’s Nvidia, AMD, Intel, Graphcore or Cerebras. Their main concern is being able to develop their AI and build applications within their budgetary constraints.
This is where FlexAI’s concept of “universal AI compute” comes in, where FlexAI takes the user’s requirements and allocates it to whatever architecture makes sense for that particular job, taking care of the all the necessary conversions across the different platforms, whether that’s Intel’s Gaudi infrastructure, AMD’s Rocm or Nvidia’s CUDA.
“What this means is that the developer is only focused on building, training and using models,” Tripathi said. “We take care of everything underneath. The failures, recovery, reliability, are all managed by us, and you pay for what you use.”
In many ways, FlexAI is setting out to fast-track for AI what has already been happening in the cloud, meaning more than replicating the pay-per-usage model: It means the ability to go “multicloud” by leaning on the different benefits of different GPU and chip infrastructures.
For example, FlexAI will channel a customer’s specific workload depending on what their priorities are. If a company has limited budget for training and fine-tuning their AI models, they can set that within the FlexAI platform to get the maximum amount of compute bang for their buck. This might mean going through Intel for cheaper (but slower) compute, but if a developer has a small run that requires the fastest possible output, then it can be channeled through Nvidia instead.
Under the hood, FlexAI is basically an “aggregator of demand,” renting the hardware itself through traditional means and, using its “strong connections” with the folks at Intel and AMD, secures preferential prices that it spreads across its own customer base. This doesn’t necessarily mean side-stepping the kingpin Nvidia, but it possibly does mean that to a large extent — with Intel and AMD fighting for GPU scraps left in Nvidia’s wake — there is a huge incentive for them to play ball with aggregators such as FlexAI.
“If I can make it work for customers and bring tens to hundreds of customers onto their infrastructure, they [Intel and AMD] will be very happy,” Tripathi said.
This sits in contrast to similar GPU cloud players in the space such as the well-funded CoreWeave and Lambda Labs, which are focused squarely on Nvidia hardware.
“I want to get AI compute to the point where the current general purpose cloud computing is,” Tripathi noted. “You can’t do multicloud on AI. You have to select specific hardware, number of GPUs, infrastructure, connectivity, and then maintain it yourself. Today, that’s that’s the only way to actually get AI compute.”
When asked who the exact launch partners are, Tripathi said that he was unable to name all of them due to a lack of “formal commitments” from some of them.
“Intel is a strong partner, they are definitely providing infrastructure, and AMD is a partner that’s providing infrastructure,” he said. “But there is a second layer of partnerships that are happening with Nvidia and a couple of other silicon companies that we are not yet ready to share, but they are all in the mix and MOUs [memorandums of understanding] are being signed right now.”
Tripathi is more than equipped to deal with the challenges ahead, having worked in some of the world’s largest tech companies.
“I know enough about GPUs; I used to build GPUs,” Tripathi said of his seven-year stint at Nvidia, ending in 2007 when he jumped ship for Apple as it was launching the first iPhone. “At Apple, I became focused on solving real customer problems. I was there when Apple started building their first SoCs [system on chips] for phones.”
Tripathi also spent two years at Tesla from 2016 to 2018 as hardware engineering lead, where he ended up working directly under Elon Musk for his last six months after two people above him abruptly left the company.
“At Tesla, the thing that I learned and I’m taking into my startup is that there are no constraints other than science and physics,” he said. “How things are done today is not how it should be or needs to be done. You should go after what the right thing to do is from first principles, and to do that, remove every black box.”
Tripathi was involved in Tesla’s transition to making its own chips, a move that has since been emulated by GM and Hyundai, among other automakers.
“One of the first things I did at Tesla was to figure out how many microcontrollers there are in a car, and to do that, we literally had to sort through a bunch of those big black boxes with metal shielding and casing around it, to find these really tiny small microcontrollers in there,” Tripathi said. “And we ended up putting that on a table, laid it out and said, ‘Elon, there are 50 microcontrollers in a car. And we pay sometimes 1,000 times margins on them because they are shielded and protected in a big metal casing.’ And he’s like, ‘let’s go make our own.’ And we did that.”
Looking further into the future, FlexAI has aspirations to build out its own infrastructure, too, including data centers. This, Tripathi said, will be funded by debt financing, building on a recent trend that has seen rivals in the space including CoreWeave and Lambda Labs use Nvidia chips as collateral to secure loans — rather than giving more equity away.
“Bankers now know how to use GPUs as collaterals,” Tripathi said. “Why give away equity? Until we become a real compute provider, our company’s value is not enough to get us the hundreds of millions of dollars needed to invest in building data centres. If we did only equity, we disappear when the money is gone. But if we actually bank it on GPUs as collateral, they can take the GPUs away and put it in some other data center.”
Paris-based startup Pigment has raised a $145 million funding round just five years after its inception. The enterprise software company offers a business planning platform for large companies to visualize their past financial performance and forecast upcoming quarters.
This funding round comes as a bit of a surprise as large rounds have been few and far between in France. According to a recent study from EY, funding rounds in the French tech ecosystem were down 38% in 2023 compared to 2022.
But if you remove buzzy AI startups like Mistral AI and capital-intensive infrastructure plays that are not really tech startups, like EV charging networks (Driveco) and EV battery factories (Verkor), funding rounds are drastically down. Pure software startups have had a rough couple of years.
Pigment appears as an exception with its Series D. Existing investor Iconiq Growth is doubling down by leading this new funding round. Sandberg Bernthal Venture Partners, IVP, Meritech, Greenoaks and Felix Capital are also participating — many of them were existing investors too.
And there’s a reason why Pigment managed to raise so much at a significantly higher valuation less than a year after its previous funding round. In 2023, the startup managed to triple its revenue and double its customer base with well-known clients like Unilever, Datadog, Kayak and Merck. Half of Pigment’s clients are based in the U.S.
“Our current investors told us ‘if you’re going to raise money in 18 months to scale with others, we might as well offer you great terms right now for an internal round.’ And everything happened very quickly … In one week, it was a done deal,” co-founder and co-CEO Eléonore Crespo told me.
Before Pigment, Crespo worked for VC firm Index Ventures and Google. She co-founded Pigment with Romain Niccoli, who was the co-founder and CTO of adtech startup Criteo — an early success of the French tech ecosystem.
“IVP — one of our backers — benchmarks the growth rate of all SaaS companies. And since we’ve been selling our product, we’ve been in the top 5% of SaaS companies with the best growth rate ever, in terms of revenue growth,” Crespo said.
Image Credits: Pigment
Pigment is a flexible business planning tool that is used by chief financial officers and finance teams to create reports and budgets. It’s a modern SaaS platform, meaning that you can integrate it with all your company’s data (ERP, HRIS, data lakes, etc.) and use it as a collaboration tool.
In addition to finance teams, sales teams can use Pigment to create quotas and see how everyone is performing against quarterly quotas. HR teams can see how they should scale the workforce up and down based on strategic changes and financial objectives.
“We’ve done a lot of work to address other teams, not just finance teams. We’ve developed a lot of modules that enable us to serve HR teams, supply chain teams and sales teams,” Crespo said.
In fact, as more teams start using Pigment, it becomes an important tool for cross-team collaboration. And it’s supposed to work better than legacy tools from Oracle and SAP.
Like many software companies, Pigment has also added AI features. As Pigment acts as the central repository for all the important metrics of a company, customers can ask questions to Pigment AI in natural language to get a quick answer. Examples include “Can you give me a breakdown of revenue per country?” or “Why was our actual revenue lower than our forecast last quarter for this product?”
But more importantly, the company has optimized its core product so that it works well even with large datasets and complicated calculations. The best enterprise software products are must-have products, which means that companies usually don’t need to spend a lot of resources on improving the product — clients need this tool to operate. Pigment is still the challenger in this industry, so it believes it needs to provide a better product to compete with other business planning products.
As more funding flows into deep tech to address difficult global problems like climate change, PhD entrepreneurs coming out of Europe’s top universities and labs are increasingly turning their research into companies.
French spinout Diamfab, founded in 2019, is one example. Its co-founders, CEO Gauthier Chicot and CTO Khaled Driche, both PhDs in nanoelectronics and recognized researchers in the field of semiconducting diamond, left Institut Néel, a laboratory of the French National Center for Scientific Research (CNRS), with two licensed patents under their belt.
Since then, Chicot and Driche have registered more patents and brought on a third co-founder, Ivan Llaurado, as their chief revenue officer and partnership director. They also raised an €8.7 million round of funding from Asterion Ventures, Bpifrance’s French Tech Seed fund, Kreaxi, Better Angle, Hello Tomorrow and Grenoble Alpes Métropole.
This interest comes because the paradigm around semiconducting diamonds has changed in the last two years. “Diamonds are no longer a laboratory subject: They have become an industrial reality, with startups, with manufacturers interested in this field and with the partners we have around us,” Chicot told TechCrunch.
Silicon is still the most widely used semiconductor material in electronics because it’s ubiquitous and cheap. But there’s hope other options could someday outperform it, and not just in labs. Tesla’s decision to use silicon carbide instead of silicon was an important step in that direction, and diamond could be next.
Because diamond is naturally more resistant to high temperatures and more energy-efficient, Diamfab envisions a future in which a given component will need a much smaller surface of synthetic diamond than of silicon carbide, which will make it competitive on price.
The firm’s long-term goal is to make more efficient semiconductors with a lower carbon footprint, while also supporting what Chicot refers to as “the electrification of society,” starting with transportation.
Diamond-based electronics open the door to applications in the field of power electronics — think of smaller batteries and chargers with more autonomy, because less temperature control is required, which is particularly relevant for the automotive sector and electric mobility. But diamond wafers could also be leveraged for nuclear batteries, space tech and quantum computing, too.
The case for diamond as a better alternative to silicon doesn’t come out of nowhere; Diamfab is building on the Institut Néel’s 30 years of R&D into synthetic diamond growth. Its founders wanted to take this technology out of the lab. “We wanted to be useful pioneers,” Chicot said.
Being awarded the Jury’s Grand Prize of i-Lab in 2019 was a turning point for the firm. Co-organized by French institutions, it brought grants and a sense of validation that helped the team inwards and outwards.
With this seal of approval, “banks trust you even if you don’t generate any sales,” Chicot said. “It was a real plus in the beginning to get this award. And it was partly because we have great technology, and partly because it’s technology that’s crucial for the world.”
French public sector investment bank Bpifrance, one of the organizers of the i-Lab awards, is doubling down on Diamfab with funding from the French Tech Seed fund, which Bpifrance manages on behalf of the French government as part of the France 2030 plan.
When silicon has become a commodity, Diamfab’s high-value-added diamond wafers could be made in Europe and sold at a premium warranted by their higher efficiency, which also ties into the green transition. Decarbonization is one key goal of France 2030, and diamonds could help.
Their carbon footprint would be lighter because of the smaller surface that diamond requires compared to silicon carbide, but also because Diamfab synthetizes its diamonds from methane. In the future, this source could be biomethane, giving a commercial outlet to this byproduct of recycling.
Image Credits: Diamfab
Most of this, however, is still in the future. Diamfab is not decades away from its goals, but says it will need five years for its technology to be able to support the mass production of diamond wafers that fit industry requirements. This means taking its know-how in growing and doping diamond layers on one-inch wafers, and applying it to the four-inch wafers that silicon carbide already works on. Even with enough funding to support a small pilot production line, this will take a few years.
This five-year horizon made Diamfab a no-go for some VCs; while these may be sympathetic to the idea of reindustrializing Europe with cutting-edge innovation, their liquidity cycles make these types of investments more difficult. But Chicot ultimately managed to round up the €8.7 million that will help the startup go through its pre-industrialization phase.
The group of investors that have rallied around Diamfab is “balanced,” Chicot said, including public players, evergreen fund Asterion Ventures, and supporters of Diamfab’s region, Auvergne-Rhône-Alpes, and its city of Grenoble.
While there’s warranted hype around AI in Paris, Grenoble may be the closest to a French Silicon Valley. In no small part thanks to Nobel Prize-winning physicist Louis Néel, the Alpine city’s focus on electronics turned it into a deep tech hub that’s now also part of the conversation on both green tech and sovereign tech.
Grenoble startups that pop to mind include Verkor, which secured more than €2 billion for its gigafactory in Northern France, and Renaissance Fusion, which raised $16.4 million last year to build nuclear fusion technology in Europe. But Diamfab may benefit more from its partnerships with larger players with local ties, including CEA, Schneider Electric, Soitec and STMicroelectronics.
There’s no doubt that more semiconductors will come out of the French Alps. As both the EU and the U.S. adopted Chip Acts to reduce their dependency in Asia, France is set to provide €2.9 billion in aid for the upcoming joint factory of STMicroelectronics and GlobalFoundries, and Soitec recently opened a fourth factory nearby. Now Diamfab hopes it can play a part, too, and unleash the full potential of diamond in semiconductors.
Founded in 2017, Atoms Lanka Solutions is a provider of IT consulting and software development services. Throughout the company’s history, we have worked with over 160 customers from 5+ countries..
