17 October, 2024
India's EtherealX puts $5M seed toward fully reusable launch vehicles
EtherealX, an Indian space startup, has raised $5 million in a seed funding round as it plans to develop fully reusable medium-lift launch vehicles, making satellite launches cost-effective and time-efficient.
Space launches have multiplied in recent years. However, despite the number of players, launches still involve substantial transportation costs and considerable waiting periods. For instance, SpaceX launches are booked until 2026, even while on a ride-sharing model.
EtherealX aims to solve this problem with a new fully reusable medium-lift vehicle, offering absolute reusability to help reduce transportation costs and cut launch timeframes. Unlike SpaceX’s Falcon 9, which provides partial reusability by bringing its booster back to Earth after successful launches, EtherealX is designing its vehicles to get both the upper stage and booster back.
Founded in 2022 by Manu J. Nair (CEO), along with former Indian Space Research Organisation (ISRO) scientist Shubhayu Sardar (COO) and aerospace engineer Prashant Sharma (CTO), the Bengaluru-headquartered startup intends to take on SpaceX with its medium-lift vehicle named Razor Crest Mk-1. In a fully reusable configuration, the design is intended to put 8 tons into the lower Earth orbit. The vehicle could also put over 24.8 tons into the lower Earth orbit in an expendable and 22.8 tons in a partially reusable configuration. They claim it can also deliver payloads in geostationary transfer and trans-lunar injection orbits.
“When bringing back the upper stages, the reentry heat is so much that the refurbishment cost is almost always more than the fresh vehicle itself. So, we built from scratch a completely new rocket engine cycle, which, coupled with the deployment system, allows us to operate our engines efficiently in both vacuum and atmosphere,” said Nair in an interview.
The startup claims that it can operate between $350 and $2,000 per kilogram, a fraction of what launch vehicles, including Falcon 9, currently offer and 1/35th of the global average launch price.
“At the price point at which we will enter the market, we’ll comfortably capture 30-40% of it,” Nair told TechCrunch.
The two-year-old startup has acquired 16 acres of land in Tamil Nadu, where it is developing what it says is India’s largest privately developed rocket engine facility.
What’s the approach for complete reusability?
Unlike conventional methodologies for fending off the reentry heat, such as heat tiles and other thermal protection systems, Nair explained to TechCrunch that EtherealX’s proprietary rocket engine cycle works along with a deployment system to redirect the reentry heat throughout the reentry phase. This helps the system to operate efficiently in both vacuum and atmosphere and allows the startup to achieve pinpoint landing as opposed to the traditional ballistic reentry, which requires a much larger range of target landing zones.
“The closest model to our approach could be SpaceX’s Starship. They’re attempting to bring the upper stage back, but they’re doing it with the usage of heat shields or tiles,” the executive said. “We’re not fighting the reentry heat.”
The startup declined to provide too many details on the record so it can test in secrecy.
Currently, EtherealX does not produce the rocket engine in-house to avoid manufacturing-related capital expenditures and has partnered with a few companies to outsource its production. However, it does have plans to manufacture the rocket over time.
“During the rocket’s development, we need to observe the most effective way of manufacturing these components so that we can establish a proper manufacturing facility, which will be tested in-house,” said Nair.
EtherealX aims to test its development through a technology demonstrator vehicle (TDV), which it plans to launch in 2026. The company told TechCrunch that the startup plans for a full orbital launch to around 400 kilometers with its initial vehicle, using the same engine type as the full-scale vehicle but in a smaller count: four engines on the upper stage and one in the booster stage.
The TDV will be 35 meters tall and 2.5 meters wide with a 1.2-ton capacity.
The co-founder told TechCrunch that it is going with the TDV launch first to verify the telemetry and flight software for its commercial launch later.
Meanwhile, the startup has already identified 35 potential customers for its 2026 launch, though it plans to go ahead with 10–15 customers initially.
India’s space ecosystem has grown significantly. The South Asian nation is home to 229 space tech startups, per the Indian government data, and the ecosystem already has players building small satellite launch vehicles and solutions to offer space situational awareness and hyperspectral imagery.
The Indian government projects that the country will raise its share in the global space ecosystem fourfold by 2030. In the last few months, New Delhi introduced its space policy and updated rules to attract foreign investors and companies. The country also gained global attention for events, including its successful moon landing and partnership with NASA for joining Artemis Accords.
In 2023, space tech investments in India hit $126 million, up 7% from the $118 million raised in 2022 and a 235% increase from the $37.6 million in 2021, per Tracxn. The funding landscape has so far been driven by early-stage investments as the ecosystem is yet to be matured for commercial activities.
EtherealX’s seed funding round, led by Indian deep tech fund YourNest, also included BIG Capital, BlueHill Capital, Campus Fund, SGgrow and Golden Sparrow Ventures.
The startup plans to use its fresh funds to kick off engine test firing in the next six months and set the pitch for its TDV launch in a couple of years. It also aims to finish building its engine test facility and manufacturing the 40-kilonewton and 925-kilonewton engines.
BeyondMath's 'digital wind tunnel' puts a physics-based AI simulation to work on F1 cars
Simulating the real world is a tremendously complex problem if you want to do it at any useful level of fidelity. Traditional techniques are holding back design teams at vehicle and aerospace companies, but BeyondMath is putting AI on the task with a new way of simulating the world that could save them days or weeks of waiting.
“Unlike language, where we don’t have mathematical models to describe what the next word should be, when it comes to physics, we do have those models. And what we’re seeing is that machine learning is actually quite good at computation, not just pattern recognition,” said co-founder Darren Garvey.
The field in which BeyondMath is taking its first strides is called computational fluid dynamics (CFD), and it’s been around about as long as computing has. The equations that govern how an object moves through air or water, or air around an object, are fiendishly complex. So while we’ve continually improved our ability to predict, say, the way air flows over a wing, we’re still nowhere near perfect — and what we can do takes so much computational power that it’s limited to supercomputers and GPU clusters.
The result is that the design process in industries like cars, planes and boats involves a lot of wait time.
“For a designer, they put a lot of thought into what might work, then they run a simulation. Then they come in the next morning and they’ve got the results. Either it did what they wanted or not, and they have to go through this loop a few more times. Then you take it to the wind tunnel,” Garvey said — and the wind tunnel may well not agree with the simulation, so it’s back to the drawing board.
BeyondMath’s goal is to accelerate the digital design side, which means shortening the delay between having an idea and finding out whether it is likely to work.
“They’re saying, if I make this design change, will it make my car more fuel efficient? Imagine you’ve got six months to design a part for a plane. Given that a simulation takes so long, you might get 20 attempts to try things out. But if a designer thinks of an idea and gets results within seconds or a couple of minutes, in that same six months you might be able to run a million changes,” said Garvey.
And it’s increasingly looking like machine learning, as opposed to just more GPUs running the same old equations, is the way to do that. Their first product is a “digital wind tunnel” that provides near-real-time simulation of airflow over a complex surface at a fidelity that would normally take hundreds of times as long.
We’ve seen something like this in scientific literature, where a model of a weather system can be effectively approximated in a fraction of the time, using a machine learning model trained on thousands of hours of simulations and observed patterns. But BeyondMath doesn’t have the luxury of a pre-existing training set.
“There’s just not a lot of simulation data out there — we don’t have the whole internet to train off of, like the LLMs. So how do you get something that’s equivalent to what designers are using, that works on these very complex geometries, as a startup?”
Surprisingly, the answer they’ve found is not to rely on simulations, but rather to have a model that understands the theory behind something like a wind tunnel, as well as the observed reality of that theory.
“We’re not trying to approximate the simulations, we’re trying to approximate the real world,” Garvey said. “And you have to bring in real-world data to do that.”
Once the model understands how a system behaves, it can also be an active participant in design, a possibility many engineers have already begun to explore in other domains. Garvey compared it to image understanding: There, too, machine learning models had to walk before they could run, but once they were adept at analyzing an image, it was an intuitive next step for them to generate one.
Among BeyondMath’s first markets is Formula 1 racing, where some unnamed teams are exploring using the software to speed up their aerodynamics and vehicle design processes.
“They’re one of the heaviest users of CFD, and they’re fast-moving, they’ll adopt new technologies. We’ve been working closely with a couple F1 teams, doing a lot of evaluation and understanding their core problems. We’re close to having a platform that will actually make their cars faster,” Garvey said.
In fact, he expressed hope (with the usual warning that there was no guarantee) that within six months “we’ll be able to show that customers are benefiting from these models, and they’ve gone out of research and proofs of concepts into things that have real impact.”
New funding should help make that happen: BeyondMath just raised an $8.5 million seed round led by UP.Partners, with Insight Partners and InMotion Ventures participating.
The startup expects to double its team size and scale up its compute; they’re buying Nvidia DGX 200s and working with the chip giant on this interesting new application of its ubiquitous compute hardware.
Though the highly competitive, deep-pocketed F1 racing community is certainly a good customer to have, BeyondMath is thinking about its next steps.
“We’re seeing a lot of success in our customers’ design space, but it’ll be a journey from that to something more generalizable. For example, if a model understands cars, or car-like objects, it’s not necessarily going to understand a plane, or a blood vessel,” Garvey said. “But that’s the classic startup dance — you have to find your path to traction before you have the runway to expand. As a business we’re focused on these top-tier customers so they can help bootstrap the company.”
India's EtherealX puts $5M seed toward fully reusable launch vehicles
EtherealX, an Indian space startup, has raised $5 million in a seed funding round as it plans to develop fully reusable medium-lift launch vehicles, making satellite launches cost-effective and time-efficient.
Space launches have multiplied in recent years. However, despite the number of players, launches still involve substantial transportation costs and considerable waiting periods. For instance, SpaceX launches are booked until 2026, even while on a ride-sharing model.
EtherealX aims to solve this problem with a new fully reusable medium-lift vehicle, offering absolute reusability to help reduce transportation costs and cut launch timeframes. Unlike SpaceX’s Falcon 9, which provides partial reusability by bringing its booster back to Earth after successful launches, EtherealX is designing its vehicles to get both the upper stage and booster back.
Founded in 2022 by Manu J. Nair (CEO) along with former Indian Space Research Organisation (ISRO) scientist Shubhayu Sardar (COO) and aerospace engineer Prashant Sharma (CTO), the Bengaluru-headquartered startup intends to take on SpaceX with its medium-lift vehicle named Razor Crest Mk-1. In a fully reusable configuration, the design is intended to put 8 tons into the lower Earth orbit. The vehicle could also put over 24.8 tons into the lower Earth orbit in an expendable and 22.8 tons in a partially reusable configuration. They claim it can also deliver payloads in geostationary transfer and trans-lunar injection orbits.
“When bringing back the upper stages, the reentry heat is so much that the refurbishment cost is almost always more than the fresh vehicle itself. So, we built from scratch a completely new rocket engine cycle, which, coupled with the deployment system, allows us to operate our engines efficiently in both vacuum and atmosphere,” said Nair in an interview.
The startup claims that it can operate between $350–$2,000 per kilogram, a fraction of what launch vehicles including Falcon 9 currently offer and 1/35th of the global average launch price.
“At the price point at which we will enter the market, we’ll comfortably capture 30-40% of it,” Nair told TechCrunch.
The two-year-old startup has acquired 16 acres of land in Tamil Nadu, where it is developing what it says is India’s largest privately developed rocket engine facility.
What’s the approach for complete reusability?
Unlike conventional methodologies for fending off the reentry heat, such as heat tiles and other thermal protection systems, Nair explained to TechCrunch that EtherealX’s proprietary rocket engine cycle works along with a deployment system to redirect the reentry heat throughout the reentry phase. This helps the system to operate efficiently in both vacuum and atmosphere and allows the startup to achieve pin-point landing as opposed to the traditional ballistic reentry, which requires a much larger range of target landing zones.
“The closest model to our approach could be SpaceX’s Starship. They’re attempting to bring the upper stage back, but they’re doing it with the usage of heat shields or tiles,” the executive said. “We’re not fighting the reentry heat.”
The startup declined to provide too many details on the record so it can test in secrecy.
Currently, EtherealX does not produce the rocket engine in-house to avoid manufacturing-related capital expenditures and has partnered with a few companies to outsource its production. However, it does have plans to manufacture the rocket over time.
“During the rocket’s development, we need to observe the most effective way of manufacturing these components so that we can establish a proper manufacturing facility, which will be tested in-house,” said Nair.
EtherealX aims to test its development through a technology demonstrator vehicle (TDV), which it plans to launch in 2026. The company told TechCrunch that the startup plans for a full orbital launch to around 400 kilometers with its initial vehicle, using the same engine type as the full-scale vehicle but in a smaller count: four engines on the upper stage and one in the booster stage.
The TDV will be 35 meters tall and 2.5 meters wide with a 1.2-ton capacity.
The co-founder told TechCrunch that it is going with the TDV launch first to verify the telemetry and flight software for its commercial launch later.
Meanwhile, the startup has already identified 35 potential customers for its 2026 launch, though it plans to go ahead with 10–15 customers initially.
India’s space ecosystem has grown significantly. The South Asian nation is home to 229 space-tech startups, per the Indian government data, and the ecosystem already has players building small satellite launch vehicles and solutions to offer space situational awareness and hyperspectral imagery.
The Indian government projects that the country will raise its share in the global space ecosystem fourfold by 2030. In the last few months, New Delhi introduced its space policy and updated rules to attract foreign investors and companies. The country also gained global attention for events, including its successful moon landing and partnership with NASA for joining Artemis Accords.
In 2023, space-tech investments in India hit $126 million, up 7% from the $118 million raised in 2022 and a 235% increase from the $37.6 million in 2021, per Tracxn. The funding landscape has so far been driven by early-stage investments as the ecosystem is yet to be matured for commercial activities.
EtherealX’s seed funding round, led by Indian deep tech fund YourNest, also included BIG Capital, BlueHill Capital, Campus Fund, SGgrow and Golden Sparrow Ventures.
The startup plans to use its fresh funds to kick off engine test firing in the next six months and set the pitch for its TDV launch in a couple of years. It also aims to finish building its engine test facility and manufacturing the 40-kilonewton and 925-kilonewton engines.
China's $47B semiconductor fund puts chip sovereignty front and center
China has closed a third state-backed investment fund to bolster its semiconductor industry and reduce reliance on other nations, both for using and for manufacturing wafers — prioritizing what is called chip sovereignty.
China’s National Integrated Circuit Industry Investment Fund, also known simply as ‘the Big Fund,’ had two previous vintages: Big Fund I (2014 to 2019) and Big Fund II (2019 to 2024). The latter was significantly larger than the former, but Big Fund III is larger than both at 344 billion yuan, or about $47.5 billion, public filings revealed.
Exceeding expectations, and following Huawei’s recent increased reliance on Chinese suppliers, the size of Big Fund III confirms the country’s aim to achieve self-sufficiency in semiconductor production. It is also a reminder that the chip war between China and the West goes both ways.
The U.S. and Europe aren’t alone in wishing to reduce their dependence on their perennial tech rival. China, too, has reasons to worry about its supply, and it’s not just exports from the U.S. and its partners that are at risk.
When it comes to chip manufacturing, Taiwan is the chief concern. China seizing control of its production capabilities would put the U.S. and its allies at a massive disadvantage; Taiwan Semiconductor Manufacturing Co. (TSMC) currently makes around 90% of the world’s most advanced chips.
On the other hand, Bloomberg heard from sources that Netherlands-based ASML and TSMC have ways to disable chip-making machines in the event that China invades Taiwan.
As for China, it is producing some 60% of legacy chips — the type that are found in cars and appliances, U.S. Commerce Secretary Gina Raimondo recently declared.
The chip war extends to both legacy and advanced chips, with uneven results.
The Chinese official narrative is that U.S. policy is backfiring, with exports from leading U.S. chip players dropping, and others share that view.
Either way, this leaves a company like Nvidia walking a fine line “between maintaining the Chinese market and navigating U.S. tensions,” Hebe Chen, a market analyst at IG, recently told Reuters. The company tailored three chips for China after U.S. sanctions prevented it from exporting its most advanced semiconductors, but competition forced it to adopt a lower price than it might have wanted.
However, it could also be argued that the commercial struggles of Western chip players might be worth the cost if it can prevent China from developing and accessing more advanced chips as fast as its competitors.
How are global chipmakers preparing for the US-China chip war?
Signs indicate that restrictions could hit China where it hurts; for instance, if the country’s AI firms lose access to Nvidia’s cutting edge chips, or if it makes it harder for its champion, SMIC, to produce its own.
Big Fund III itself shows that China is feeling the heat. According to reports, the money will go towards large-scale wafer manufacturing like previous funds, but also to making High Bandwidth Memory chips. Known as HBM chips, these are used in AI, 5G, IoT and more.
Its size, though, is the biggest tell.
Backed by six major state-owned banks, Big Fund III is now larger than the $39 billion in direct incentives that the U.S. government will dedicate to chip manufacturing as part of the CHIPS Act. However, the whole federal funding envelope adds up to $280 billion.
At €43 billion, the EU Chips Act looks small in comparison to both, as does South Korea’s $19 billion support package, and the markets likely took notice.
The news of Big Fund III caused a rally around stock from Chinese semiconductor companies that stand to benefit from this new capital. However, Bloomberg noted that Beijing’s past investments haven’t always paid off.
In particular, “China’s top leadership was frustrated with a years-long failure to develop semiconductors that could replace U.S. circuitry. In addition, the former boss of the Big Fund was removed and investigated for corruption,” the media outlet pointed out.
Even without corruption, making major changes to semiconductor manufacturing is a slow process. In Europe and the U.S, too, this takes time, but there are interesting new developments.
French deep tech startup Diamfab, for instance, is working on diamond semiconductors that could support green transition, particularly in the automotive industry. That’s still a few years away, but it is the type of Western innovations that could be as interesting to track as whatever Chinese legacy players may do.
Additional reporting by Rita Liao.
Fireworks.ai open source API puts generative AI in reach of any developer
Just about everyone is trying to get a piece of the generative AI action these days. While the majority of the focus remains on the model vendors like OpenAI, Anthropic and Cohere, or the bigger companies like Microsoft, Meta, Google and Amazon, there are in fact, a lot of startups trying to attack the generative AI problem in a variety of ways.
Fireworks.ai is one such startup. While lacking the brand name recognition of some of these other players, it boasts the largest open source model API with over 12,000 users, per the company. That kind of open source traction tends to attract investor attention, and the company has raised $25 million so far.
Fireworks co-founder and CEO Lin Qiao points out that her company isn’t training foundation models from scratch, but rather helping fine tune other models to the particular needs of a business. “It can be either off the shelf, open source models or the models we tune or the models our customer can tune by themselves. All three varieties can be served through our inference engine API,” Qiao told TechCrunch.
Being an API, developers can plug it into their application, bring their model of choice trained on their data, and add generative AI capabilities like asking questions very quickly. Qiao says it’s fast, efficient and produces high-quality results.
Another advantage of Firework’s approach is that it allows companies to experiment with multiple models, something that’s important in a fast-changing market. “Our philosophy here is we want to empower users to iterate and experiment with multiple models and have effective tools to infuse their data into multiple models and test with a product,” she said.
Perhaps even more importantly, they keep costs down by limiting the model size to between 7 billion and 13 billion parameters, compared with over 1 trillion parameters in ChatGPT4. While that limits the universe of words the large language model can understand, it enables developers to focus on much smaller, focused data sets designed to work with more limited business use cases.
Qiao is uniquely qualified to build such a system having previously worked at Meta, leading the AI platform development team with a goal of building a fast, scalable development engine to power AI across all of Meta’s products and services. She was able to take this knowledge from working at Meta and create an API-based tool that puts that kind of power in reach of any company without requiring the level of engineering resources of a company the size of Meta.
The company raised $25 million in 2022 led by Benchmark, with participation from Sequoia Capital and unnamed angel investors.
Alchemist's latest batch puts AI to work as accelerator expands to Tokyo, Doha
Alchemist Accelerator has a new pile of AI-forward companies demoing their wares today, if you care to watch, and the program itself is making some international moves into Tokyo and Doha. Read on for our picks of the batch.
Chatting with Alchemist CEO and founder Ravi Belani ahead of demo day (today at 10:30 a.m. Pacific) about this cohort, it was clear that ambitions for AI startups have contracted, and that’s not a bad thing.
No early-stage startup today is at all likely to become the next OpenAI or Anthropic — their lead is too huge right now in the domain of foundational large language models.
“The cost of building a basic LLM is prohibitively high; you get into the hundreds of millions of dollars just to get it out. The question is, as a startup, how do you compete?” Belani said. “VCs don’t want wrappers around LLMs. We’re looking for companies where there’s a vertical play, where they own the end user and there’s a network effect and lock-in over time.”
That was also my read, as the companies selected for this group are all highly specific in their applications, using AI but solving for a specific problem in a specific domain.
An example of this is healthcare, where AI models for assisting diagnosis, planning care and so on are increasingly but still cautiously being tested out. The specter of liability and bias hang heavy over this heavily regulated industry, but there are also lots of legacy processes that could be replaced with real, tangible benefit.
Equality AI isn’t trying to revolutionize cancer care or anything — the goal is to ensure that the models being put to work don’t fall afoul of important non-discrimination protections in AI regulation. This is a serious risk, because if your care or diagnosis model were found to exhibit bias against a protected class (for instance assigning a higher risk to a Muslim or a queer person), that could sink the product and open you up to lawsuits.
Do you want to trust the model maker or vendor? Or do you want a disinterested (in its original sense, of having no conflicting interest) specialist who knows the ins and outs of the policies, and also how to evaluate a model properly?
“We all deserve the right to trust that the AI behind the medical curtain is safe and effective,” CEO and founder Maia Hightower told TechCrunch. “Healthcare leaders are struggling to keep up with the complex regulatory environment and rapidly changing AI technology. In the next couple of years, AI compliance and litigation risk will continue to grow, driving the widespread adoption of responsible AI practices in healthcare. The risk of non-compliance and penalties as stiff as loss of certification makes our solution very timely.”
It’s a similar story for Cerevox, which is working on eliminating hallucinations and other errors from today’s LLMs. But not just in a general sense: They work with companies to structure their data pipelines and structures so that these bad habits of AI models can be minimized and observed. It’s not about keeping ChatGPT from making up a physicist when you ask it about a non-existent discovery in the 1800s, it’s about preventing a risk evaluation engine from extrapolating from data in a column that should exist but doesn’t.
They’re working with fintech and insuretech companies first, which Belani acknowledged is “an unsexy use case, but it’s a path to build out a product.” A path with paying customers, which is, you know, how you start a business.
Quickr Bio is building on top of the new world of biotech being built on the back of Crispr-Cas9 gene editing, which brings with it new risks as well as new opportunities. How do you verify that the edits you’re making are the right ones? Being 99% sure isn’t enough (again, regulations and liability), but testing to raise your confidence can be time-consuming and expensive. Quickr claims its method of quantifying and understanding the actual modifications made (as opposed to theoretical — ideally these are identical) is up to 100 times faster than existing methods.
In other words, they’re not creating a new paradigm, just aiming to be the best solution for empowering the existing one. If they can show even a significant percentage of their claimed efficacy they could be a must-have in many labs.
You can check out the rest of the cohort here — you’ll see the above-mentioned are representative of the vibe. Demos commence at 10:30 a.m. Pacific.
As for the program itself, it’s getting some serious buy-in for programs in Tokyo and Doha.
“We think it’s an inflection point in Japan, it’s going to be an exciting place to source stories from and for companies to come to,” Belani said. A recent change to tax policy should free up early-stage capital at startups, and investment slipping out of China is landing in Japan, particularly Tokyo, where he expects a new (or rather refurbished) tech center to emerge. The fact that OpenAI is building out a satellite there is actually, he suggested, all you need to know.
Mitsubishi is investing through some arm or another, and the Japan External Trade Organization is involved as well. I’ll certainly be interested to see what the awakened Japanese startup economy produces.
Alchemist Doha is getting a $13 million commitment from the government, with an interesting twist.
“The mandate there is focusing on emerging market founders, the 90% of the world orphaned by where a lot of tech innovation is occurring,” Belani said. “We have found that some of the best companies in the U.S. are not from the U.S. There’s something about having an outside perspective that creates amazing companies. There’s also a lot of instability out there and this talent needs a home.”
He noted that they’ll be making bigger investments, from $200,000 to $1 million, out of this program, which may change the type of companies that take part.