Tech
From high school science project to $18.3M: AI-accelerated enzymes are coming for fast fashion’s plastic waste
A U.K. startup, originating from founder Jacob Nathan’s high school science project on using enzymes to break down plastic waste, has secured an oversubscribed $18.3 million in Series A funding.
Founded in 2019 in London, Epoch Biodesign is now a 30+ strong multidisciplinary team of chemists, biologists and software engineers. The startup will use the new funding to scale up production of its plastic-eating enzymes. This means transferring the biorecycling process from the labs where the team has been developing the enzymes to their first production facility this year, which Nathan says will be able to gobble through 150 tonnes per year of waste once it’s up and running.
Thereafter, the first production runs of commercial-scale capacity are expected by 2028 if not sooner, as Nathan says the startup is looking for ways to accelerate the scaling. They’ll be roughly doubling the size of the team over the next 12 months as they work on switching to a higher gear, he tells TechCrunch.
Plastic not-so-fantastic
Stepping back for a second, the world’s plastic waste problem is staggeringly vast, with some 400 million tonnes of the stuff produced annually, according to the UN. Only a tiny fraction of which gets recycled currently being as, in crude cost terms, it’s far cheaper to pump out more virgin plastic than deal with processing the stuff we’ve already produced.
At the same time, the environmental and health costs of unchecked plastic pollution are stark. So there is growing pressure on regulators to act on plastic pollution and on businesses that use plastic in their products to clean up their act.
There are also a growing number of startups working on technologies targeting plastic waste from various angles — including startups applying AI to speed up sorting plastics for recycling and others developing non-fossil fuel-based plastic alternatives. But biorecycling, so leaning on biological entities to help break down resistant waste, is where Epoch Biodesign hopes to make its mark on plastics.
The biotech is developing a library of plastic-eating enzymes with the goal of disrupting the plastic pollution cycle by powering up biorecycling-based circularity — starting with a handful of plastics that are used in common synthetic fabrics. The first materials they’ve developed enzymes to tackle are polyester and two types of nylon (nylon 6 and nylon 66).
A graphical animation of the process on its website depicts waste garments going in at one end, being industrially sorted and/or pre-treated, depolymerized, purified and repolymerized, and then ready-to-use nylon (extrusion) or polyester (pellets) coming out the other end.
GenAI to the rescue?
While some plastic-eating enzymes have been discovered existing in nature, the catch is they are very slow at digesting this stuff — far too slow to help humanity escape its plastic waste mountain on any useful timescale. It’s also the case that we have produced far more types of plastics than enzymes have been found in the wild that can break them down, as yet. And as the plastic keeps piling up, the need for speed increases.
Epoch wants to lend a helping hand to evolutionary ingenuity by using technology tools to accelerate the discovery of biological catalysts that can tackle plastic waste fast. And key to unlocking this mission are developments in generative AI — specifically the rise of powerful large language models (LLMs) — that are helping accelerate the search for biological agents that can be precision targeted at this problem.
“The challenge with biology is that it’s just too complicated,” explains Nathan. “Humans don’t understand how it works. We’ll never be able to rationalize it. Most of these biological questions that we have remain unanswered. So the big shift here has been our ability to understand large, complex data-sets — which is effectively AI.”
“We’re just sort of un-baking the cake and then putting things back together at the other end,” he also says of what this biorecycling process boils down to. He adds that it only takes a “matter of hours” to go from waste fabrics to reclaiming molecularly identical material (nylon or polyester) in a form that’s ready for reusing to make new clothes or other products.
He describes enzyme design as a “ridiculously large search problem” to tackle. But by turning to GenAI, the startup’s scientists have essentially been able to shortcut sifting through possible combinations of amino acid and proteins to land on potentially useful agents — fine-tuning LLMs with information on proteins and amino acids but also feeding in “proprietary data” from its own lab work on plastic-eating enzymes.
“We’ve been able to generate tens of thousands of plastic-eating enzymes in our lab that are unique,” he says, explaining that after querying the AI models to yield promising candidates they switch to lab tests and then feed in more data from their results on the “predicted enzymes” to keep iterating the model until the search turns up “an enzyme that performs in the way that we want.”
“What we’re effectively doing is we’re concentrating hundreds of millions of years, billions of years of evolution into a few cycles in the lab that happen over the course of days, weeks, months,” he adds. “We’re making big evolutionary jumps that would be very unlikely to happen just naturally based on random mutations, natural selection.”
Epoch’s AI-driven enzyme design search has also enabled it to “pretty regularly” get speed improvements on enzymes in the region of 25x, according to Nathan.
“That means we can use less enzyme in our process,” he notes. “We can make less of it. The [capital expenditure] associated with manufacturing that enzyme in the first place goes down. And ultimately, all of that translates into a lower cost of goods for output.”
“We’re not the only company out there which is trying to design biology to do different things … but we really think we’re quite unique in the approach we’re taking in applying these tool sets to recycling — and then to our flavor of recycling: biorecycling,” he adds.
Focus on cost and commercial scale
So far, the startup has built three “best-in-class processes to recycle three very chemically distinct types of plastics” — and scaling those to commercially useful volumes is next on the slate with the new Series A cash.
“We’re building our first production facility in the U.K. this year for our first nylon process,” he says, claiming: “These technologies use entirely new biochemistries. They completely shift the cost bases of recycling into new areas that basically makes recycling the cheaper option compared to virgin.”
A key part of why Epoch is able to drive down recycling costs is the fact its process doesn’t require high temperatures to run — saving on energy costs compared to other forms of recycling which require the waste to be heated and/or melted. Nathan also points out that this means a lower capex for this (lower power) recycling facility — shrinking overall project costs.
The biological recycling process is also “incredibly high yield” compared to industrial recycling — he says they’re getting upwards of 90%, meaning most of the waste that’s fed in is coming back out the other end in a reusable state.
Plus, there’s no “unwanted side products” from biorecycling — which, again, reduces the cost and complexity of recycling the plastic.
“All of these things add up, basically, to reduce cost across the board of the process and get us into a position where — at that commercial scale — we’re reaching cost competitiveness with the materials that are on the market today made from fossil carbon,” he suggests.
Production of the enzyme itself involves a microorganism that’s been genetically engineered to include the DNA for making the enzyme and housed in a fermenter so it can replicate and churn out lots of the plastic digesting stuff — a synthetic biology technique that’s used for many other types of applications, from producing chemicals to novel foods.
Epoch’s approach to recycling plastic could have some additional benefits as Nathan suggests it could incorporate additional purification — by having the enzymes also “scrub” undesirable chemicals — since some plastics contain chemicals that can cause concerns for recycling the material.
Although he concedes that even biorecycling of plastics won’t fix the problem of microplastics where tiny pieces of plastic can wash out of garments that are made from synthetic fabrics and find their way into the environment — posing a hazard to biological life.
Still, he argues we’re going to be stuck needing to use synthetic plastic for decades, adding: “I think it’s really important that that new synthetic plastic is made from old materials, not from newly extracted fossil carbon.”
Designing enzymes to digest other types of plastic waste — such as packaging — is a wider goal for the startup. Although Nathan says they are focused on fabrics first as it’s a huge problem that’s also been getting more public attention. The business case also looks cleaner.
Notably, the startup’s Series A includes a strategic investment by Spanish fast fashion giant Inditex, owner of clothing brand Zara, which has inked a multi-year “joint development agreement” with Epoch — clearly with an eye on improving the sustainability of its business at a time of rising public awareness vis-a-vis the fashion industry’s role in the global plastic crisis.
“We want to produce material that’s actually useful,” notes Nathan. “We want to produce something for brands that is, you know, indistinguishable from the stuff that they’re using today — so in order for that to be true, we need to go through various tests. We need to do this at larger and larger and larger scale. And so having, effectively, the machinery of a business like Inditex with the scale that they have just helps us accelerate that process.”
The Series A round is led by the climate-focused fund Extantia Capital, with Day One Ventures, Happiness Capital, Kibo Invest, Lowercarbon Capital and others also participating alongside Inditex, and a $1M grant from the U.K. government. Epoch Biodesign’s total capital raised to date is now $34 million, including the latest raise.
Tech
Volkswagen’s cheapest EV ever is the first to use Rivian software
Volkswagen’s ultra-cheap EV called the ID EVERY1 — a small four-door hatchback revealed Wednesday — will be the first to roll out with software and architecture from Rivian, according to a source familiar with the new model.
The EV is expected to go into production in 2027 with a starting price of 20,000 euros ($21,500). A second EV called the ID.2all, which will be priced in the 25,000 euro price category, will be available in 2026. Both vehicles are part of the automaker’s new of category electric urban front-wheel drive cars that are being developing under the so-called “Brand Group Core” that makes up the volume brands in the VW Group. And both vehicles are for the European market.
The EVERY1 will be the first to ship with Rivian’s vehicle architecture and software as part of a $5.8 billion joint venture struck last year between the German automaker and U.S. EV maker. The ID.2all is based on the E3 1.1 architecture and software developed by VW’s software unit Cariad.
VW didn’t name Rivian in its reveal Wednesday, although there were numerous nods to next-generation software. Kai Grünitz, member of the Volkswagen Brand Board of Management responsible for Technical Development, noted it would be the first model in the entire VW Group to use a “fundamentally new, particularly powerful software architecture.”
“This means the future entry-level Volkswagen can be equipped with new functions throughout its entire life cycle,” he said. “Even after purchase of a new car, the small Volkswagen can still be individually adapted to customer needs.”
Sources who didn’t want to be named because they were not authorized to speak publicly, confirmed to TechCrunch that Rivian’s software will be in the ID EVERY1 EV. TechCrunch has reached out to Rivian and VW and will update the article if the companies respond.
The new joint venture provides Rivian with a needed influx of cash and the opportunity to diversify its business. Meanwhile, VW Group gains a next-generation electrical architecture and software for EVs that will help it better compete. Both companies have said that the joint venture, called Rivian and Volkswagen Group Technologies, will reduce development costs and help scale new technologies more quickly.
The joint venture is a 50-50 partnership with co-CEOs. Rivian’s head of software, Wassym Bensaid, and Volkswagen Group’s chief technical engineer, Carsten Helbing, will lead the joint venture. The team will be based initially in Palo Alto, California. Three other sites are in development in North America and Europe, the companies have previously said.
“The ID. EVERY1 represents the last piece of the puzzle on our way to the widest model selection in the volume segment,” Thomas Schäfer, CEO of the Volkswagen Passenger Cars brand and Head of the Brand Group Core, said in a statement. “We will then offer every customer the right car with the right drive system–including affordable all-electric entry-level mobility. Our goal is to be the world’s technologically leading high-volume manufacturer by 2030. And as a brand for everyone–just as you would expect from Volkswagen.”
The Volkswagen ID EVERY1 is just a concept for now — and with only a few details attached to the unveiling. The concept vehicle reaches a top speed of 130 km/h (80 miles per hour) and is powered by a newly developed electric drive motor with 70 kW, according to Volkswagen. The German automaker said the range on the EVERY1 will be at least 250 kilometers (150 miles). The vehicle is small but larger than VW’s former UP! vehicle. The company said it will have enough space for four people and a luggage compartment volume of 305 liters.
Tech
The hottest AI models, what they do, and how to use them
AI models are being cranked out at a dizzying pace, by everyone from Big Tech companies like Google to startups like OpenAI and Anthropic. Keeping track of the latest ones can be overwhelming.
Adding to the confusion is that AI models are often promoted based on industry benchmarks. But these technical metrics often reveal little about how real people and companies actually use them.
To cut through the noise, TechCrunch has compiled an overview of the most advanced AI models released since 2024, with details on how to use them and what they’re best for. We’ll keep this list updated with the latest launches, too.
There are literally over a million AI models out there: Hugging Face, for example, hosts over 1.4 million. So this list might miss some models that perform better, in one way or another.
AI models released in 2025
Cohere’s Aya Vision
Cohere released a multimodal model called Aya Vision that it claims is best in class at doing things like captioning images and answering questions about photos. It also excels in languages other than English, unlike other models, Cohere claims. It is available for free on WhatsApp.
OpenAI’s GPT 4.5 ‘Orion’
OpenAI calls Orion their largest model to date, touting its strong “world knowledge” and “emotional intelligence.” However, it underperforms on certain benchmarks compared to newer reasoning models. Orion is available to subscribers of OpenAI’s $200 a month plan.
Claude Sonnet 3.7
Anthropic says this is the industry’s first ‘hybrid’ reasoning model, because it can both fire off quick answers and really think things through when needed. It also gives users control over how long the model can think for, per Anthropic. Sonnet 3.7 is available to all Claude users, but heavier users will need a $20 a month Pro plan.
xAI’s Grok 3
Grok 3 is the latest flagship model from Elon Musk-founded startup xAI. It’s claimed to outperform other leading models on math, science, and coding. The model requires X Premium (which is $50 a month.) After one study found Grok 2 leaned left, Musk pledged to shift Grok more “politically neutral” but it’s not yet clear if that’s been achieved.
OpenAI o3-mini
This is OpenAI’s latest reasoning model and is optimized for STEM-related tasks like coding, math, and science. It’s not OpenAI’s most powerful model but because it’s smaller, the company says it’s significantly lower cost. It is available for free but requires a subscription for heavy users.
OpenAI Deep Research
OpenAI’s Deep Research is designed for doing in-depth research on a topic with clear citations. This service is only available with ChatGPT’s $200 per month Pro subscription. OpenAI recommends it for everything from science to shopping research, but beware that hallucinations remain a problem for AI.
Mistral Le Chat
Mistral has launched app versions of Le Chat, a multimodal AI personal assistant. Mistral claims Le Chat responds faster than any other chatbot. It also has a paid version with up-to-date journalism from the AFP. Tests from Le Monde found Le Chat’s performance impressive, although it made more errors than ChatGPT.
OpenAI Operator
OpenAI’s Operator is meant to be a personal intern that can do things independently, like help you buy groceries. It requires a $200 a month ChatGPT Pro subscription. AI agents hold a lot of promise, but they’re still experimental: a Washington Post reviewer says Operator decided on its own to order a dozen eggs for $31, paid with the reviewer’s credit card.
Google Gemini 2.0 Pro Experimental
Google Gemini’s much-awaited flagship model says it excels at coding and understanding general knowledge. It also has a super-long context window of 2 million tokens, helping users who need to quickly process massive chunks of text. The service requires (at minimum) a Google One AI Premium subscription of $19.99 a month.
AI models released in 2024
DeepSeek R1
This Chinese AI model took Silicon Valley by storm. DeepSeek’s R1 performs well on coding and math, while its open source nature means anyone can run it locally. Plus, it’s free. However, R1 integrates Chinese government censorship and faces rising bans for potentially sending user data back to China.
Gemini Deep Research
Deep Research summarizes Google’s search results in a simple and well-cited document. The service is helpful for students and anyone else who needs a quick research summary. However, its quality isn’t nearly as good as an actual peer-reviewed paper. Deep Research requires a $19.99 Google One AI Premium subscription.
Meta Llama 3.3 70B
This is the newest and most advanced version of Meta’s open source Llama AI models. Meta has touted this version as its cheapest and most efficient yet, especially for math, general knowledge, and instruction following. It is free and open source.
OpenAI Sora
Sora is a model that creates realistic videos based on text. While it can generate entire scenes rather than just clips, OpenAI admits that it often generates “unrealistic physics.” It’s currently only available on paid versions of ChatGPT, starting with Plus, which is $20 a month.
Alibaba Qwen QwQ-32B-Preview
This model is one of the few to rival OpenAI’s o1 on certain industry benchmarks, excelling in math and coding. Ironically for a “reasoning model,” it has “room for improvement in common sense reasoning,” Alibaba says. It also incorporates Chinese government censorship, TechCrunch testing shows. It’s free and open source.
Anthropic’s Computer Use
Claude’s Computer Use is meant to take control of your computer to complete tasks like coding or booking a plane ticket, making it a predecessor of OpenAI’s Operator. Computer use, however, remains in beta. Pricing is via API: $0.80 per million tokens of input and $4 per million tokens of output.
x.AI’s Grok 2
Elon Musk’s AI company, x.AI, has launched an enhanced version of its flagship Grok 2 chatbot it claims is “three times faster.” Free users are limited to 10 questions every two hours on Grok, while subscribers to X’s Premium and Premium+ plans enjoy higher usage limits. x.AI also launched an image generator, Aurora, that produces highly photorealistic images, including some graphic or violent content.
OpenAI o1
OpenAI’s o1 family is meant to produce better answers by “thinking” through responses through a hidden reasoning feature. The model excels at coding, math, and safety, OpenAI claims, but has issues deceiving humans, too. Using o1 requires subscribing to ChatGPT Plus, which is $20 a month.
Anthropic’s Claude Sonnet 3.5
Claude Sonnet 3.5 is a model Anthropic claims as being best in class. It’s become known for its coding capabilities and is considered a tech insider’s chatbot of choice. The model can be accessed for free on Claude although heavy users will need a $20 monthly Pro subscription. While it can understand images, it can’t generate them.
OpenAI GPT 4o-mini
OpenAI has touted GPT 4o-mini as its most affordable and fastest model yet thanks to its small size. It’s meant to enable a broad range of tasks like powering customer service chatbots. The model is available on ChatGPT’s free tier. It’s better suited for high-volume simple tasks compared to more complex ones.
Cohere Command R+
Cohere’s Command R+ model excels at complex Retrieval-Augmented Generation (or RAG) applications for enterprises. That means it can find and cite specific pieces of information really well. (The inventor of RAG actually works at Cohere.) Still, RAG doesn’t fully solve AI’s hallucination problem.
Tech
Not all cancer patients need chemo. Ataraxis AI raised $20M to fix that.
Artificial intelligence is a big trend in cancer care, and it’s mostly focused detecting cancer at the earliest possible stage. That makes a lot of sense, given that cancer is less deadly the earlier it’s detected.
But fewer are asking another fundamental question: if someone does have cancer, is an aggressive treatment like chemotherapy necessary? That’s the problem Ataraxis AI is trying to solve.
The New York-based startup is focused on using AI to accurately predict not only if a patient has cancer, but also what their cancer outcome looks like in 5 to 10 years. If there’s only a small chance of the cancer coming back, chemo can be avoided altogether – saving a lot of money, while avoiding the treatment’s notorious side effects.
Ataraxis AI now plans to launch their first commercial test, for breast cancer, to U.S. oncologists in the coming months, its co-founder Jan Witowski tells TechCrunch. To bolster the launch and expand into other types of cancer, the startup has raised a $20.4 million Series A, it told TechCrunch exclusively.
The round was led by AIX Ventures with participation from Thiel Bio, Founders Fund, Floating Point, Bertelsmann, and existing investors Giant Ventures and Obvious Ventures. Ataraxis emerged from stealth last year with a $4 million seed round.
Ataraxis was co-founded by Witowski and Krzysztof Geras, an assistant professor at NYU’s medical school who focuses on AI.
Ataraxis’ tech is powered by an AI model that extracts information from high-resolution images of cancer cells. The model is trained on hundreds of millions of real images from thousands of patients, Witowski said. A recent study showed Ataraxis’ tech was 30% more accurate than the current standard of care for breast cancer, per Ataraxis.
Long term, Ataraxis has big ambitions. It wants its tests to impact at least half of new cancer cases by 2030. It also views itself as a frontier AI company that builds its own models, touting Meta’s chief AI scientist Yann LeCun as an AI advisor.
“I think at Ataraxis we are trying to build what is essentially an AI frontier lab, but for healthcare applications,” Witowski said. “Because so many of those problems require a very novel technology.”
The AI boom has led to a rush of fundraises for cancer care startups. Valar Labs raised $22 million to help patients figure out their treatment plan in May 2024, for example. There’s also a bevvy of AI-powered drug discovery firms in the cancer space, like Manas AI which raised $24.6 million in January 2025 and was co-founded by Reid Hoffman, the LinkedIn co-founder.
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