AI Cashmere Promises a New Kind of Luxury
AI cashmere is emerging as a surprising answer to a question many shoppers now ask: how can luxury fabrics stay affordable without harming the planet? In recent years, consumers have seen cashmere sweaters selling for prices that feel too good to be true. The reason is simple—traditional cashmere supply is limited, expensive, and increasingly strained. Everbloom, a venture-backed startup, believes artificial intelligence can help solve this problem. By using AI to recreate the feel and performance of cashmere from waste materials, the company is betting on a future where luxury no longer depends on scarce animal fibers. The idea sounds radical, but the technology behind it is grounded in real material science. For shoppers, brands, and sustainability advocates, AI cashmere could reshape how clothing is made and valued. It also signals a broader shift in how AI is quietly entering physical manufacturing, not just digital products.
Credit: EverbloomWhy Traditional Cashmere Is Under Pressure
Cashmere comes from the fine undercoat of specific goat breeds, and supply has always been limited. A single goat produces only a few ounces of usable fiber per year, even when sheared multiple times. As global demand has grown, producers have pushed animals harder to keep up, often at the expense of quality and animal welfare. Lower-priced cashmere on store shelves usually reflects this strain on the system. Fibers become shorter, rougher, and less durable over time. At the same time, overgrazing linked to cashmere production has contributed to land degradation in parts of Asia. These issues make it difficult for the industry to scale responsibly. As consumers grow more aware of sustainability, traditional cashmere’s image has started to crack. This pressure created the opening for alternatives like AI cashmere to gain attention.
Everbloom’s Bet on AI and Materials Science
Rather than trying to reform centuries-old herding practices, Everbloom chose a different path. The company set out to recreate the properties of cashmere without relying on goats at all. Led by co-founder and CEO Sim Gulati, the startup raised more than $8 million from investors including Hoxton Ventures and SOSV. Their approach blends artificial intelligence with material science, focusing on how fibers behave at a molecular level. Instead of copying cashmere exactly, Everbloom analyzes what makes it feel soft, warm, and lightweight. The goal is functional equivalence, not perfect imitation. This mindset allows the company to innovate faster than traditional textile development cycles. It also aligns with how AI excels—optimizing complex variables at scale. For investors, the pitch is compelling: luxury-quality fibers built from waste, powered by software.
Inside Braid.AI, the Engine Behind AI Cashmere
At the heart of Everbloom’s process is Braid.AI, a proprietary material science model. The system fine-tunes variables such as fiber length, thickness, and texture to achieve specific performance outcomes. Cashmere is one target, but the same platform can be adapted for wool-like or down-like materials. Braid.AI runs simulations that would take human researchers years to test physically. By narrowing down promising combinations digitally, the company reduces time, cost, and material waste. This approach mirrors how AI transformed drug discovery and semiconductor design. In textiles, however, it is still relatively new. The result is faster experimentation and more consistent results. Over time, this could allow brands to customize fibers for specific garments, climates, or price points. AI cashmere is just the first proof of concept.
Turning Waste Streams Into Valuable Fibers
Everbloom’s raw materials come from places the fashion industry usually ignores. Today, the company collects waste from cashmere and wool farms, textile mills, and down bedding suppliers. These materials often end up discarded despite containing usable proteins. In the future, Everbloom plans to expand into poultry feathers, a massive and underutilized waste stream. What connects all these sources is keratin, the protein that gives hair, wool, and feathers their structure. By focusing on keratin, Everbloom can standardize inputs that would otherwise be inconsistent. This waste-first strategy reduces reliance on virgin resources. It also gives the company access to abundant, low-cost feedstock. From an environmental perspective, it turns a disposal problem into an economic opportunity. For AI cashmere, waste is not a limitation—it is the foundation.
From Chopped Fibers to Spinnable Thread
Once collected, the waste materials go through a carefully controlled transformation. The fibers are chopped to uniform sizes and blended with proprietary compounds designed to stabilize and enhance performance. This mixture is then pushed through a plastic extrusion machine, forming small pellets. Those pellets are fed into spinning machines typically used to produce polyester fibers. This step is crucial because it allows Everbloom to fit into existing textile infrastructure. Most of the global textile market already relies on this equipment. By acting as a drop-in replacement, AI cashmere avoids costly factory retooling. This compatibility makes adoption easier for manufacturers. It also shortens the path from lab innovation to mass production. For the industry, that practicality may matter more than novelty.
Why Drop-In Compatibility Matters for Fashion Brands
Fashion brands are often cautious about new materials, even when sustainability benefits are clear. Supply chain disruption is expensive and risky. Everbloom’s strategy directly addresses this concern by designing fibers that work with current machines. Brands can integrate AI cashmere without rebuilding factories or retraining entire workforces. This lowers the barrier to experimentation and pilot programs. It also accelerates scaling if demand grows. From a business standpoint, this makes Everbloom more attractive than alternatives requiring bespoke processes. The company positions itself not as a disruptor that breaks systems, but as one that quietly upgrades them. That framing resonates with large apparel players. As margins tighten and sustainability reporting becomes stricter, practical solutions carry real weight.
The Sustainability Case for AI Cashmere
Beyond convenience, AI cashmere carries a strong environmental argument. Traditional cashmere production is resource-intensive and geographically constrained. In contrast, waste-derived keratin is widely available and underused. Using feathers and textile scraps reduces landfill pressure and associated emissions. It also decouples luxury fibers from animal population limits. While AI cashmere is not entirely impact-free, its footprint is more controllable and transparent. The process can be optimized over time as energy sources and chemistry improve. For regulators and ESG-focused investors, this predictability matters. Sustainability is no longer just a marketing claim; it is a measurable requirement. AI-driven materials offer a way to meet those expectations without sacrificing quality.
Consumer Perception and the Feel of Luxury
For AI cashmere to succeed, consumers must accept it as a genuine luxury material. Softness, warmth, and durability are non-negotiable expectations. Everbloom claims its fibers are nearly indistinguishable from traditional cashmere to the average wearer. If true, this could shift how people define authenticity in fashion. Younger consumers already show more openness to lab-grown and upcycled products. They often prioritize impact over origin stories. Clear labeling and storytelling will still matter, especially at higher price points. Brands adopting AI cashmere will need to communicate both performance and purpose. If done well, the material could become a selling point rather than a compromise. Luxury, in this context, becomes about innovation as much as heritage.
What AI Cashmere Signals for the Future of Textiles
Everbloom’s work points to a broader transformation in the textile industry. AI is moving beyond design and marketing into the physical makeup of fabrics themselves. As models like Braid.AI improve, materials could be engineered with unprecedented precision. This opens the door to clothes tailored for climate resilience, longer lifespans, or easier recycling. AI cashmere is an early example, but not the endpoint. The real shift lies in how software and biology intersect. If successful, this approach could reduce the industry’s dependence on fragile supply chains and scarce resources. For fashion, that change may be overdue. What once sounded futuristic is quickly becoming practical—and profitable.
