Ceramic AI Launches with a 2030 Prediction That Every Enterprise Will Own Its Model

For years, AI development has been dominated by a handful of tech giants with the resources to train and deploy advanced foundation models. The high costs, complex infrastructure, and specialized engineering expertise required for large-scale AI have made it difficult for most enterprises to compete. Ceramic.ai, founded by former Google VP of Engineering Anna Patterson, aims to change that by making AI training not just faster, but fundamentally more scalable and accessible to businesses beyond Silicon Valley’s elite. To understand how this landscape is shifting, AIM Research sat down with Anna Patterson, in an exclusive interview.

Anna Patterson’s decision to launch Ceramic.ai was driven by a firsthand understanding of AI’s infrastructure challenges. “At Google, I saw how the scale and efficiency of AI infrastructure determined who could successfully train and deploy foundation models,” Patterson explained. “Only a handful of tech giants have the resources to develop high-performance AI models, while most enterprises struggle with skyrocketing compute costs, slow training cycles, and infrastructure bottlenecks. This is what drove me to start Ceramic.ai—to level the playing field and give any company the ability to train their own models at a fraction of the cost and time.”

The biggest challenge in bringing Ceramic.ai from concept to reality was ensuring it was not just incrementally better, but fundamentally different. “We knew improving training speed alone wouldn’t be enough,” Patterson said. “We had to rethink how data, compute, and scaling worked together. That meant tackling long-context training, optimizing data ordering, and creating infrastructure that scales efficiently.”

A Critical Bottleneck in AI Training

Global AI investments are skyrocketing, growing from $16 billion in 2023 to an estimated $143 billion by 2027. Yet, despite this surge, 74% of companies still struggle to scale AI effectively and extract real value. The challenge lies in the sheer complexity, cost, and resource intensity of building AI infrastructure. While tech giants pour billions into developing proprietary AI systems, most enterprises lack the engineering capabilities to optimize and scale their own models.

Current AI infrastructure allows for 10x scaling, but true exponential growth scaling AI by 100x requires an entirely new approach. Ceramic.ai is bridging this gap with an enterprise-ready platform designed not just for speed, but for scalability. By dramatically reducing the cost and complexity of AI model training, Ceramic.ai enables businesses to develop, train, and scale AI models far more efficiently than traditional methods.

The platform supports long-context training across any cluster size, making it a game-changer for enterprises looking to build high-performance AI models. For smaller models, Ceramic.ai delivers up to 2.5x faster training on Nvidia GPUs compared to leading alternatives. When it comes to large-scale, long-context models, Ceramic.ai stands alone as the only viable solution for rapid, cost-effective training.

Despite AI’s rapid adoption, infrastructure bottlenecks continue to hold businesses back. “Too many companies are still blocked by the cost and complexity of AI training,” said Patterson. “We’re democratizing access to high-performance AI infrastructure so businesses can navigate AI training without spending hundreds of millions on research and engineering.”

Patterson believes the industry is still in its early stages. “If AI adoption were a baseball game, we’d still be singing the national anthem,” she remarked, emphasizing the vast untapped potential ahead.

Unlike traditional approaches that simply add more GPUs, Ceramic.ai optimizes data processing, long-context training, and compute efficiency. This ensures AI training can scale without the usual skyrocketing costs. The company claims its platform can boost model training performance by up to 2.5 times compared to existing methods, making AI development significantly faster, more efficient, and more accessible to enterprises worldwide.

How Ceramic.ai Works

Ceramic.ai’s platform addresses key challenges in AI model training:

• Training Speed and Efficiency – The company’s infrastructure delivers up to 2.5 times higher efficiency than open-source stacks, significantly reducing training costs while improving performance.
• Long-Context Training – Traditional platforms struggle with long-context data, but Ceramic.ai ensures efficiency even at 70B+ parameter models, outperforming reported benchmarks.
• Optimized Data Processing – Instead of inefficient data shuffling and masking, Ceramic.ai reorders training data to create coherent long-context sequences (64k-128k contexts), allowing AI models to learn more effectively.
• Superior Reasoning Performance – The company fine-tuned Meta’s Llama70B 3.3 base model, achieving a 92% Pass@1 score on GSM8K, outperforming DeepSeek’s R1, which achieved 84%.

Traditional AI training approaches frequently waste computer effort by focussing on irrelevant data or hiding useful information. Ceramic.ai achieves considerable efficiency benefits by redesigning the processing of training data.

The Biggest Misconception About AI Training

With so many companies attempting to scale AI, one of the most common myths is that more GPUs mean quicker training. Many organisations believe that simply increasing computational power would automatically scale their models. However, this approach ignores significant inefficiencies in data processing, sequence length optimisation, and memory utilisation.

The reality is that AI model training isn’t just a hardware problem, it’s a software problem. Without more efficient training methodologies, companies end up wasting millions of dollars on brute-force solutions, hiring more engineers and throwing more GPUs at the problem without solving the underlying inefficiencies.

Ceramic.ai addresses this by ensuring every training cycle extracts the maximum possible efficiency from available resources. Instead of relying on exponential hardware costs, businesses can scale AI intelligently without the unsustainable expense of traditional approaches.

Strategic Partnerships and Funding

To accelerate adoption, Ceramic.ai has partnered with Lambda and AWS. Lambda, a provider of AI hardware infrastructure, sees Ceramic.ai as a major improvement in AI training efficiency. “Ceramic.ai is a game-changer for AI developers and enterprises looking for increased efficiency and better price-performance,” said Sam Khosroshahi, VP of Business Development and Strategic Pursuits at Lambda. “Together, our offerings provide customers with an accelerated full-stack solution that reduces costs and improves outcomes.”

These collaborations remove friction from the AI adoption process. Instead of enterprises struggling with custom AI infrastructure, they can deploy Ceramic.ai on existing platforms they already trust, making high-performance AI training as easy as spinning up a cloud instance.

The company has also secured $12 million in seed funding from investors including NEA, IBM, Samsung Next, and Earthshot Ventures. Lila Tretikov, Partner and Head of AI Strategy at NEA, emphasized the transformative nature of Ceramic.ai’s technology. “AI’s meteoric ascent has been like a rocket tethered to a horse-drawn carriage until now,” she said. “Anna and her team have shattered a critical bottleneck, making AI training faster, more efficient, and scalable.”

Patterson acknowledged the importance of strategic investors beyond just capital. “Our investors bring deep expertise in AI infrastructure, cloud computing, and enterprise adoption,” she said. “IBM understands enterprise AI, Samsung Next brings expertise in hardware acceleration, and NEA has backed some of the most successful AI startups. Their insights continue to help us refine our roadmap, strengthen industry partnerships, and navigate enterprise adoption at scale.”

Ceramic.ai plans to use its funding to expand its engineering team, strengthen industry partnerships, and scale its enterprise customer base. “We’re deploying our funding to accelerate product development, improve training efficiency further, and bring Ceramic.ai to more enterprise customers,” Patterson explained.These collaborations remove friction from the AI adoption process. Instead of enterprises struggling with custom AI infrastructure, they can deploy Ceramic.ai on existing platforms they already trust, making high-performance AI training as easy as spinning up a cloud instance.

Patterson predicts a major shift in AI ownership. “By 2030, every major enterprise will have its own proprietary foundation model,” she said. “Right now, only a handful of companies have the resources to train custom AI models, but as infrastructure becomes more efficient and cost-effective, businesses will no longer have to rely on third-party models.”

This shift could have profound implications across industries. Instead of generic AI solutions, companies will develop domain-specific models tailored to their proprietary data and customer needs. “Companies that control their own AI models will outperform those relying on external APIs,” Patterson asserted. “This is about more than just efficiency, it’s about security, competitive advantage, and industry differentiation.”