The AI Alliance’s Rapid First-Year Growth Through Collaboration

The AI Alliance was founded in December 2023 by IBM and Meta, along with 50 other initial members. Over a year’s time, its membership has grown quickly, so that now it has more than 140 worldwide members and allows companies, non-profits and academic institutions of all sizes to collaborate on building a robust and open AI ecosystem. In the bigger picture, the AI Alliance has become an important force in the democratization of AI, which is why it’s worth reviewing what it has accomplished in its first year-plus of existence.

Before we get into those specifics, it’s important to understand why the organization was founded in the first place. For most of AI’s history, open source development was a fragmented effort that led to underperforming models. Prior to 2023, there were few nonprofit institutions capable of training AI models with even GPT-2 capabilities. At that time, large tech companies dominated proprietary AI, and open source AI was mainly confined to niche applications.

Everything changed in 2023 when multiple new base models with permissive licenses were released. Then by mid-2023, Meta released its open source Llama 2 model in partnership with Microsoft. Within six months, it was used to create more than 10,000 derivative models. A major new phase of open source AI development was underway.

In this context, the AI Alliance established an impressive list of goals right from its inception. These goals included fostering open collaboration, establishing governance and guardrails for AI and developing benchmarking tools and clear policy positions. Additionally, the alliance prioritized extensive educational initiatives and nurturing robust hardware ecosystems. The AI Alliance’s strength is further demonstrated by the quality of its steering committee, which has a roster of well-known commercial organizations and universities.

(Note: Moor Insights & Strategy has client relationships with about a dozen of the AI Alliance’s 140-plus members.)

Membership And Its Standards

To join the AI Alliance, an organization must meet four important standards. First, the potential member must be aligned with the mission to cultivate safety, open science and innovation. Second, members must be committed to work on significant projects aligned with the Alliance’s mission. Third, the potential member must be willing to contribute to the diversity of perspectives and cultures that exist within its global membership of 140-plus organizations. In the future, it is expected that the membership will grow even larger and even more diverse. The final expectation for membership is reputation; the AI Alliance seeks members with a recognized reputation as an educator, builder or advocate within the AI open source community.

Alliance members generally fall into one of those three categories. Builders are responsible for models, datasets, tools and applications that use AI. Enablers evangelize the adoption of open AI technologies using tutorials, use cases and general community support. Advocates emphasize the benefits of the AI Alliance ecosystem, plus foster public trust and safety among organizational leaders, societal stakeholders and regulatory bodies.

AI Alliance Focus Areas

The AI Alliance defines its long range priorities in terms of six focus areas. However, it doesn’t restrict its activities just to these areas. The alliance takes a holistic approach to the entire AI ecosystem by encouraging its community members and developers and allowing them to participate in one or more these areas, then switch if interests or priorities change.

Here are the six key focus areas of The AI Alliance:

• Skills and Education provide AI knowledge for both consumers and business leaders trying to evaluate the risks of using AI, as well as students and developers building AI applications. This area was designed to make it easier to locate expert guidance for a given area. It also contains a model evaluation initiative. Last year, the alliance created the Guide to Essential Competencies for AI. That guide was the result of an extensive survey done to understand roles needed in AI and what skills are needed in those roles. Even though the first version of the guide was published in mid-2024, it has already had nine revisions. Another follow-up survey is planned to clarify issues discovered in the first survey.
• Trust and Safety explores these important issues, which are necessary for all AI applications to be successful. Benchmarks, tools and methodologies are used to ensure that models and the applications that use them are high-quality, safe and trustworthy, including support for evolving standards of conduct and effective responses to risks. This work group gathers best-of-breed concepts about trust and safety, then points users to the expertise they need. The State of Open Source AI Trust and Safety — End of 2024 Edition survey published on the AI Alliance website covered both needs and successes associated with trust and safety. There are some research and environmental gaps that are being addressed through research and development efforts by many AI Alliance members.
• Applications and Tools explore tools and techniques for building efficient and robust AI-enabled applications. This group is also building an AI lab to enable experimentation and testing of AI applications and to accelerate innovation.
• Hardware Enablement is fostering a robust AI hardware accelerator ecosystem by ensuring that the AI software stack is hardware-agnostic. Technologies like MLIR and Triton are key software tools to ensure high-performance hardware portability. These tools allow organizations to take advantage of whatever hardware they prefer. Increased flexibility and performance reduces dependency on proprietary systems.
• Foundation Models and Datasets focus on models for underserved areas, including multilingual, multimodal, time series, science and other domains. For example, the science and domain-specific models target climate change, molecular discovery and the semiconductor industry. Effective models and AI application architectures require useful datasets with clear governance and rights for use. The Open Trusted Data Initiative is clarifying requirements for such datasets and building catalogs of compliant datasets. This effort should largely eliminate concerns about legal, copyright and privacy issues.
• Advocacy of regulatory policies is needed to create a healthy and open AI ecosystem. All AI policies and regulations should represent balanced rather than biased viewpoints.

Making AI Safer: New Tools, Tests And Transparency

Trust and Safety is an important and large field within the AI Alliance. It has many specialists working on tools to detect and reduce hate speech, bias and other harmful material. The Trust and Safety Evaluation Initiative is a major initiative for 2025 providing a unified view of the entire spectrum of evaluation, not just for safety, but also for performance and other areas where evaluating the effectiveness of AI models and applications is required. A sub-project is exploring specific safety priorities by domain for areas such as health, law and finance.

In mid-2025, the AI Alliance plans to publish a Hugging Face leaderboard that will allow developers to search for evaluations that best fit their needs, to compare how open models perform against those evaluations, and to download and deploy those evaluations to examine their own private models and AI applications. That initiative will also provide guidance on important safety and compliance aspects of use cases.

Not all invocations of AI models will use hosted commercial services. Some situations require air-gapped solutions. AI-enabled smart edge devices make it likely that more companies will begin deploying new small and powerful models on-premises, and sometimes without an internet connection. To support these use cases and facilitate large-scale model serving with flexible hardware configurations, the AI Alliance is developing hardware-agnostic software stacks.

Collaboration Creates AI Alliance Innovations

Two examples will illustrate how open collaboration between alliance members is providing significant benefits for everyone. The first example, SemiKong, was a collaborative effort among three alliance members. The members created an open-source large language model for the semiconductor manufacturing process domain. Manufacturers can use this model to accelerate the development of new devices and processes. SemiKong contains specialized knowledge about the physics and chemistry of semiconductor devices. In only six months, SemiKong captured the attention of the global semiconductor industry.

SemiKong was created by fine-tuning a Llama 3 base model using datasets curated by Tokyo Electron. The tuning created an industry-specific generative AI model that had more knowledge about semiconductor etching processes than the generic base model. A technical report on SemiKong is available here.

The second example is DANA — the Domain-Aware Neurosymbolic Agents project. It is a joint development of Aitomatic Inc. (based in Silicon Valley) and Fenrir Inc. (based in Japan). DANA is an early example of the now-popular agent architecture, where models are integrated with other tools to provide complementary capabilities. While models alone can provide amazing results, many studies have shown that LLMs often generate incorrect answers. A 2023 study cited in the SemiKong paper measures typical LLM errors of 50%, while DANA’s complementary use of reasoning and planning tools increased the accuracy to 90% for the target applications.

DANA uses neurosymbolic agents that combine the pattern recognition of neural networks with symbolic reasoning that supports rigorous logic and rules-based capabilities to solve problems. Logical reasoning combined with tools for planning (such as for designing assembly-line processes) produce accurate and reliable results that are essential for industrial quality control systems and automated planning and scheduling.

DANA can be used for multiple domains. For example, for financial forecasting and decision-making, DANA can understand market trends and make predictions based on complex theories, using both structured and unstructured data. That same ability can also be applied to retrieval and evaluation of medical literature and research information to ensure that existing diagnoses and treatments meet established medical protocols and practices. In short, DANA can enhance patient outcomes and reduce errors in critical patient applications.

Accomplishments In 2024 And Beyond

The AI Alliance began 2025 in a strong position with members in 23 countries and a number of working groups focused on major AI issues. The AI Alliance has over 1,200 working-group collaborators working on 90-plus active projects. Internationally, the AI Alliance has participated in events held in 10 countries involving more than 20,000 people, and it has published five how-to guides on important AI topics to help researchers and developers build and use AI.

The AI Alliance has published examples for using AI on models such as IBM’s Granite family and Meta’s Llama models. Its growing curation of “recipes” utilize the most popular open libraries and models for popular application patterns, including RAG, knowledge graphs, neurosymbolic systems and emerging agent planning and reasoning architectures.

In 2025, the AI Alliance is committed to scaling up its reach and impact tenfold. Two of its new major initiatives, discussed previously, are the Open Trusted Data Initiative and the Trust and Safety Evaluation Initiative. The AI Alliance also plans to create an industry-standard community lab for developing and testing AI application technologies. Its domain-specific model initiatives will also evolve. For example, the new Climate and Sustainability Working Group has plans to develop multimodal foundation models and open source software tooling to meet major challenges in climate change and its mitigation.

By 2030, it is estimated that AI will help grow the global economy by $20 trillion. By then, it is forecasted that 70% of industrial AI applications will be run on open source AI. It is also expected that the shortage of AI professionals will become even more pronounced than it is today. AI Alliance members may be able to reduce that problem by collaborating with other members to gain access to diverse expertise and resource sharing.

The AI Alliance is following a similar growth trajectory followed by other successful open-source organizations, such as the Linux Foundation, the Apache Software Foundation and the Open Source Initiative. These include:

• Comprehensive AI education and skills programs
• Global advocacy for responsible AI
• Creating tools to ensure AI safety and trustworthiness, as well as ease of development and use
• Collaborative research with academic institutions

I believe the AI Alliance will continue to attract developers, researchers, business and government leaders as contributors and collaborators. The AI Alliance’s leadership has established scaling of global collaboration as its overarching mission for 2025. Everything considered, the AI Alliance has the foundation to grow into a dominant global force that shapes, improves and innovates the future of artificial intelligence.

Moor Insights & Strategy provides or has provided paid services to technology companies, like all tech industry research and analyst firms. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking and video and speaking sponsorships. Of the companies mentioned in this article, Moor Insights & Strategy currently has (or has had) a paid business relationship with IBM and Meta.