### Challenges with Generative AI Infrastructure

Many generative AI (GenAI) end users are currently facing significant challenges when it comes to setting up large language models (LLMs). The complexity of infrastructure setup combined with the high costs of management can be quite daunting. As a potential solution, a growing number of users may turn towards adopting small language models (SMLs) as an alternative. These smaller models promise to ease the burden associated with deployment and maintenance, making AI more accessible.

The trends indicate that in the forthcoming year, a large proportion of organizations may indeed opt for SMLs. According to the latest InfoQ Trends report, companies such as Microsoft have unveiled models like Phi-3, which can be tested right away. This allows businesses to weigh the cost-effectiveness and advantages of integrating SMLs as opposed to traditional LLMs. The report highlights that these new language models are particularly well-suited for edge computing scenarios, enabling efficient operation on smaller devices.

InfoQ, a platform with a readership of approximately 1.5 million worldwide, serves as a resource mainly for software engineers and developers. Yet, its insights, including the Trends report, are designed to resonate with general technology enthusiasts, making complex topics more digestible for a broader audience.

### Anticipated Trends in AI Development

In addition to the shift towards SMLs, there are several significant trends expected to shape the AI landscape in the coming months, as identified by software architect Srini Penchikala and his colleagues at InfoQ. Below are five trends that industry professionals should watch closely.

#### 1. The Future of AI is Open and Accessible

The authors note that we are currently in a transformative era characterized by large language and foundation models. Although the majority of available models are closed-source, efforts by companies like Meta are underway to encourage a shift towards more open-source alternatives. This transition could enhance collaboration and innovation across the AI landscape.

> *“Even though most currently available models are closed source, companies are trying to shift the trend toward open-source models.”*

#### 2. Importance of Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) techniques are likely to gain traction as organizations seek to optimize their outputs without resorting to cloud-based LLMs. By integrating LLMs with external knowledge repositories, RAG presents a practical solution for companies aiming to maintain control over their data while leveraging the capabilities of AI.

> *“RAG will also be useful for applicable use cases of LLMs at scale.”*

#### 3. Focus on AI-Powered Hardware

AI-driven hardware will attract increased attention, particularly infrastructure that supports AI-enabled GPUs and advanced AI-powered personal computers. Such innovations are designed to utilize AI technology to enhance the efficiency and speed of various tasks. GPUs like Nvidia’s GeForce RTX and advanced devices like Apple’s M4 are expected to facilitate quicker model training and content generation.

> *“This is going to see significant development in the next 12 months.”*

#### 4. Adoption of AI Agents in Development

The forthcoming period is expected to see a rise in the adoption of AI-powered agents, particularly in corporate software development environments. Autonomous agents and GenAI-integrated virtual assistants are being deployed to enhance developer productivity. Examples include GitHub’s Copilot and Microsoft Teams’ Copilot, all of which facilitate improved collaboration among team members.

> *“AI-assisted programs can enable individual team members to increase productivity or collaborate with each other.”*

#### 5. Emphasis on AI Safety and Security

As the ecosystem of language models evolves, so too does the critical nature of AI safety and security within organizations. Training employees in best practices for data privacy and ensuring that secure pathways are also the most user-friendly will be paramount. Organizations are encouraged to embrace self-hosted models and open-source LLM solutions to bolster their security frameworks.

> *“Self-hosted models and open-source LLM solutions can help improve the AI security posture.”*

### How AI legalese decoder Can Help

Navigating these emerging trends in AI can be overwhelming, particularly when it comes to understanding the legal implications surrounding the use of LLMs and SMLs. That’s where tools like the AI legalese decoder come into play, helping organizations decode complex legal jargon associated with AI agreements, terms of service, and compliance regulations. By simplifying legal communication, this tool empowers businesses to make informed decisions and align their AI strategies with legal requirements more efficiently.

This article encompasses insights derived from a podcast produced by the InfoQ editorial team, offering both written and audio content for further exploration of these trends.