The New AI Landscape: 10 Predictions in 2024

January 18, 2024 • Written By The BentoML Team

Artificial intelligence (AI) was a headline-stealer in 2023, transforming from a specialized field to a ubiquitous force in an unprecedentedly short span of time. Now, as we step into 2024, the journey is poised to become even more exhilarating. In this blog post, let’s take a look at what we may be expecting in the year ahead.

1. The dawn of multimodal AI

2024 is shaping up to be a landmark year for multimodal AI models, which are capable of understanding, interpreting, and engaging with various forms of data like text, images, audio, and video. These models can provide more intuitive, context-aware, and personalized user experiences, heralding a new era of comprehensive and adaptive AI solutions.

The development of these multimodal models is, however, not without its challenges. It requires substantial expertise in different AI fields as well as computational resources. As a result, the development forefront of these models will be dominated by a handful of tech giants that have both the intellectual and financial capital to invest in such advanced research.

2. Open-source models overshadow closed-source models

2023 marked a turning point for open-source AI models, as the gap with their closed-source counterparts became closer and in some cases, they turned out to be a better choice due to privacy, cost-efficiency, and customization. This shift towards open-source models is a reflection of the growing emphasis on collaboration, transparency, and the democratization of AI technology. A prime example of this trend is Large Language Models (LLMs), where open-source models demonstrated performance and sophistication that could rival those developed in more proprietary settings.

As we advance into 2024, the impact of open-source AI models is expected to grow even further. This growth will be driven not only by their technical capabilities but also by a community-driven approach to AI development, where sharing knowledge and resources becomes the mainstream.

As an open-source project, BentoML will continue playing a part in this new era of AI. Its ability to seamlessly integrate and deploy AI models makes it an important tool for organizations looking to leverage the power of open-source AI and fulfill their potential in practical, real-word applications.

3. Democratization of GPU access in the cloud

The availability of GPUs in the cloud will cease to be a bottleneck for AI adoption in 2024. Nvidia may expand its cloud offerings, positioning it as a serious contender against major cloud service providers. This will make advanced computing resources more accessible for AI development.

This democratization is further accelerated by the increasing production of GPUs, leading to a more competitive market. Nvidia’s proactive approach in scaling up its GPU production is a key factor in this shift. However, it is not alone in this endeavor. Other major chipmakers like Intel and AMD are also ramping up their efforts, contributing to a more diversified and accessible GPU market.

4. The era of cost-efficient AI at scale

2024 is set to mark a major shift towards cost efficiency in AI, particularly as AI use cases become increasingly common in production. This transition is not just about scaling AI technologies but doing so in a manner that balances performance and cost. In this context, the focus intensifies on optimizing AI applications for maximum cost-effectiveness, a crucial consideration for businesses looking to leverage AI without incurring prohibitive costs.

This trend underscores the importance of efficient AI deployment solutions like BentoML and BentoCloud. Developers can use them to deploy AI applications as containers, offering scalability that aligns with usage demands. As a serverless solution, BentoCloud also reduces the need for extensive IT infrastructure and specialized personnel to manage AI deployments, allowing businesses to focus their resources on core operations.

5. Specialized AI for tailored applications

Another significant trend in the 2024 AI landscape will be the shift from broad, generalized models to more specialized, domain-specific ones. This evolution reflects an increasing recognition of the value that tailored AI solutions can bring to specific business sectors. Instead of one-size-fits-all models, we will see AI systems that are finely tuned to deliver highly relevant results in particular domains, be it finance, healthcare, retail, or any other industry. Specialized models will:

  • Enhance relevance and precision
  • Generate responses that are more culturally and politically appropriate
  • Be safer and more reliable
  • Spur the development and utilization of tools and datasets suited for training and serving these models.

6. LLM-based applications flourish across diverse domains

The impact of LLMs across diverse domains will become increasingly evident in 2024. Their ability to understand and generate human language in a contextually relevant manner is not just a technological feat; it’s a gateway to new forms of interaction, creativity, and efficiency for some industries. For example:

  • Gaming: LLMs are transforming the way narratives and dialogues are created. They enable dynamic storylines that respond to player choices in real time, offering a deeply personalized gaming experience.
  • Creativity and design: LLMs are becoming helpful assistants, aiding in everything from writing and content creation to graphic design and music composition.
  • Simulations and training: LLMs are being used to create realistic, interactive scenarios for educational purposes, professional training, and even in simulation of complex systems.

7. Running machine learning models on the edge

With the growth of IoT and the need for real-time data processing, edge computing has become increasingly important over the past few years. With the rise of AI, the feasibility of running ML models, like LLMs on edge devices, will see significant improvements, thanks to advancements in both hardware and software. This trend will enable more sophisticated AI applications in areas where immediate data processing is crucial. Also, tools for deploying, managing, and monitoring AI models on the edge will also see a boom in 2024.

8. Enhancements in AI explainability

Hallucination in AI, particularly in language models, has been a persistent issue, often leading to the generation of misleading or factually incorrect content. Tackling this problem involves developing methods that enable models to more accurately reference and validate information against their training material. In 2024, we expect to see significant progress in this area, with models being better equipped to indicate the source of their information and to distinguish between high and low-confidence responses. This advancement will lead to greater transparency and trust in AI decisions.

9. Thin wrapper dilemma: AI startups at a crossroads

Many AI startups that have built their applications primarily as thin wrappers around platforms like OpenAI will finding themselves in a challenging position in the coming year. These applications, though involve minimal unique coding or innovation, largely depend on the AI capabilities of established providers. They may face sustainability challenges in a rapidly evolving market and will be overshadowed by existing applications that directly incorporate simple AI features.

10. AI safety as a central theme

As AI becomes an ever more integral part of our daily lives, the focus on AI safety intensifies. Growing concerns center around the misuse of AI for malicious purposes, such as generating malicious code. These apprehensions have accelerated the development of various AI regulations.

In response to these concerns, we’re seeing a trend towards implementing models within specific network and geographic boundaries to bolster safety and privacy. In fact, this already gained some attention in 2023, notably with the introduction of the EU AI Act, the world’s first comprehensive legal framework for AI. In 2024, this trend is expected to not only continue but also expand, with more regions and organizations adopting similar frameworks.


These predictions paint a picture of an AI landscape that is more competitive, integrated, and complicated. The continuous evolution of AI technology promises not just to meet current expectations but to redefine them, opening new horizons for innovation and application. Let’s wait and see.