What to Expect from Generative AI in 2024

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Representational image for Generative AI in 2024

A recent McKinsey study suggests that using generative AI (GenAI) could significantly boost global productivity, potentially adding trillions of dollars in value to the economy. They estimate an annual addition of $2.6 trillion to $4.4 trillion across 63 analyzed use cases. About 75% of this value falls into customer operations, marketing and sales, software engineering, and R&D. The report also notes that this estimate could double if we consider embedding generative AI into existing software for tasks beyond the analyzed use cases. 

Some organizations eagerly adopted this technology, while others took a more cautious “wait-and-see approach.” Regardless of the approach, enterprises can expect to witness various generative AI trends in 2024.

Emergence Of Multimodal Generative AI Models

In 2024, the world of large language models, like GPT4, Meta’s Llama 2, and Mistral, showcases exciting progress. These models don’t just stick to text – they embrace multimodal AI, enabling users to combine text, audio, images, and videos to create and prompt new content. This involves blending various data types, like images, text, and speech, using smart algorithms to predict and generate outcomes.

Looking Ahead

2024 promises a significant evolution in multimodal AI, reshaping generative AI capabilities. These models are moving beyond the usual single-mode functions, integrating different data types such as images, language, and audio. This shift to multimodal models is making AI more intuitive and dynamic.

ChatGPT Plus subscribers already appreciate the capabilities of GPT4-V, a popular choice for its multimodal features. In 2024, open models like LLaVA (Large Language and Vision Assistant) are anticipated to gain prominence, marking a continued trend toward more accessible and versatile AI.

Decision-tree chatbots will become a thing of the past

Currently, when you chat with customer service bots, they often ask you a bunch of similar multiple-choice questions. If your question doesn’t fit into those options, they tell you to call a customer representative. With the rise of advanced AI, these bots will soon get smarter.

They’ll consider important info like what you’ve bought before and your preferences. Then, they’ll give you personalized and accurate suggestions whenever you need them. This way, businesses can recreate the experience of talking to a salesperson in a store, but now it’s online and available 24/7. Brands using AI can now help customers with their questions, recommend products based on what they’ve bought in the past, and address support issues instantly.

Cloud Native Becomes Key To On-Prem Generative AI

Kubernetes is the go-to choice for hosting generative AI models. Big names like Hugging Face, OpenAI, and Google plan to use Kubernetes-powered cloud infrastructure for their generative AI platforms.

Tools like Hugging Face’s Text Generation Inference, AnyScale’s Ray Serve, and vLLM already support running model inference in containers. In 2024, we can expect frameworks, tools, and platforms on Kubernetes to reach maturity, handling the complete lifecycle of foundation models. This means users can efficiently pre-train, fine-tune, deploy, and scale generative models.

Major players in the cloud-native ecosystem will share reference architectures, best practices, and optimizations for running generative AI on cloud infrastructure. LLMOps will also be expanded to support integrated cloud-native workflows.

Expect a flood of new AI-powered tools

Increased investments in AI software-as-a-service (SaaS) this year mean we can expect lots of AI-powered products in 2024. This surge may make it tricky for businesses buying software to sift through the many automation tools available. The best products will be the ones that genuinely use AI to boost productivity. On the flip side, there will be many similar-looking applications using LLM APIs, competing based on cost and likely fading away over time.

Capable And Powerful Small Language Models

If 2023 was all about large language models, 2024 is set to highlight the effectiveness of small language models (SLMs). While large language models (LLMs) learn from huge datasets like Common Crawl and The Pile, which come from countless public websites, the data can be a bit noisy due to its general internet origins.

On the flip side, SLMs are trained on more focused datasets with high-quality content from sources like textbooks and journals. These models are smaller in terms of parameters, making them efficient on less powerful hardware. Despite their size, SLMs can generate content comparable to their larger counterparts.

Notable SLMs like Microsoft’s PHI-2 and Mistral 7B are poised to drive the next wave of generative AI applications.

Enterprises can fine-tune SLMs to suit specific tasks and domains, meeting legal and regulatory requirements, thus accelerating the adoption of language models.

Wrapping up

The potential economic impact of generative AI (GenAI) is substantial, with McKinsey estimating a significant boost to global productivity. The emergence of multimodal AI models in 2024, exemplified by GPT4 and LLaVA, signals a transformative shift towards more intuitive and dynamic generative AI capabilities. The evolution beyond decision-tree chatbots towards personalized AI-driven customer interactions is imminent.


Cloud-native solutions using Kubernetes are becoming integral to on-premises GenAI deployment and the rise of capable small language models promises efficiency without compromising quality. As 2024 unfolds, the AI landscape is poised for a flood of new tools, but only those genuinely enhancing productivity will endure.

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