This article is part of a VentureBeat special, “AI at Scale: From Vision to Viability.” Read more about this story here.

As we close out 2024, we can look back and admit that artificial intelligence has come a long way. For now, predicting what wonders 2025 has in store for AI is impossible. But several events provide a clear picture of what businesses can expect in the coming year and how they can prepare to make the most of it.

Decreased emotional value

Over the past year, the cost of coastal models has steadily declined. The price per million tokens of OpenAI’s best-in-class language (LLM) has dropped more than 200 times over the past two years.

One of the main factors driving the decline in the value of ideas is the growth of competition. For most businesses, multiple border types will be appropriate, making it easy to switch from one to the other, shifting competition to pricing. The evolution of accelerator chips is special reference tools it also makes it possible for AI labs to provide their samples at a lower cost.

To take advantage of this trend, businesses need to start experimenting with high-end LLMs and creating prototypes to use around the clock even if the cost is high. The continued drop in model prices means that many of these will soon be gone. At the same time, modeling skills continue to improve, which means you can do more with the same budget than you did last year.

The increase of the main models of reasoning

Release of OpenAI o1 has started a new wave in the LLM space. The practice of allowing the “models” to “think” for a long period of time and review their answers allows them to solve complex problems of conversation that were impossible with one-dimensional phones. Although OpenAI has not released much information about o1, its impressive capabilities have sparked new competition in the AI ​​space. It’s available now many open examples which takes o1’s thinking skills and expands the mind to new areas, such as accountability unanswered questions.

Advances in o1-like models, sometimes called large-scale reasoning models (LRMs), can have two implications for the future. First, considering the number of tokens that LRMs have to create their solutions, we can expect hardware companies to be encouraged to create them. special AI accelerators with higher tokens.

Second, LRMs can help address one of the most important issues of the next generation of languages: higher education. There are already reports that OpenAI is using o1 to genprovide examples of studies for his next generation of models. We can also expect LRMs to help create a new generation of specialized micro-models trained on data for special applications.

In order to take advantage of these developments, businesses need to set aside time and budget to test the use of frontier LRMs. They need to test the limits of the boundary models, and think about the kinds of applications that are possible if the next generation of models can overcome those limits. Coupled with the price drop, LRMs can open many new programs in the coming year.

Transformer changes are picking up steam

Memorizing and calculating the complexity of variables, the main learning constructs used in LLMs, has led to the field of some complex linear models. The most popular of these architectures, state space modeling (SSM), has seen a lot of progress in the past year. Some reliable examples include water neural network (LNNs), which use new mathematical equations to do more with a few artificial neurons and count cycles.

In the past year, researchers and AI labs have released white SSM versions also hybrids which combines power transformers and linear models. Although these models have not started to act on the damaged metal, they act quickly and already have the rules of growing quickly and efficiently. If the progress of the project continues, many simple LLM applications can be downloaded to these models and run on edge devices or local servers, where businesses can use the bespoke data without sending it to others.

Changes to scaling rules

The rules for LLMs are constantly changing. Release of GPT-3 in 2020 proved that sample size will continue to produce impressive results and enable models to perform tasks they were not properly trained for. In 2022, DeepMind released Chinchilla paperwhich sets a new standard for data upload rules. Chinchilla proved that by training a model on a large dataset that is many times larger than the number of its parts, you can continue to improve. This growth allowed smaller models to compete with limit models with hundreds of billions of units.

Today, there are fears that both laws are approaching their limits. Reports shows that frontier laboratories are returning to decline in teaching large species. At the same time, educational datasets have already reached trillions of symbols, and obtaining good data is difficult and expensive.

Meanwhile, LRMs promise a new vector: time extension. Where the quality and size of the data fail, we can adapt new methods by allowing the models to automatically add and correct their errors.

As we enter 2025, the landscape of AI continues to evolve in unexpected ways, with new architectures, reasoning skills, and economic models redefining what’s possible. For businesses looking to experiment and adapt, this represents not only a technological advance, but a fundamental shift in how we can use AI to solve real-world problems.

Daily thoughts on business use cases by VB Daily

If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with AI output, from regulatory frameworks to application environments, so you can share insights for greater ROI.

Read our Privacy Policy

Thank you for subscribing. See more VB articles here.

There was a problem.