As we enter 2025, the AI sector is at a critical turning point. While the industry continues to attract unprecedented levels of investment and attention, especially in the generated AI landscape, the basic market dynamics are almost basic market dynamics, which shows that we are changing dramatically towards the AI landscape for the coming year.
From my experiences that lead to AI startups and to observe the rapid growth of the industry, I believe this year will bring many fundamental changes: from large conceptual models (LCMS) that expect to be serious competitors to large language models (LLMS), specializing in The AI hardware, used to carry out large-scale AI infrastructure construction, will ultimately give them the ability to surpass startups like Openai and Anthropic and Anthropic, and they know that, and may even secure their AI monopoly.
Unique challenge for AI companies: Neither software nor hardware
The basic problem is that AI companies operate in a previously invisible middle position between traditional software and hardware businesses. Unlike pure software companies that invest primarily in human capital with relatively low operating expenses, or hardware companies invest in long-term capital with clear returns, AI companies face a unique combination of challenges that make their current funding model dangerous.
These companies require large up-term capital expenditures for GPU clusters and infrastructure, and only computing resources cost $10-200 million per year. However, unlike hardware companies, they cannot amortize these investments over a long period of time. Instead, they operated on a two-year cycle between funding rounds, each time requiring exponential growth and cutting-edge performance to justify their next valuation mark.
LLMS differentiation problem
Adding to this structural challenge is a trend of concern: the rapid fusion of large language model (LLM) capabilities. Like Unicorn Mistral AI and other companies, startups have shown that open source models can achieve comparable performance to their closed source counterparts, but technical differences that have previously proven to be highly valued are becoming increasingly difficult to maintain.
In other words, while every new LLM has impressive performance based on standard benchmarks, there is no real major shift in the underlying model architecture.
The current limitations of this field come from three key areas: Data Availabilitybecause we do not have high-quality training materials (recently confirmed by Elon Musk); Curation Methodsbecause they all adopted similar human feedback methods pioneered by Openai; and Computational architecturebecause they rely on the same limited pool of professional GPU hardware.
What is emerging is a model that is increasingly coming from efficiency rather than scale. The company focuses on compressing more knowledge into fewer tokens and developing better search systems for engineering artifacts such as graphic rags. Essentially, we are approaching a natural plateau where throwing more resources on the issue yields reduced returns.
Due to the unprecedented pace of innovation in the past two years, this convergence of LLM functions has occurred faster than anyone expected, bringing competition with time for companies raising funds.
According to the latest research trends, the next area to solve this problem is the emergence of large conceptual models (LCMs) As a new, groundbreaking architecture that competes with LLM in its core areas, it is Natural Language Understanding (NLP).
Technically, LCM will have several advantages, including better performance with fewer iterations and the ability to get similar results with smaller teams. I believe these next generation LCMs will be developed and commercialized by spin-off teams, the famous “pre-small technology” Mavericks founded a new startup to lead the revolution.
Monetization timeline mismatch
The compression of innovation cycles creates another key problem: the mismatch between market time and sustainable monetization. Although we see unprecedented speed in the verticalization of AI applications, for example, voice AI agents go from concept to revenue-generating products in just a few months, this rapid commercialization masks a deeper problem.
Consider this: $20 billion worth of AI startups today may need to generate about $1 billion in annual revenue in 4-5 years to prove publicly available in reasonable multiples. This requires not only technological excellence but also a huge shift in the entire business model, from R&D-centric to sales-oriented while maintaining innovation and managing huge infrastructure costs.
In this sense, new LCM-focused startups that will emerge in 2025 will be in a better position to raise funds, with lower initial valuations making them more attractive to investors’ funding goals .
Hardware shortages and emerging alternatives
Let’s take a closer look at the infrastructure. Today, every new GPU cluster buys it even before large players build it, forcing smaller players to either sign long-term contracts with cloud providers or turn them out altogether.
But it’s really interesting: While everyone is fighting for the GPU, the shift in the hardware landscape is still largely overlooked. The current GPU architecture called GPGPU (General Purpose GPU) is very inefficient for the actual needs of most companies in production. Just like running a calculator application with a supercomputer.
That’s why I believe professional AI hardware will be the next big shift in our industry. Companies like Groq and Cerebras are building reasoning-specific hardware that is 4-5 times cheaper than traditional GPUs. Yes, it is costly to optimize the models of these platforms, but the efficiency gains are obvious for companies running large-scale inference workloads.
Data density and small, smart models rise
Migrating to the next innovation frontier in AI may require not only greater computing power (especially for large models like LCM), but also richer and more comprehensive datasets.
Interestingly, smaller, more efficient models begin to challenge larger models by leveraging their available data intensive training. For example, models like Microsoft’s Feefree or Google’s Gema2b have much fewer parameters (usually around 2 to 3 billion), while ET’s performance levels can be comparable to large models with 8 billion parameters.
These smaller models are becoming increasingly competitive due to their high data density, and despite their high size, their data density is high. The transition to a compact and powerful model coincides with strategically advantaged companies like Microsoft and Google Hold: accessing large, diverse data sets through platforms such as Bing and Google Search.
This dynamic reveals two key “wars” in AI development: one over-calculates power and the other over-data. While computing resources are critical to pushing boundaries, data density is becoming equally (if not more) criticism. The unique position of a company with a large number of data sets is to train smaller models with unparalleled efficiency and robustness, cementing its advantages in the evolving AI landscape.
Who will win the AI war?
In this case, everyone likes to wonder who is the best in the current AI landscape to win. Here are some foods to think about.
Large tech companies have been pre-ordering the entire GPU cluster before construction, creating scarce environments for smaller players. Oracle’s over 100,000 GPU orders and similar actions from Meta and Microsoft illustrate this trend.
The companies have invested hundreds of millions of dollars in AI programs, requiring thousands of professional AI engineers and researchers. This creates an unprecedented demand for talent that can only be met through strategic acquisitions, which could lead to many startups being absorbed in the coming months.
While 2025 will be spent on large-scale R&D and the infrastructure of such actors, by 2026, due to unparalleled resources, they will have the ability to strike like they are now.
This is not to say that smaller AI companies are doomed to fail – so. The industry will continue to innovate and create value. Some key innovations in the industry, such as LCMS, may lead in anthropomorphic ways along with Meta, Google/Alphabet and Openai over the next year, all of which may be anthropomorphic, all of which are engaged in arbitrary and Excited project.
However, we are likely to see how AI companies fund and value basic restructuring. As venture capital becomes increasingly discriminatory, companies will need to demonstrate clear pathways to sustainable unit economics, which is a particular challenge for open source companies competing with resource-rich proprietary alternatives.
For open source AI companies, the path forward may require focusing on specific vertical applications where transparency and customization capabilities have obvious advantages over proprietary solutions.
