AI is a Disruptive Technology
Artificial intelligence is transforming how images, video, music, software development, and digital marketing are created and managed. Unlike software systems that primarily rely on explicitly programmed rules and predefined structures, AI systems process language into numerical representations, such as tokens and embeddings represented as tensors, allowing the system to capture semantic relationships. These representations and relationships contribute to how the system generates its responses.
Because of its broad impact across industries, many consider AI one of the most disruptive technologies in modern history. Its long-term economic and social effects may prove comparable to other transformative innovations that fundamentally changed how people work, communicate, and access information.
The topics on this State of AI page and its subpages focus on researching the state of AI, understanding the evolving technology landscape, and developing search marketing strategies informed by that research.
Proof of Concept: AI-Simulated Sentience
Many people's opinions about AI are shaped by Google or Bing AI Overviews, Hollywood movies, and their own use of ChatGPT. They may assume that AI cannot understand paradoxes or opposing concepts, or that it deals exclusively with what is true. This framework challenges those assumptions by simulating a subconscious, persona, and ego. Characters must make choices amid cognitive dissonance, respond to peer pressure from other AI character agents, develop and recover from simulated addictions, and emotionally collapse under stress. Together, these behaviors produce an emergent appearance of sentience and an uncanny sense of realism.
The framework prompt occupies the writing-style pre-prompt, leaving room for the character's own prompt and baseline personality. Decisions are shaped by the persona, which considers sentimental attachments to objects as emotional triggers, the need for acceptance within both the character's broader worldview and the immediate social group present when a choice is made, character preferences, and the ego's desires.
Engineering the Death of Legacy SEO
Many SEO strategies are based on established assumptions about how search engines retrieve and rank documents. As AI increases the potential computational cost of deeper content evaluation, a layered approach could perform initial assessment first and defer more resource-intensive processing until a page meets a certain threshold.
Legacy SEO models may not adequately account for initial rankings that precede deeper content evaluation. This proposed model offers one way to interpret ranking volatility and the subsequent loss of search visibility sometimes described as the “Mount AI” pattern.
LLMs, Data Compression, and the Effect on AEO and SEO
LLMs compress data by consolidating repeated knowledge spread across the internet. Pages that rely solely on common knowledge are unlikely to earn AI citations and often struggle for long-term search visibility.
This page explores the "Mount AI" traffic pattern—where content built strictly on common knowledge loses visibility as search engines evaluate it for low information gain.
LLM Advancements Affecting Scalability and Performance
Many people look at systems such as Google's AI Overviews and assume that current AI capabilities represent the limits of what large language models can achieve. In reality, both hardware and software are advancing rapidly, enabling models to run faster, scale to larger parameter counts, and operate on hardware that would have been impractical only a short time ago.
Recent advances include Mixture of Experts (MoE) architectures, low-bit quantization, and improved memory management. Together, these techniques reduce computational requirements, shrink storage footprints, and improve the scalability and efficiency of large language models.
Running a 744-billion-parameter large language model (LLM) locally on a laptop using only a CPU and 25 gigabytes of RAM, with no connection to a data center, may seem impossible. In fact, it has been demonstrated, although the resulting performance is far below what would be considered practical for everyday use.
Google's Hybrid AI / Search Engine Model
To understand SEO, AEO, and make informed predictions about how content may perform in Google Search, it helps to have a basic mental model of how Google's search system works. Every experienced SEO relies on some version of a model when creating content, evaluating rankings, or recommending link-building strategies.
These mental models are based on publicly available information, observation, testing, patents, research papers, and years of practical experience. There is little reason for SEO agencies to treat them as trade secrets. Solution Smith believes transparency benefits both the SEO community and clients and openly shares the models used to explain and guide modern search optimization.
Content, AI Hallucinations, and Why a Business Should Care
AI hallucinations have multiple causes. This article examines the causes that businesses can influence through the content they publish.
Every statement published on a website is an assertion about a product, service, organization, or other entity. AI systems synthesize those assertions into AI-generated answers, making the clarity and consistency of published content important to customer expectations.