Over the last few years, artificial intelligence has moved from something abstract and experimental to something increasingly embedded in everyday life, for better and for worse. Organizations are no longer asking whether AI will affect their industry, but how it should be deployed responsibly, sustainably, and at scale. At the same time, it is no longer sufficient to apply AI indiscriminately to every possible problem. As adoption grows, society is becoming more discerning about the quality, usefulness, and trustworthiness of these systems.
Looking ahead to 2026, we see four trends that are likely to shape both public perception and real world adoption of AI.
1. Transparency is Becoming a Core Requirement
The conversation around AI has shifted from novelty to responsibility. As AI systems begin to influence financial decisions, public services, creative work, and even legal outcomes, the question of trust has become unavoidable.
Regulatory frameworks are starting to reflect this reality. In the EU, the Artificial Intelligence Act is setting clearer expectations around risk classification, accountability, and oversight. Rather than slowing innovation, these frameworks signal that AI is being treated as critical infrastructure, something that must operate predictably and transparently.
At the same time, organizations are beginning to ask harder questions internally. How do we audit AI outputs? How do we explain automated decisions to users, partners, or regulators? How do we ensure that innovation at scale does not come at the expense of responsibility? Looking ahead, transparency will become more than a nice to have feature. It will be a prerequisite for adoption in regulated and trust sensitive environments.
In anticipation of this shift, Chromia has been developing tools designed to make AI systems more transparent and easier to evaluate. The recently updated AI Inference Extension enables models to be hosted on-chain while generating immutable logs of inputs, outputs, and execution context, creating a verifiable record of how decisions are produced over time. Chromia has also introduced on-chain vector database capabilities, allowing applications to perform similarity search and retrieval directly within the network. Ongoing work is focused on lowering the barrier to entry for these systems, making it possible to deploy them without requiring deep familiarity with Rell or Chromia’s underlying technology stack.
2. Fighting Hallucinations
AI hallucinations have moved from abstract risk to headline news. From fabricated legal citations submitted in court filings, to false accusations made against public figures, to newspapers printing convincing but fake book lists, the consequences of unreliable AI outputs are now well documented.
An ‘AI hallucination’ occurs when a model generates information that appears coherent and confident but is factually incorrect, unsupported, or entirely fabricated. These outputs are not the result of deception, but of statistical pattern matching that fills gaps when the model lacks reliable grounding. What these cases share is not malicious intent, but misplaced trust. Systems designed to sound confident can easily be mistaken for systems designed to be correct.
Training models by indiscriminately scraping vast amounts of internet data may produce fluent systems, but it does not guarantee accuracy, relevance, or accountability. As AI becomes embedded in workflows that affect reputations, finances, and public information, tolerance for hallucinations will continue to decline. Organizations will increasingly prioritize verifiability, traceability, and data provenance over raw fluency. This is where architectural choices matter. AI systems that can reference structured data, log decision paths, and expose how conclusions were reached will be better positioned than opaque black boxes that cannot be audited after the fact.
We see this as another foundational challenge that will only grow in importance. Chromia is actively investigating this problem, including ways to extend its on-chain vector database capabilities to support on-chain embeddings with the goal of creating specialized AI systems that can be inspected, validated, and trusted over time.
3. Quality Over Quantity
The term "slop" has entered the mainstream, reflecting a growing fatigue with low effort, low quality, AI generated content. What began as a novelty has, in many cases, come to be seen as undesirable.
Consumers and creators are becoming more selective about what they engage with and what they produce. Audiences are quicker to filter out low effort content, while creators are placing greater value on originality, intent, and relevance rather than sheer output. There is a growing recognition that not everything is worth consuming, and that more content does not automatically lead to better results. As with any powerful tool, the difference lies in how it is applied. Thoughtful judgment, curation, and human oversight are increasingly what determine whether AI raises the bar or adds to the noise.
In 2026, we expect a continued shift toward intentional, well integrated AI systems that produce less noise and more signal..
4. Growing Diversity in LLM Models
For many people, GPT was the first model name they encountered, to the point where it has become shorthand for AI itself. While ChatGPT remains a widely used tool and a common reference point, it now exists alongside a growing set of capable alternatives.
Models such as Claude and Grok are increasingly referenced across media, developer communities, and public discourse, each emphasizing different strengths and design philosophies. In 2025, DeepSeek also drew significant attention after demonstrating strong performance at a fraction of the expected cost, triggering broader conversations about efficiency, open research, and competitive pressure that sent ripples through global markets.
This recent Wired commentary by Will Knight points to Qwen as another model to watch, highlighting how quickly advanced capabilities are spreading beyond a small group of established players. (As a side note, a version of Qwen was used in Chromia’s recent AI Inference demo).
This diversification matters. It reduces concentration risk, encourages specialization, and gives organizations greater flexibility to choose models that align with their values, requirements, and constraints. Rather than asking which model is best in absolute terms, teams are increasingly focused on which model is best suited to a specific task, dataset, or trust profile.
Where Chromia Comes In
Taken together, these trends signal a more grounded phase of AI adoption where transparency and quality matter as much as scale and speed. Meaningful progress may come not from doing more, faster, but from doing things deliberately, with clearer accountability and a sharper understanding of where AI adds value.
Chromia is contributing in the areas of transparency and trustworthiness by building tools that enable verifiable, auditable, and responsible applications. A recent example is the partnership between ChromaWay and AICrit, a project at the Faculty of Law at the University of Turku exploring repeatable workflows. Chromia provides the underlying infrastructure to support scalable, data-driven experimentation for this collaboration.
This is the kind of deliberate, accountable innovation that will define the next phase of adoption. One where discernment, rather than blind scale, shapes how systems are built, deployed, and trusted. We will continue to monitor these trends and emerging developments in the year ahead, while remaining committed to building and supporting high quality AI applications and infrastructure.
About Chromia
Chromia is a Layer-1 relational blockchain platform that uses a modular framework to empower users and developers with dedicated dapp chains, customizable fee structures, and enhanced digital assets. By fundamentally changing how information is structured on the blockchain, Chromia provides natively queryable data indexed in real-time, challenging the status quo to deliver innovations that will streamline the end-user experience and facilitate new Web3 business models.
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