Executive Summary
Building on our 2025 Year in Review report, which highlighted the rapid advancements and challenges in the AI landscape, Stepmark Partners presents our forward-looking predictions for 2026. These insights draw from emerging trends in technology, regulation, and market dynamics, offering a glimpse into potential developments in the AI industry. While these predictions may not all materialize, they serve as a fun exercise to benchmark progress over the next 12 months and guide discussions among investors and the general public.
Without further ado, below are our 10 AI industry predictions in no particular order:
1/ The Innovator’s Dilemma in Agentic AI
Startups are likely to lead in showcasing truly agentic abilities, such as Moltbot (recently renamed to comply with feedback from Anthropic), delivering practical automation in real-world scenarios. Large enterprises, hesitant to cannibalize existing per-seat pricing models that could reduce customer staffing needs, will lag in internal innovation. Instead, mergers and acquisitions will become the primary path for adoption once startups prove viable, while enterprise marketing of “AI agents” is expected to outpace actual autonomous execution in 2026.
2/ The Dam Breaks: A Flood of Ads in AI Chatbots
Recent announcements from OpenAI regarding advertising integration mark a turning point, paving the way for other chatbots with massive user bases to follow suit after years of reluctance. This shift will unleash a surge of marketer interest, redirecting spending from traditional media and igniting a resurgence in direct-to-consumer advertising reminiscent of the boom driven by brands like Harry’s, Away, and Casper. In 2026, the AI advertising market is poised to heat up significantly as sponsored content becomes integrated into conversational experiences.
3/ Model Convergence and Product-Led Differentiation
Consumer-facing AI applications now deliver highly similar performance on everyday tasks, as most user queries involve simple needs like cooking recipes or shopping recommendations rather than complex mathematical or scientific problems. Incremental gains in raw model intelligence thus provide limited differentiation for typical consumers. Competitive edges will increasingly come from hardware form factors, hyper-personalization (such as proactively suggesting recipes or products aligned with individual tastes), seamless integrations, and ecosystem advantages.
4/ VITO Becomes the Primary Communication Method
Voice Input, Text Output (VITO) will emerge as the dominant mode for human-AI interactions, capitalizing on the efficiency of speaking over typing and reading over listening. Users will increasingly prefer this hybrid approach for its speed and comprehension benefits in daily tasks. By 2026, VITO is anticipated to streamline communication across devices, enhancing productivity and accessibility in consumer applications.
5/ Major AI Announcements in Consumer Hardware
Building on early successes like Meta’s Ray-Ban glasses, 2026 will see a wave of AI integrations in everyday consumer hardware, including televisions, toys, and vehicles. These announcements will focus on embedding intelligent features that make devices more interactive and intuitive. This trend will expand AI’s reach into homes and daily life, fostering new opportunities for seamless, hands-free experiences.
6/ Evolution from Social Graph to Interest Graph to Trust Graph
With AI-generated content becoming ubiquitous, social media platforms will face user fatigue from an influx of low-quality “AI slop,” leading to a significant decline in engagement and interest. This shift will propel the rise of a “Trust Graph,” a concept coined by Om Malik, where platforms prioritize verified authenticity and connections based on trust to restore user confidence. In 2026, this evolution could redefine social interactions, favoring genuine creators and combating the erosion of platform value.
7/ China’s Leadership in AI Monetization and Metrics
Chinese AI companies have outpaced their American counterparts in going public, attracting strong investor interest. As public entities, they will face scrutiny to deliver sustained growth through expanding user metrics or financial returns to support elevated valuations. Limited access to leading Western models like OpenAI and Gemini will further spur creative commercialization, positioning China to define new industry benchmarks such as revenue per token or revenue per watt.
8/ Sovereign AI: Domestic Infrastructure with Global Technology
Sovereign AI initiatives will accelerate as governments recognize the economic benefits of directing investments domestically rather than solely to U.S.-based providers. Leading labs will increasingly build local data centers—often in exchange for regional funding, as seen with OpenAI’s Middle East partnerships—boosting national GDP and technological self-reliance in a constructive cycle. This shift will solidify AI infrastructure as an essential utility, with rising per-capita usage and enterprise adoption driving sustained compute demand across GPU architectures.
9/ Regulation and Fragmented AI Access
Europe and parts of Asia are expected to intensify AI regulation, potentially blocking products and fragmenting global access. For instance, Grok’s minimal guardrails when compared to other leading AI labs has enabled the generation of explicit images, drawing political scrutiny and highlighting how accessible advanced capabilities can prompt regulatory overcorrection focused on safety rather than innovation. This diverging landscape will require region-specific strategies and heavily influence which AI solutions achieve broad, international scale.
10/ To Boldly Go: AI Data Centers Enter Orbit
With SpaceX’s anticipated IPO on the horizon, it would be a highlight of the year if Starlink can launch a proof-of-concept mini data center into space to prove the naysayers wrong. This demonstration would explore the advantages of abundant solar energy and the naturally chilled environment of space for AI compute, particularly where latency is less critical for applications like chatbots. While experimental in nature, such a step could signal new possibilities for distributed AI infrastructure in 2026.
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