AI's Rapid Evolution: Multimodal, Bespoke Software, and Knowledge Work Transformation

The AI Daily Brief: Artificial Intelligence News and Analysis · December 29, 2025 · Listen to Original Episode →

Original Title: 50 AI Predictions for 2026 - Part 1

TL;DR

AI model capabilities will continue to advance, with improvements in speed and reliability driven by new hardware architectures like Nvidia's Blackwell chips, enabling models to surpass human capacity in many tasks.
The frequency and variety of AI model releases will increase, leading to a "double-edged sword" for users who gain constant access to new tools but face an unending cycle of testing and evaluation.
Multimodal AI capabilities, encompassing image and video generation, will become a significant competitive battleground among major AI labs, driving innovation beyond text-based models.
"Vibe coding" will bifurcate into distinct applications for engineers and non-developers, with non-technical enterprise departments increasingly using AI for production-level tasks like custom analyzers and onboarding apps.
The rise of bespoke personal software, built by individuals for specific needs, will challenge traditional template-based solutions and potentially spawn a new class of AI app entrepreneurs with different economic models.
Enterprises will shift from "doing" to "managing" knowledge work, leading to new roles like "forward deployed vibers" who bridge functional expertise with AI coding capabilities to drive adoption.
Companies will increasingly focus on measuring AI ROI with dashboards and quantitative metrics, driving investment in data and context engineering to unlock the full potential of AI agents.

Deep Dive

The AI landscape in 2026 will be characterized by incremental but significant advancements in model capabilities, a strategic shift in release strategies, and the expanding influence of "vibe coding" beyond engineering into broader enterprise functions. While foundational models will continue to improve at a steady pace, the emphasis will shift from raw power to productization, user experience, and the development of specialized interfaces for building and managing AI agents. This evolution will drive a fundamental reorganization of knowledge work, enabling individuals and organizations to move from doing tasks to managing and orchestrating AI-driven processes.

The trajectory of AI models will remain on a consistent improvement curve, with capabilities doubling roughly every four months, largely driven by hardware advancements like NVIDIA's Blackwell chips, rather than a sudden leap to recursively self-improving AI. This steady progress, however, will be accompanied by a more frequent and varied release cadence for models, a strategy adopted to mitigate the risk of building massive expectations around single releases, as seen with GPT-5. For users, this means a constant stream of new tools to evaluate, with model selection increasingly becoming a "vibe-based" decision, prioritizing stylistic fit and use-case suitability over marginal performance differences. The consequence of this saturation in core writing and thinking tasks will be a heightened focus on multimodal capabilities, with intense competition in areas like image and video generation, and productization efforts that prioritize user experience and intuitive interfaces for interacting with and building AI applications. We will likely see specialized interfaces emerge for agent building, moving beyond current drag-and-drop automation tools to more intuitive studio-like environments. Furthermore, the intense focus on coding capabilities will not only persist but radicalize, becoming a core competency for every major AI lab. Crucially, the value of last-mile end-user data will become a significant competitive differentiator, potentially allowing agent-focused labs to challenge established model providers. Memory in LLMs will also become a critical battleground, serving as both a major opportunity for enhanced user experience and a significant barrier to model switching, thereby influencing user lock-in strategies. Finally, the distinction between AI assistants and agents will blur, with agents increasingly proliferating through users delegating more complex tasks, leading to a rise in "agent managers" rather than fully autonomous agents in the enterprise.

"Vibe coding," the practice of using natural language to generate software, will expand dramatically beyond its engineering origins. In software engineering, the initial resistance to AI-assisted coding will give way to a focus on managing AI autonomy and reorganizing teams to leverage these new capabilities. Beyond engineering, "vibe coding" will move into production across non-technical enterprise functions, enabling the creation of custom tools for legal, HR, and marketing, often without direct engineering involvement beyond ensuring security and readiness. On the consumer side, this will fuel a rise in bespoke personal software, where individuals build tailored tools for specific needs that existing applications do not meet, potentially creating a new class of AI app entrepreneurs with different economic models. This trend will also challenge template-based website creation software, as users will prefer the flexibility of natural language control. For enterprises, 2026 will mark a significant "knowledge work vibefication," shifting from direct task execution to managing AI processes, a trend that will feel distinct even in large organizations. This will also lead to the creation of new roles, such as "forward-deployed vibers," who bridge functional expertise with AI coding proficiency. While large-scale replacements of enterprise software suites are unlikely, smaller and medium-sized businesses may increasingly build custom internal-facing software to meet specific needs, bypassing expensive, feature-heavy SaaS solutions. A major focus for enterprises will be the development of robust ROI dashboards and quantitative measurement of AI impact, moving away from qualitative assessments. Investment in "data and context engineering" will become crucial for unlocking the full value of AI and agents, as organizations realize the necessity of preparing their data infrastructure. Furthermore, the development of improved interfaces for agentic workflows will be key, as true value lies in process reinvention by agents, not merely replicating human processes. Ultimately, leading organizations will experience compounding AI benefits, widening the gap with laggards and opening new product and revenue opportunities.

Action Items

Build agent studio prototype: Design a simple interface for building agents, inspired by NotebookLM concepts, to enable mainstream adoption.
Audit enterprise AI adoption: For 5-10 organizations, measure the ROI and benchmarking focus to identify key impact metrics.
Track data and context engineering investment: For 3-5 enterprise teams, quantify investment in AI and agent infrastructure to support agent value.
Evaluate bespoke personal software market: Identify 3-5 use cases where custom-built software offers significant advantages over off-the-shelf solutions.
Analyze enterprise software replacement: For 3-5 small to medium-sized companies, assess the feasibility of building custom internal-facing software to replace existing enterprise solutions.

Key Quotes

"for a while we saw capabilities doubling every seven months and more recently it's jumped up to closer to four and a half months you can see here the difference between the seven month line and the four month line on both the 50 and the 80 reliability threshold now it is at least theoretically possible that we see recursively self improving ai but i think it's far more likely that the new nvidia architecture which is coming online in the form of blackwell chips and then eventually hopper chips keeps us on something like this trajectory even as we max out capabilities and move them beyond human capacity in a lot of different areas"

The speaker, in discussing AI model capabilities, notes a significant acceleration in the rate at which these capabilities are improving. This acceleration, from doubling every seven months to every four and a half months, is attributed to advancements like new NVIDIA chip architectures. The speaker suggests this trend will continue, pushing AI capabilities beyond human capacity in many areas.

"GPT 5 more than anything showed that there is just a ton of risk in building up big expectations around a single model release now yes of course Gemini 3 was kind of the opposite but the hit to OpenAI and more broadly the entire AI field that GPT 5 wrought probably could have been avoided by a different approach to release schedules"

The speaker highlights the risk associated with creating significant anticipation for a single AI model release, using GPT-5 as a prime example of a situation that negatively impacted OpenAI and the broader AI field. This suggests that a more staggered or frequent release strategy for AI models might mitigate such risks and manage expectations more effectively.

"what's more i think especially when it comes to writing type tasks or just generally being smart research etcetera model upgrades are going to be increasingly vibe based this is of course due to the fact that all of the premier models are really good right now when i'm deciding between Gemini 3 Opus 4 5 and GPT 5 2 for some writing or research use case it's largely going to be stylistic for me and use case by use case"

The speaker predicts that for tasks like writing and research, AI model upgrades will become increasingly "vibe based." This is because, according to the speaker, the leading models are already highly capable, making the choice between them often a matter of personal stylistic preference rather than functional superiority for specific use cases.

"I think that in 2026 specifically we are going to continue to get really cool demos and maybe some really early sandboxes but I don't think that we're going to have a generalist usability type of moment yet right now world models feel a little bit to me like the VR of the AI world where it's not hard to understand how powerful they could be in theory but because they represent some totally new capability set for experiences and are not just a one to one replacement for things we used to do there's just going to be a lot more time to shift that type of behavior"

The speaker forecasts that while "world models" in AI will continue to produce impressive demonstrations and early applications, widespread general usability is unlikely by 2026. The speaker likens this stage to early VR, where the potential is clear but the shift in user behavior and integration into daily activities will require more time due to their novel nature.

"first of all I think we're going to see a big bifurcation right now we use the same words to describe two totally different things vibe coding or AI and agentic coding within software engineering organizations and vibe coding among non developers these are wildly different things and I think that we'll stop treating them as the same thing"

The speaker predicts a significant split in how "vibe coding" is understood and applied. The speaker argues that the term currently encompasses distinct concepts: AI and agentic coding within software engineering, and vibe coding used by non-developers. The speaker believes these will be recognized as separate domains, leading to more specific terminology and approaches for each.

"I also think that there is going to be a ton of focus on data and context engineering I think investing in your AI and agent infrastructure is going to be sexy in the enterprise in 2026 companies are going to realize that to really get full value especially out of agents they're just going to have to take the time and make the investment to have their data available to work for those agents"

The speaker anticipates a strong emphasis on data and context engineering within enterprises in 2026, particularly for maximizing the value of AI agents. The speaker suggests that companies will recognize the necessity of investing in their AI and agent infrastructure to ensure data is readily accessible and usable by these agents to achieve full potential.

Resources

External Resources

Podcasts & Audio

The AI Daily Brief - Mentioned as a daily podcast and video about important news and discussions in AI.
KPMG 'You Can with AI' podcast - Mentioned as a new podcast offering insights for smarter enterprise decisions.

Tools & Software

Genspark - Used to help produce visuals for the episode and preferred for its performance.
Manas - Used to help produce visuals for the episode, noted for its "obvious nano banana pro sheen."
Zapier - Mentioned as an example of a drag-and-drop automation builder.
Lindi - Mentioned as an example of a drag-and-drop automation builder.
Replit - Mentioned as a tool that can be used to build bespoke personal software.
Shopify - Mentioned as a platform that interfaces with e-commerce and online business for small creators and builders.
Workday - Mentioned as an enterprise software that Clara scrapped.
Salesforce - Mentioned as an enterprise software that Clara scrapped and as a provider of enterprise software.
HubSpot - Mentioned as a long-tail software provider in the CRM space.

Websites & Online Resources

aadbintel.com - Mentioned for accessing information about the AI ROI benchmarking survey and the forthcoming AADB Intelligence Service.
aidailybrief.ai - Mentioned as a contact point for sponsoring the show.
blitzy.com - Mentioned as a website to visit to build enterprise software.
robotsandpencils.com - Mentioned as a website to partner with for intelligent cloud-native systems powered by AI.
besuper.ai - Mentioned as a website to request a company's agent readiness score.
pats.com - Mentioned as a website for sponsoring the show.
kpmg.us/AIpodcasts - Mentioned as a URL to listen to the KPMG 'You Can with AI' podcast.
kpmg.us/agents - Mentioned as a URL to discover how KPMG's journey can accelerate yours.
pats.com - Mentioned as a website for sponsoring the show.
pats.com - Mentioned as a website for sponsoring the show.
pats.com - Mentioned as a website for sponsoring the show.

Organizations & Institutions

KPMG - Mentioned as a sponsor that transforms AI potential into business value.
Blitzy.com - Mentioned as an enterprise autonomous software development platform.
Superintelligent - Mentioned as an AI planning platform launching a new assessment called Plateau Breaker.
Robots & Pencils - Mentioned as a company that partners with organizations on intelligent cloud-native systems powered by AI.
OpenAI - Mentioned in relation to GPT-5, GPT-5.1, GPT-5.2, and their image releases.
Anthropic - Mentioned for splitting releases of their high Q Sonnet and Opus versions and for not entering the multimodal race.
Google - Mentioned in relation to Gemini 3 and Google AI Studio.
Nvidia - Mentioned in relation to Blackwell and Hopper chips.
Cognition - Mentioned as an example of an agent lab.
Cursor - Mentioned as an example of an agent lab.
Meta - Mentioned in relation to Jan LeCun leaving.
Wix - Mentioned as a company aware of the shift away from templates and investing in AI.
Squarespace - Mentioned as a company aware of the shift away from templates.

Other Resources

AI ROI Benchmarking Survey - Mentioned as a resource to learn more about.
AADB Intelligence Service - Mentioned as a forthcoming service that includes original research and information benchmarks.
GPT-5 - Mentioned as a model release that built up significant expectations.
Gemini 3 - Mentioned as a model release and in relation to Google's second half of the year.
Images 1.5 - Mentioned as an OpenAI model release.
Grok - Mentioned as continuing to push images and video.
Notebook LM for agent building - Mentioned as a potential interface for building agents.
World Models - Mentioned as an area of excitement and a potential path to AGI.
Vibe coding - Mentioned as a significant theme of 2025 and its expected changes in 2026.
Agent Readiness Audit - Mentioned as a resource to request a company's agent readiness score.
AI Daily Brief - Mentioned as a podcast that helps understand important news and discussions in AI.
AI - Mentioned throughout the text in various contexts.
Assistance to agent management - Mentioned as an evolution in AI capabilities.
Bespoke personal software - Mentioned as a trend likely to grow.
Enterprise adoption - Mentioned as a trend that will be reshaped by other trends.
Models and capabilities - Mentioned as a category for AI predictions.
Vibe coding - Mentioned as a category for AI predictions.
Enterprise plus vibe coding - Mentioned as a category for AI predictions.
Enterprise trends not including vibe coding - Mentioned as a category for AI predictions.
Competition market and politics - Mentioned as a category for AI predictions.
Meter line - Mentioned in relation to a chart measuring task length in human hours.
Blackwell chips - Mentioned as part of a new Nvidia architecture.
Hopper chips - Mentioned as part of a new Nvidia architecture.
GPT-5.1 - Mentioned as a subsequent release after GPT-5.
GPT-5.1 Codex - Mentioned as a subsequent release after GPT-5.
GPT-5.2 - Mentioned as a subsequent release after GPT-5.
GPT-5.2 Codex - Mentioned as a subsequent release after GPT-5.
Sonnet - Mentioned as a version of Anthropic's models.
Opus - Mentioned as a version of Anthropic's models.
Nano Banana Pro - Mentioned in relation to images created by Manas.
Images 1.5 - Mentioned as an OpenAI model release.
Agent building - Mentioned in relation to interfaces for building agents.
Google AI Studio - Mentioned as inching towards an interface for building agents.
AI ROI benchmarking survey - Mentioned as a resource to learn more about.
AADB Intelligence Service - Mentioned as a forthcoming service that includes original research and information benchmarks.
Last mile end user data - Mentioned as valuable for refining models.
Agent labs - Mentioned as a category of labs, including Cognition and Cursor.
Model labs - Mentioned as a category of labs, including OpenAI and Anthropic.
Memory - Mentioned as a significant opportunity in LLMs.
LLMs - Mentioned in relation to memory capabilities.
AGI - Mentioned as a potential outcome of world models.
VR - Used as an analogy for the current state of world models.
Assistance - Mentioned as a current state of AI that will evolve.
Agents - Mentioned as an evolving capability of AI.
Agent managers - Mentioned as a likely focus in 2026.
Vibe coding among non developers - Mentioned as a distinct concept from engineering vibe coding.
AI enabled coding - Mentioned as a factor in the reorganization of engineering organizations.
Custom legal contract analyzers - Mentioned as an example of vibe coding in non-tech areas.
Onboarding apps for HR - Mentioned as an example of vibe coding in non-tech areas.
Ephemeral software - Mentioned as a potential term for bespoke personal software.
Luvable - Mentioned as a tool that can be used to build bespoke personal software.
Personal software - Mentioned as a trend likely to grow.
AI app entrepreneur - Mentioned as a new class of entrepreneur likely to emerge.
Template based website creation software - Mentioned as facing a tough time.
Knowledge work vibefication - Mentioned as a trend for 2026.
Forward deployed vibers - Mentioned as a new role companies may hire.
Replacement software - Mentioned as something companies may build in 2026.
Dashboard - Mentioned as a focus for 2026, calling it the "year of the dashboard."
Impact metrics - Mentioned as a focus for measuring value.
Data and context engineering - Mentioned as a focus for enterprise infrastructure.
AI and agent infrastructure - Mentioned as an area of investment for enterprises.
Assisted AI - Mentioned as a part of personal productivity gains.
Automated workflows - Mentioned as a type of AI process.
Process reinvention - Mentioned as a likely destiny for workflows with agents.
AI compounding - Mentioned as a factor that will increase the gap between leading and lagging organizations.