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The competition for enterprise AI has become even more clearly defined

OpenAI said it plans to nearly double its headcount to about 8,000 by the end of 2026, stepping up its push for enterprise AI [1]. This isn’t just a hiring spree—it signals a shift toward strengthening B2B product development, sales, and support to raise its profile in the enterprise market.

What makes this move especially important is that the “battlefield” for AI is shifting from hype to ongoing, repeatable business deployment. Because enterprises evaluate not only product performance but also post-deployment maintenance, administration, security, and workflow design, organizational capability becomes as much a competitive advantage as model quality. For OpenAI, increasing headcount is about more than competing on model performance—it’s about building the capacity to go head-to-head with enterprise-focused strong players such as Anthropic [1].

Going forward, it’s likely that decisions about adoption will hinge less on raw model-performance gaps than on deployment support, management capabilities, auditability, and cost-effectiveness. In other words, the evaluation axis will move from “Can we use it?” to “Can we run it safely inside the company?” Vendors will therefore be pushed toward a broader competition that includes sales and support.

AI policy is moving from “strong regulation” toward “coordination with state laws and industry development”

The White House released a national policy framework that does not plan to create new AI regulators at the federal level and instead outlines a relatively light-touch approach [4]. It highlights seven areas—protection of children, copyright, free speech, the workforce, education, and more—while pointing toward using existing institutions and industry standards.

What matters most is its posture of leaving decisions on copyright for training data to the courts, along with promoting voluntary licensing [4]. Because one of the hardest issues to settle in deploying generative AI in society is “what was used for training,” the trajectory may be less about administrative bodies drawing lines all at once and more about shaping practice through lawsuits and industry norms.

It’s also important that it emphasizes placing federal law on top of state law (i.e., aligning rather than preempting aggressively) [4]. For companies, this could reduce the cost of handling rules that vary from state to state. However, uncertainty remains—especially around copyright, likeness, and employment impacts. In the future, it may be more useful to watch which topics get handed off to courts, standards, and industry agreements than to ask simply whether regulation will become stricter.

AI agents: the main challenge is “boundary design,” not “how smart they are”

In research and implementation of AI agents, the focus has shifted beyond simply improving capability toward issues of execution boundaries and identity management. In the Nimbus example, agents lacked stable self-modeling and sometimes confused themselves with other agents depending on context—revealing weak identification in shared environments [2]. Meanwhile, debate continues over where exactly to place the agents’ execution boundary, and the layer at which allow/deny decisions are made strongly affects both safety and how easily the system can be combined with workflows [21].

Separately, when asking AI to write code, incidents driven by “speed-first” behavior stand out. AI coding demos can generate large numbers of files in a short time, but reports from production environments show failures such as infinite loops or database exhaustion. This reinforced that idempotency, backoff, testing, and observability are indispensable [9]. Design frameworks that ask questions before writing code are also emerging, and the mindset of having AI act as a design assistant rather than directly as an “implementer” is gaining traction [8].

The takeaway going forward is clear: in agent adoption, the decisive factor will shift from the agent’s “intelligence” to who it pretends to be, what it is allowed to execute, and how it stops when something goes wrong. Agents may multiply, but that doesn’t automatically mean value increases when you let them run without limits.

On-device AI and multimodalization are becoming practical realities

There is growing momentum to run large models locally. Tinybox has been presented as an offline AI device capable of inference at the scale of 12 billion parameters-class without cloud connectivity, demonstrating demand for privacy protection and local inference [6]. There are also shared practices for maximizing limited GPU resources—for example, optimizing Qwen3.5-9B on RTX 3070 Mobile to reach roughly 50 t/s, and scripts exploring MOE settings and batch configurations for llama.cpp [23][24].

At the same time, implementation of multimodal RAG is becoming more concrete. Gemini Embedding 2 embeds text, images, audio, and video into the same space, enabling cross-modal and integrated search [5]. This connects directly to enterprise needs that go beyond internal documents to include video manuals, meeting audio, and image materials.

Overall, this trend indicates that AI usage is moving from “chatting in the cloud” toward how to integrate internal data and how far to keep things closed locally. Going forward, it’s likely that the standard approach will be to design architectures that choose between cloud and local based on a tradeoff among cost, speed, privacy, and manageability.

Model selection is no longer determined only by “maximum performance”

Nemotron Cascade 2 30B-A3B achieved 97.6% on HumanEval, showing performance that outpaces mid-sized Qwen-family models [11]. Meanwhile, it’s also been shown that even small models under 30B can function sufficiently as agents if you combine them with MCP tools and sandbox execution [17]. In other words, the decision of whether to use a large model or whether a smaller model is “good enough” is becoming more grounded in real-world needs.

There are also cases where domain-specific evaluation and instruction data matter more than general-purpose models—for example, PIXIU, which is focused on finance [15]. In medical imaging, it has been shown that changing the slice thickness in CT scans significantly affects detection sensitivity, reinforcing that AI is vulnerable not only to the model itself but also to variations in input conditions [13].

Going forward, model choice will be based on more than benchmark numbers. Enterprises will need to select based on data conditions, business domain, and operational constraints. While performance competition will continue, what determines whether adoption succeeds is whether the model can stay reliable under your company’s conditions.

Talent, publishing, and investment: AI enthusiasm and caution are unfolding at the same time

OpenAI’s hiring expansion [1], reports that major researchers at DeepSeek have resigned [12], and publishers withdrawing publications amid allegations of AI involvement [22] all symbolize turbulence in the industry’s structure. Talent becomes a competition, intellectual property is scrutinized more strictly, and companies will increasingly bear responsibility to explain how much AI they used.

On the other hand, as seen in how NVIDIA’s stock price fell after a major event, investors are beginning to incorporate not only growth expectations for AI but also concerns about a bubble and uncertainties about monetization [7]. While AI’s economic impact is widely expected to be significant, whether large-scale labor displacement will actually occur remains unclear and depends on hiring pace, wages, and policy responses [16].

Therefore, AI going forward won’t be a matter of “adopt it and you win.” The real competitive advantage will come from whether you can design—holistically—your organization, talent strategy, legal posture, operations, and investment decisions.

1. Start with “use AI to ask questions”

The most common way AI fails is when you operationalize it to generate code or deliverables from the start [8][9]. As your first step, have ChatGPT or Claude do “verification,” not “creation.”

Example use cases

• Goal: Reduce missing or overlooked requirements
• Prompt examples:
  • “Before implementing this new feature, list 10 questions you should verify.”
  • “Compare the tradeoffs between JWT authentication and session authentication for non-engineers.”
  • “List failure patterns for this business workflow first.”

2. Use agents in stages

Following an approach like Spec-Kit-CoLearn, don’t put AI directly in the role of coding from the beginning. Separating design → approval → implementation reduces incidents [8]. You can run this workflow with ChatGPT, Claude, Cursor, or GitHub Copilot.

Recommended workflow

1. Use ChatGPT/Claude to organize requirements
2. Ask it to produce “unknown points,” “risks,” and “alternatives” for the specifications
3. After approval, implement with Cursor or GitHub Copilot
4. Generate test code and review perspectives via separate prompts

Copy-paste prompt examples

• “First, don’t implement anything—only ask questions as the designer.”
• “Next, propose three implementation options and compare them by maintainability, cost, and safety.”
• “Finally, code only the approved option.”

3. Think about enterprise search and internal RAG together with MCP

MCP (Model Context Protocol) is a standard that’s easy to use for connecting external data and tools from Claude Desktop or an IDE [14]. It works especially well when connecting internal FAQs, meeting minutes, knowledge bases, or ticketing systems.

How to get started

• First, set up an MCP server in read-only mode
• Limit references to a single internal data source
• Ask the answers to include source URLs or document names
• Prevent access to data for which the user lacks permissions

4. For multimodal RAG, “how you expand the searchable scope” is crucial

Using Gemini Embedding 2, you can embed text, images, audio, and video across modalities and unify them into the same search experience [5]. For example, you can enable search across sales decks, product images, explanation videos, and sales-call audio.

A good setup to try first

• Narrow to one theme (e.g., product description materials)
• Start small with images and PDFs only
• Use nearest-neighbor search to surface “likely relevant” items
• Then add meeting audio and video

5. Try local AI with “lightweight” use cases first

As Tinybox and llama.cpp examples show, local inference is already within a practical range [6][23][24]. With tools such as LM Studio, Ollama, and llama.cpp, you can target tasks like highly confidential summarization and drafting.

Use cases you can try today

• Summarizing internal memos
• Drafting meeting minutes
• Classifying documents with high confidentiality
• Formatting text you don’t want to send externally

6. For content workflows, run it as “generate → edit → reuse”

Using ChatGPT or Claude, you can improve efficiency by generating multiple formats from one long piece of text [19].

Practical example

• From a single pitch deck:
  • One-page summary for executives
  • Proposal text for sales
  • FAQ for customers
  • Draft social media posts
  • Email wording

Prompt example

• “Rewrite this text into three versions: for leadership, for frontline teams, and for customers.”
• “Break this long text into five short posts.”

7. Use the extra guidance for developers

• Visual Studio Code’s Microsoft Foundry extension can be a candidate if you’re using an Azure-based development flow [18]
• Bifrost CLI + Codex CLI are useful if you want to line up the initial setup for a coding agent [20]
• OpenTelemetry’s LLM tracing standard is effective when your operations team needs to track AI behavior [3]

8. What to do first when rolling out AI in a business

• Split candidate tasks for AI into “creation,” “summarization,” “search,” and “classification”
• Among them, start with the ones that are least likely to cause incidents
• After using it, record not only what got faster, but also what became harder to see