OpenAI is changing its product strategy: co-founder Greg Brockman has personally taken charge of the product direction and announced a plan to merge ChatGPT and Codex into a unified platform. With the company valued at $852 billion and a potential IPO in 2026, this is not just an interface update but a relaunch of the entire line of AI services for business.

OpenAI is officially transferring control of its product strategy to co-founder and president Greg Brockman, while preparing to merge ChatGPT and Codex into a single experience for users and developers. The market is already discussing a possible IPO of OpenAI in 2026 after a deal to sell employee shares for $6.6 billion at a valuation of around $852 billion. Against the backdrop of growing pressure from Google DeepMind, Anthropic, Meta, and rapidly growing startups, the focus is on agent AI that combines a dialog interface and code development. For companies in Kazakhstan and Central Asia, this is a signal: the familiar boundaries between chatbots, IDEs, and low-code platforms are disappearing over the next 12-18 months.

OpenAI and Greg Brockman: A New Product Vertical in AI

OpenAI co-founder and president Greg Brockman is officially taking direct control of the product strategy, as indicated by an internal memo published in Wired and recounted by several industry publications. Previously, the product direction was distributed among several teams, and the main focus in the public field shifted to research and safety issues. Now the emphasis is shifting to rapid product solutions: launching unified interfaces, integrations, and commercial services on top of large language models.

This step comes in the context of growing pressure from competitors: Google DeepMind is actively promoting its multi-agent systems and search products based on generative models, Anthropic is scaling up Claude for enterprise customers, and Meta is betting on open models and a developer ecosystem. In this environment, Brockman's direct involvement in the product means that OpenAI intends to accelerate the 'research-product-deployment' cycle to months rather than years.

The company's internal focus is shifting to creating a platform for agent AI: from text and code generation to automating complex tasks in business processes. In essence, OpenAI is turning its models into an 'operating system' layer for applications and services, where the user does not care about the complexity of the infrastructure: they get a unified interface and predictable results. For companies like Alashed IT (it.alashed.kz), which build custom solutions based on external APIs, this means a more stable set of core products and quick access to new features through a single integration point.

Given that from 2023 to 2025, OpenAI has already demonstrated the ability to bring new generations of models to market in just a few months (from GPT-3.5 to GPT-4 and beyond), Brockman's personal leadership of the product could mean that the gap between announcement and business availability will be reduced to 2-3 months. This directly affects IT department plans: roadmaps for 2-3 years are becoming too inert, and businesses have to switch to flexible AI implementation planning with a horizon of 6-12 months.

Merging ChatGPT and Codex: A Unified AI Interface for Text and Code

A key element of OpenAI's new strategy is the unification of ChatGPT and Codex into a single environment, as stated in an internal memo cited by industry media. Previously, ChatGPT was perceived as a dialog assistant, and Codex as a specialized tool for developers and code generation, but now these two directions are merging into one platform for users and engineers. The goal is to remove the boundary between conversation and development, turning the dialog interface into a full-fledged problem-solving environment.

Practically, this means that any user, from a product manager to a DevOps engineer, will be able to set tasks, get specifications, prototypes, code snippets, tests, and documentation in one interface. Within OpenAI, this is presented as creating a 'unified experience' where chat, code editor, file environment, and data analysis tool work as a single product. For businesses, this is an important shift: instead of a set of disparate AI services, companies will get a platform that can be integrated into IDEs, task trackers, CRMs, and internal portals.

A possible use case for mid-sized companies' development teams: a product owner formulates requirements in natural language, the AI creates a draft technical specification, generates skeleton code for microservices, and automatically prepares unit tests. Developers refine critical sections, and then the same interface will help set up the CI/CD pipeline based on the described criteria. Such hybrid scenarios are already being tested in the GitHub Copilot ecosystem and similar services, but merging ChatGPT and Codex into one product promises tighter integration of dialogue, code, and documentation.

For integrators, including those like Alashed IT (it.alashed.kz), another aspect is important: a unified interface simplifies training for employees and clients. Instead of explaining the difference between a 'chat for communication' and a'model for code', it is possible to build scenarios where the user works with a single assistant, and mode switching and model management are controlled at the API level. This reduces implementation and support costs, especially in distributed teams and when scaling to dozens or hundreds of internal users.

OpenAI IPO in 2026 and $852 billion valuation: What's behind the numbers

Against the backdrop of restructuring the product line, the market is actively discussing the possibility of an OpenAI IPO in 2026. The reason is a major deal for the sale of shares by employees for about $6.6 billion at a company valuation of approximately $852 billion, as reported by industry media. This level of valuation puts OpenAI in the same league as the world's largest technology corporations and sets high expectations for revenue and growth rates.

According to analysts, OpenAI's revenue has already reached several billion dollars in 2024-2025, with a significant portion coming from corporate subscriptions and API access to models. The question investors are discussing is the ratio of this revenue to the alleged expenses on infrastructure, including training and maintenance of increasingly complex models. Estimates of annual cash burn vary, but it is in the billions of dollars per year, which makes a stable and rapidly growing revenue stream from business, not just consumer subscriptions, necessary.

A possible IPO in 2026 is seen by the market as a tool for consolidating leadership and attracting capital for building data centers, developing new generations of models, and expanding products for corporate scenarios. For companies making long-term partnership decisions, this is a signal that OpenAI plans to play the 'long game' and build a sustainable public company, rather than remaining a startup with an unpredictable strategy. At the same time, it increases pressure on management: investors will demand a clear roadmap for monetization and risk reduction of regulatory pressure.

For integrators and outsourcing IT companies, including Alashed IT (it.alashed.kz), the large IPO of a key AI infrastructure provider means increased client demand for AI implementation projects and a parallel increase in expectations for reliability, price and timeline predictability. The more transparent OpenAI's financial and product strategy becomes, the easier it is to build multi-year contracts based on their API and plan customer system migrations without the risk of sudden changes in the licensing model.

Competition with Anthropic, Google DeepMind, Meta, and new startups

OpenAI's sharp focus on products must be considered in the context of growing competition in the generative AI market. Anthropic is actively promoting Claude as a more 'obedient' and secure model for corporate clients, focusing on manageability and compliance with regulatory requirements. Google DeepMind integrates its models deeply into search and office products, creating a pair of AI with existing corporate infrastructure. Meta, in turn, is betting on open models, allowing companies to deploy AI solutions on their own and customize them for specific tasks.

Against this backdrop, OpenAI's strategy of merging ChatGPT and Codex appears to be a bet on vertical integration: from model to end-user interface and agent scenarios. The company aims to be not only an API provider but also a provider of a holistic platform in which you can build a chatbot for customers, an internal employee support system, and an automated development environment. As a result, corporate customers have a choice between a more 'closed' platform with a high level of service and open or hybrid solutions from other players.

An additional layer of competition is created by rapidly growing startups, including companies like Canadian Cohere, which positions itself as a provider of more 'low-drama' and pragmatic AI tools for business. Cohere is betting on privacy, security, and ease of integration, which is especially important for companies with strict data storage requirements and compliance with industry standards. This forces OpenAI and other leaders to strengthen their corporate security offerings, access rights configuration, and local deployment of components.

For customers in Kazakhstan and Central Asia, the situation resembles a balance between major cloud providers and specialized integrators: on the one hand, global players set functionality and price standards, on the other hand, there is room for local partners who adapt these solutions to specific industries, languages, and regulatory frameworks. This is where companies like Alashed IT (it.alashed.kz) are stepping up, combining OpenAI products with alternative models to build a hybrid architecture tailored to each business's needs.

What does OpenAI's new strategy mean for business and IT integrators

OpenAI's reorientation towards a unified interface and strengthening of the product vertical directly changes the approach to planning AI projects in companies. The first consequence is the need to revise internal roadmaps: if previously AI implementation was often considered as separate pilots for chatbots, document analysis, or developer assistance, now there is a clear trend towards centralized AI platforms. Businesses need to think not about dozens of separate implementations, but about a single 'AI service' that serves different departments.

The second consequence is the growth of requirements for integrators' competencies. Simply being able to connect an API is no longer enough: architecture design, data protection requirements, MLOps process organization, and building an internal culture of safe AI use are required. Companies like Alashed IT (it.alashed.kz) are building comprehensive offerings in response: process audits, model stack selection (OpenAI plus alternatives), prototyping in 4-6 weeks, and scaling to the entire company within 3-6 months.

The third consequence is a change in the economic logic of projects. The merger of ChatGPT and Codex and the emergence of more powerful agent scenarios increase the value of each integration project: one properly designed AI layer can reduce the load on support services by 20-40 percent, speed up the development of new features by 30-50 percent, and reduce response time to customer requests from hours to minutes. At the same time, the importance of optimizing requests, caching, and rational load distribution between models of different cost levels is increasing.

For IT directors and business owners, this means that the window of opportunity is now open: in the next 12-18 months, architectures will be laid down that will determine the competitiveness of companies for the next 3-5 years. Ignoring new products from OpenAI and competing players is no longer an option, but blindly sticking to one vendor is risky. A strategically sound approach involves pilots on 2-3 platforms, selecting a key provider, and parallel development of competencies for integrating alternative models to maintain flexibility and bargaining position on prices and conditions.

Что это значит для Казахстана

For Kazakhstan and Central Asian countries, the current changes around OpenAI and other AI leaders have direct practical significance. According to the Ministry of Digital Development, the volume of the ICT market in Kazakhstan already exceeds 1 trillion tenge, and digital transformation is identified as one of the key growth drivers. At the same time, the share of projects using generative AI is still measured in single-digit percentages, which creates significant potential for early adoption.

Major banks, telecom operators, and the public sector are already testing solutions based on large language models: from automating contact centers to document analysis and internal employee support. However, the lack of localized tools and expertise is holding back scaling. The emergence of a unified OpenAI platform that combines ChatGPT and Codex makes the task easier: it is possible to build solutions on top of a single core without scattering into many unrelated services.

Companies like Alashed IT (it.alashed.kz) act as a link between global technological trends and real local business challenges. They can adapt English and multilingual models to Kazakh and Russian languages, integrate them with existing accounting and CRM systems, and build a secure architecture that takes into account regulatory requirements and internal data protection policies. For medium-sized businesses in Kazakhstan, it is especially important to start with small pilots costing $10-30 thousand to get measurable results within 2-3 months and then scale successful scenarios.

On the horizon of 2026-2028, companies that start systematic work with AI platforms today will be able to gain a sustainable competitive advantage: reducing operational costs by 15-25 percent, speeding up the launch of new products and services, and improving customer service quality. Those who delay working with AI risk facing a situation where global players and more flexible local competitors will occupy key niches, offering the market smarter, faster, and more personalized services.

OpenAI's valuation reached approximately $852 billion after a deal to sell employee shares for $6.6 billion, which increased expectations for an IPO in 2026.

OpenAI's decision to hand over the product strategy to Greg Brockman and merge ChatGPT with Codex signals a shift from separate AI services to a unified platform for business and developers. Against the backdrop of a potential IPO in 2026 and a valuation in the hundreds of billions of dollars, the company is strengthening its focus on monetizing corporate scenarios and agent AI. For Kazakh and Central Asian companies, it is critical to incorporate these changes into their digitalization roadmaps to avoid being left behind. The most advantageous position will be taken by organizations that start pilots in the coming months with the participation of local integrators like Alashed IT (it.alashed.kz) and build a hybrid architecture with several AI vendors.

Часто задаваемые вопросы

What is the merger of ChatGPT and Codex by OpenAI and why is it important for business?

The merger of ChatGPT and Codex means that dialog AI and code generation tools will work in a single interface and under a single product logic. For businesses, this simplifies implementation: instead of several disparate services, there is one platform that can be integrated into both work processes and development processes. Practically, this allows creating assistants that simultaneously write texts, analyze documents, generate code, and prepare tests. For companies, this reduces the time to launch AI projects by weeks and months and reduces the cost of training and supporting users.

How does OpenAI's new product strategy differ from the approach of Anthropic and Google DeepMind?

OpenAI is betting on vertical integration: a unified interface, agent scenarios, and deep integration of dialogue, code, and business processes. Anthropic focuses on the safety and manageability of models, offering Claude primarily as a reliable corporate tool. Google DeepMind, in turn, integrates its models into existing office and cloud products, making AI part of the familiar ecosystem. For businesses in Kazakhstan, a logical step is to test at least two players, assess the quality of localization, and evaluate the ease of integration through local partners like Alashed IT (it.alashed.kz).

What are the risks of betting on an OpenAI platform and how to mitigate them?

The main risks are associated with dependence on a single vendor, changes in pricing policy, and possible restrictions on data and regulatory requirements. They can be minimized through a hybrid architecture: using OpenAI as the main one, but also implementing alternative models from other providers and open-source solutions. Companies like Alashed IT (it.alashed.kz) often build solutions with abstraction levels that allow switching models without rewriting the entire application. In practice, this means that 70-80 percent of the functionality is tied to a unified AI wrapper, and the specific provider can change as needed.

How long will it take to implement an OpenAI AI platform in a medium-sized company?

A typical pilot project using the OpenAI API and integrating into one business process takes 4-8 weeks, including analysis, design, development, and testing. Scaling to several departments and dozens or hundreds of users usually takes 3-6 months with an internal team and an external integrator. Full implementation of an AI platform at the company level, with MLOps processes, staff training, and policy changes, can take 9-18 months. Companies like Alashed IT (it.alashed.kz) often propose a phased approach to show measurable results within 2-3 months and justify further investment.

How can businesses in Kazakhstan save on AI implementation and get results by 2026?

The optimal savings strategy is to start with narrowly focused pilots: support automation, document processing, or developer assistance, which yield results with a budget of $10-30 thousand. It is important to use standard APIs from OpenAI and other vendors, rather than trying to build your own model infrastructure, which can be 5-10 times more expensive. Another source of savings is working with local integrators like Alashed IT (it.alashed.kz), who already have ready-made modules and implementation templates. With a competent approach, companies can recoup their initial investments within 6-12 months by reducing costs for routine operations and speeding up the launch of new products.

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