Microsoft’s new winapp CLI simplifies Windows app development with one-command setup, faster testing, and easier packaging.
The post Microsoft Launches winapp to Simplify Windows App Development appeared first on TechRepublic.
Microsoft’s new winapp CLI simplifies Windows app development with one-command setup, faster testing, and easier packaging.
The post Microsoft Launches winapp to Simplify Windows App Development appeared first on TechRepublic.
Both platforms serve as backbone infrastructure for remote work and software development, making these flaws particularly dangerous for business continuity.
The post Zoom and GitLab Patch RCE, DoS, and 2FA Bypass Vulnerabilities appeared first on TechRepublic.
Both platforms serve as backbone infrastructure for remote work and software development, making these flaws particularly dangerous for business continuity.
The post Zoom and GitLab Patch RCE, DoS, and 2FA Bypass Vulnerabilities appeared first on TechRepublic.
As discussions surrounding the CLARITY Act—often referred to as the crypto market structure bill—continue in Washington, Kristin Smith, President of the Solana Policy Institute, has provided insights on the current status of the legislation and the organization’s top priorities.
One of the main priorities disclosed by Smith in a recent post on social media platform X (formerly Twitter), is the importance of protecting open-source developers in the legislative landscape.
Smith pointed out that the recent delay in the markup of the market structure bill last week after Coinbase’s withdrawal should be seen as a temporary setback. “Despite the delay, industry engagement remains robust, and there is clear bipartisan support to achieve durable regulatory clarity for market structure,” she noted.
The Senate Agriculture Committee is making advancements with its own draft of the legislation expected to be released on Wednesday, as earlier reported by Bitcoinist.
Smith also highlighted a shared objective: to create a framework that protects consumers, fosters innovation, and provides certainty for developers operating in the United States. A central tenet of this goal is the safeguarding of developers, which Smith argued is crucial for the success of the industry.
The Solana Institute was founded to ensure that policymakers gain a comprehensive understanding of public blockchains and the protocols that underpin them.
Smith articulated the critical role that open-source software plays within the crypto ecosystem, noting that developers around the world collaborate to produce software that anyone can inspect, use, or improve. “Openness is a strength—not a liability,” she asserted.
However, she raised concerns regarding the case against Roman Storm of Tornado Cash, indicating that it treats open-source innovation as something questionable. Smith warned that penalizing developers merely for writing and publishing open-source code endangers all those involved in such collaborative efforts.
She emphasized the “chilling effect” that the prosecution could have on open-source developers, asserting that writing code is an expressive act protected by the First Amendment.
Smith called for clear policy that differentiates between bad actors and developers working on lawful, general-purpose tools. To bolster this cause, she encouraged supporters to draft letters expressing their stance in favor of open-source protections.
Roman Storm responded to Smith’s support, thanking her and the broader community for advocating for open-source principles. He remarked, “Criminalizing the act of writing and publishing code threatens not just one developer, but the foundations of digital security, privacy, and innovation.”
At the time of writing, Solana’s native token, SOL, was trading at $130.33, mirroring the performance of the broader crypto market, dropping 11% in the weekly time frame.
Featured image from DALL-E, chart from TradingView.com
Machine learning has officially moved out of the lab.
In 2026, businesses are no longer asking “Can we build an ML model?” — they’re asking “Can we run reliable, scalable, and cost-efficient ML pipelines in production?”
The difference between experimental ML and real business impact lies in production-grade ML pipelines. These pipelines ingest data, train models, deploy them, monitor performance, retrain automatically, and integrate with real-world systems. And at the center of all this complexity is one critical decision:
👉 Hire TensorFlow developers who understand production ML, not just model training.
TensorFlow remains one of the most trusted and widely adopted frameworks for building end-to-end ML systems. But in 2026, simply knowing TensorFlow APIs is not enough. Companies need TensorFlow developers who can design, deploy, optimize, and maintain production ML pipelines that actually work at scale.
In this guide, we’ll explore why production ML pipelines matter, why TensorFlow is still a leading choice, what skills modern TensorFlow developers must have, and how hiring the right talent determines long-term ML success.
Many organizations still equate ML success with model accuracy. In reality, accuracy is only one small part of the equation.
A production ML pipeline must handle:
Without these capabilities, even the best-performing model becomes unusable.
This is why organizations that succeed with ML focus less on individual models and more on robust ML pipelines — and why they deliberately hire TensorFlow developers with production experience.
Despite the growth of alternative frameworks, TensorFlow continues to dominate production ML environments for several reasons.
TensorFlow supports the full ML lifecycle — from data pipelines and training to deployment and monitoring.
TensorFlow is battle-tested at scale, supporting distributed training, GPUs, TPUs, and large enterprise workloads.
With tools like TensorFlow Serving, TensorFlow Extended (TFX), and TensorFlow Lite, teams can deploy models reliably across environments.
Many enterprises rely on TensorFlow due to its stability, long-term support, and strong community.
Because of this maturity, companies building serious ML systems continue to hire TensorFlow developers for production pipelines.
Production ML is hard — and it fails more often than most teams expect.
Common failure points include:
These problems rarely come from the framework itself. They come from lack of production ML expertise.
Hiring TensorFlow developers with hands-on pipeline experience dramatically reduces these risks.
Before discussing hiring, it’s important to define what production-ready actually means.
A mature ML pipeline in 2026 should be:
TensorFlow developers play a key role in delivering all of these qualities.
When you hire TensorFlow developers for production ML, you’re not just hiring model builders — you’re hiring system engineers.
Here’s what experienced TensorFlow developers contribute.
Data is the foundation of ML.
TensorFlow developers design pipelines that:
Poor data pipelines are the number one cause of ML failures.
Feature consistency is critical.
TensorFlow developers ensure:
This consistency prevents subtle bugs that degrade model performance.
Production ML often requires large datasets and complex models.
TensorFlow developers handle:
This ensures training is efficient, repeatable, and cost-controlled.
Before deployment, models must be validated rigorously.
TensorFlow developers implement:
This protects production systems from regressions.
Model deployment is where many teams struggle.
TensorFlow developers design serving systems that:
This is essential for production reliability.
Once deployed, models must be watched continuously.
TensorFlow developers build monitoring for:
Without monitoring, production ML becomes a blind spot.
In 2026, ML pipelines must evolve automatically.
TensorFlow developers implement:
This keeps ML systems accurate over time.
Hiring the right TensorFlow developers requires evaluating the right skill set.
Developers should be fluent in:
This enables flexibility and optimization.
Look for experience with:
Production ML experience is non-negotiable.
TensorFlow developers must understand:
Scalability is critical in production environments.
Production ML often runs in the cloud.
Developers should know how to:
Unoptimized ML pipelines can be expensive.
TensorFlow developers should optimize:
This directly impacts ROI.
Production ML is software engineering.
Developers must follow
This ensures long-term maintainability.
Many organizations make avoidable mistakes, such as:
Avoiding these mistakes starts with hiring the right TensorFlow developers.
To assess candidates effectively:
Practical experience matters more than theoretical knowledge.
Organizations use different hiring models based on needs.
Best for long-term, core ML platforms.
Popular for flexibility, cost efficiency, and speed.
Useful for pipeline audits or migrations.
Many companies choose dedicated models to scale faster.
The demand for TensorFlow talent is high.
Working with specialized partners offers:
This approach accelerates production ML adoption.
WebClues Infotech helps organizations build production-ready ML pipelines by providing skilled TensorFlow developers with real-world experience.
Their TensorFlow experts offer:
If you’re planning to hire TensorFlow developers for production ML pipelines in 2026.
In 2026, production ML pipelines are driving value across:
Across industries, success depends on pipeline reliability.
While experienced TensorFlow developers require investment, they deliver:
The ROI compounds as pipelines scale.
Looking ahead, production ML pipelines will emphasize:
TensorFlow developers who understand these trends will remain in high demand.
In 2026, ML success is no longer defined by experimentation — it’s defined by production reliability.
Organizations that invest in strong ML pipelines gain a lasting competitive advantage. And those pipelines are built by people, not frameworks.
By choosing to hire TensorFlow developers with proven production ML experience, businesses ensure their models don’t just work in theory — but deliver real, measurable value in the real world.
If your goal is to build scalable, reliable, and future-proof ML systems, the smartest move you can make is to hire the right TensorFlow developers today.
Hire TensorFlow Developers for Production ML Pipelines in 2026 was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.
The patched issues span core standard library components including archive/zip and net/http, as well as security-sensitive areas of the crypto/tls stack.
The post Go Programming Language 1.26 Patches Several Security Flaws appeared first on TechRepublic.
The patched issues span core standard library components including archive/zip and net/http, as well as security-sensitive areas of the crypto/tls stack.
The post Go Programming Language 1.26 Patches Several Security Flaws appeared first on TechRepublic.
Replit’s new feature generates iOS apps from text prompts, integrates monetization, and streamlines App Store publishing - marking a major step in AI-driven software creation.
The post Replit’s AI can build your mobile apps and push them straight to App store appeared first on Digital Trends.
From automation to system management, these are the PowerShell commands IT pros should know and use in 2026.
The post Top 10 PowerShell Commands to Use in 2026 appeared first on TechRepublic.
From automation to system management, these are the PowerShell commands IT pros should know and use in 2026.
The post Top 10 PowerShell Commands to Use in 2026 appeared first on TechRepublic.
Strengthen your API security strategy by using trusted tools that help developers protect public and private APIs, improve system reliability, and scale applications with confidence. Discover how modern security solutions enhance visibility, streamline development workflows, and support long-term performance and growth.
APIs are the foundation of modern software development. They connect applications, enable integrations, support mobile experiences, and drive cloud-native architectures. As organizations rely more heavily on APIs, protecting them becomes an opportunity for developers to build resilient, scalable, and trusted systems. Today’s API security tools are powerful, easy to integrate, and designed to enhance developer productivity. Rather than slowing development, modern security platforms streamline workflows, improve visibility, and promote best practices. This article explores the best API security tools and how they help developers protect both public and private APIs effectively.
Why API Security Matters for Developers
APIs often handle sensitive data, authentication flows, and critical business logic. A secure API environment ensures stable performance, protects user trust, and supports long-term scalability.
For developers, strong API security delivers several positive benefits:
When security is built into the development process, teams gain confidence and momentum in delivering high-quality software.
API Gateways: Centralized Protection and Traffic Control
API gateways provide a centralized layer for managing incoming requests. They handle authentication, authorization, rate limiting, routing, and logging in a consistent way. Popular platforms such as Kong, Apigee, AWS API Gateway, and Azure API Management help developers enforce security policies across all services. Gateways support modern authentication standards like OAuth, JWT tokens, and encrypted communication. This centralized control simplifies maintenance, improves consistency, and enhances overall system reliability while keeping developer workflows efficient.
Web Application and API Protection Platforms
Web Application and API Protection platforms add intelligent traffic filtering and automated threat detection. These tools analyze behavior patterns and block abnormal requests before they impact applications. Solutions such as Cloud flare, Akamai, and Fastly provide global protection, bot management, and traffic optimization. Developers benefit from consistent performance, high availability, and automatic scaling during traffic spikes. These platforms contribute to stable production environments and improved user experience.
API Security Testing and Automation Tools
Proactive testing helps teams identify potential issues early in the development lifecycle. API security testing tools scan endpoints for configuration gaps, authentication issues, and data exposure risks. Tools like Postman, OWASP ZAP, and automated scanners integrate well into CI/CD pipelines, enabling continuous validation without disrupting delivery speed. Automated testing improves code quality, strengthens development discipline, and reduces long-term maintenance costs.
Runtime Monitoring and Observability Tools
Monitoring tools provide real-time insights into API health, performance, and usage trends. Platforms such as Data dog, New Relic, and Dynatrace offer dashboards, alerts, and tracing capabilities. These tools help developers identify bottlenecks, optimize response times, and maintain consistent uptime. Observability encourages proactive optimization and continuous improvement across engineering teams. Clear visibility into production systems supports confident scaling and long-term reliability.
Identity and Access Management Solutions
Identity and Access Management platforms ensure that only authorized users and services can access APIs. They manage authentication workflows, access policies, and token lifecycle management. Solutions like Auth0, Okta, AWS Cognito, and Azure Active Directory simplify secure authentication for both internal and public APIs. Developers can implement strong access controls quickly while maintaining excellent user experience. This approach strengthens security and reduces operational complexity.
Secrets Management and Encryption Tools
Secrets management tools protect sensitive information such as API keys, certificates, and credentials. Platforms like HashiCorp Vault, AWS Secrets Manager, and Azure Key Vault store secrets securely and automate rotation. Confidentiality and compliance are guaranteed via encryption, which safeguards data while it’s in transit and at rest. These tools support safe deployments and reinforce trust across environments.
Benefits of a Strong API Security Stack
A well-designed API security stack delivers meaningful advantages:
Instead of being a limitation, security becomes the basis for development.
Choosing the Right Tools for Your Architecture
The best API security tools align with your cloud environment, application architecture, and team workflows. Developers should prioritize solutions that integrate easily with CI/CD pipelines, provide clear documentation, and support automation. A layered approach combining gateways, protection platforms, testing tools, monitoring, identity management, and secrets management creates balanced protection without unnecessary complexity.
Final Thoughts
Protecting public and private APIs has become more accessible and developer-friendly than ever. Modern API security tools empower teams to build reliable, scalable, and secure systems with confidence. By adopting the right combination of security platforms and best practices, developers can accelerate delivery, maintain system stability, and build trusted digital experiences that grow successfully over time.
What Are the Best API Security Tools for Protecting Public and Private APIs? was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.
Enterprise AI has entered a new phase. In 2026, organizations are no longer experimenting with generative AI in isolation — they are embedding it deeply into core systems, workflows, and decision-making processes. At the heart of this transformation are OpenAI-powered solutions: custom GPT applications, intelligent copilots, workflow automation engines, and AI agents integrated across departments.
But as adoption grows, so does complexity.
Building enterprise-grade AI solutions with OpenAI models is no longer about simple API calls or prompt demos. It requires a specialized, multidisciplinary skill set — one that blends AI engineering, software architecture, security, cost optimization, and business alignment.
That’s why organizations that want reliable, scalable results deliberately choose to hire OpenAI developers with proven enterprise experience.
In this in-depth guide, we’ll break down the top skills OpenAI developers must have in 2026 enterprise projects, why these skills matter, and how businesses can identify the right talent to turn AI ambition into operational success.
Early generative AI projects focused on:
In contrast, 2026 enterprise projects involve:
The stakes are higher, and so is the required expertise.
Enterprises that hire general AI developers without these specialized skills often face:
This is why the decision to hire OpenAI developers must be strategic — not tactical.
An OpenAI developer in 2026 is not just someone who “knows GPT.”
They are professionals who can:
Let’s explore the skills that make this possible.
This is the foundation.
Enterprise OpenAI developers must have hands-on experience with:
They understand when and how to use specific OpenAI models, rather than defaulting to the most powerful (and expensive) option.
This depth of knowledge is essential for building efficient enterprise systems.
Prompting in enterprise projects is no longer ad hoc.
OpenAI developers must design prompts that are:
They often build prompt architectures, not single prompts — ensuring consistency, reliability, and maintainability.
This is one of the biggest differentiators when companies hire OpenAI developers for serious projects.
Enterprise AI must be grounded in real data.
OpenAI developers need strong expertise in RAG, including:
Poor RAG design leads to hallucinations, misinformation, and loss of trust. Skilled developers avoid these pitfalls.
Modern OpenAI solutions rarely involve a single model call.
OpenAI developers should be proficient with frameworks like LangChain to:
This orchestration skill is essential for enterprise automation and decision systems.
In 2026, OpenAI solutions are software products, not experiments.
Developers must follow:
This ensures AI systems are maintainable, auditable, and scalable over time.
Enterprise AI projects deal with sensitive data.
OpenAI developers must understand:
Security is not optional — it’s a core competency.
Unoptimized OpenAI usage can become expensive very quickly.
Skilled OpenAI developers know how to:
This cost discipline is critical for enterprise-scale deployments.
Enterprise users expect fast, reliable AI systems.
OpenAI developers must optimize:
Latency optimization directly impacts adoption and user satisfaction.
OpenAI solutions must work within existing ecosystems.
Developers need experience integrating with:
Seamless integration ensures AI delivers value where teams already work.
AI agents are becoming mainstream in enterprise environments.
OpenAI developers must understand:
This skill transforms AI from a passive assistant into an active collaborator.
Enterprise AI systems must be observable.
OpenAI developers implement:
This ensures reliability, accountability, and continuous improvement.
The best OpenAI developers understand why a system exists — not just how it works.
They can:
This alignment is critical for enterprise success.
Enterprise OpenAI projects involve many stakeholders.
Developers must communicate effectively with:
Clear communication prevents misalignment and accelerates delivery.
When evaluating candidates, be cautious of:
These gaps often lead to fragile or expensive AI solutions.
Effective evaluation goes beyond interviews.
Consider:
This reveals true enterprise readiness.
Given the demand and complexity, many organizations choose to:
This approach reduces risk and speeds up delivery — especially for long-term initiatives.
WebClues Infotech helps enterprises build production-ready OpenAI solutions by providing experienced OpenAI developers with strong enterprise backgrounds.
Their OpenAI talent offers:
If you’re planning to hire OpenAI developers for enterprise projects in 2026.
To maximize success:
These practices help ensure AI delivers sustained value.
OpenAI technology evolves rapidly — but enterprise value comes from how well it’s engineered.
By choosing to hire OpenAI developers with the right skills, organizations gain:
In 2026, this expertise is no longer optional — it’s mission-critical.
Generative AI is reshaping enterprise operations — but success depends on people, not just platforms.
The most impactful organizations in 2026 are those that invest in skilled OpenAI developers who can design, deploy, and govern AI systems responsibly and effectively.
If your goal is to move beyond experiments and build enterprise-grade AI solutions, the smartest move you can make is to hire OpenAI developers with the skills outlined in this guide.
Top Skills for OpenAI Developers in 2026 Enterprise Projects was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.
AI로 인해 일자리를 잃을 수 있다는 우려 속에서도 개발자는 당분간 숨 돌릴 시간이 있을 것으로 보인다. 분석 커뮤니티 레스롱(LessWrong)이 발표한 최신 보고서에 따르면 완전한 코딩 자동화에 도달하기까지는 앞으로 5~6년이 더 걸릴 전망이다. 이는 2027년 1월에서 2028년 9월 사이에 이뤄질 것이라고 예상했던 기존 예측에서 상당히 늦춰진 시점이다.
전망 조정은 레스롱이 초기 분석 결과를 내놓은 지 불과 8개월 만에 제시됐다. 이는 AI 미래 예측이 얼마나 불안정하고 주관적이며, 끊임없이 변할 수 있는 영역인지를 보여준다.
연구진은 보고서에서 “미래는 불확실하지만, 그저 도래하기를 기다려서는 안 된다”라며 “앞으로 어떤 일이 벌어질지를 예측하고, 트렌드에 주의를 기울여 이를 바탕으로 분석한다면 가능성이 높은 방향을 더 잘 이해할 수 있고, 실제 변화가 닥치는 상황을 준비할 수 있다”라고 설명했다.
레스롱이 제시한 ‘AI 미래 모델’에 따르면 AI는 2032년 2월 ‘초인적 코더’ 수준에 도달하고, 이후 약 5년 안에 인공 초지능(ASI) 단계로 발전할 가능성이 있다. 연구진이 정의한 초인적 코더란, 조직이 보유한 전체 연산 자원의 5%만 사용하면서도 사람 엔지니어 수의 30배에 달하는 에이전트를 동시에 운영할 수 있는 AI 시스템이다. 이 AI는 최고 수준의 개발자처럼 자율적으로 작업하며, 조직 내 최고 엔지니어보다 30배 빠른 속도로 업무를 수행할 수 있다고 연구진은 설명했다.
이번 분석은 2025년 4월에 제시된 레스롱의 초기 전망보다 완전한 자동화 시점이 3.5년에서 최대 5년까지 늦춰졌다. 연구진은 이러한 변화가 여러 차례의 재검토, 관점 전환, 연구 전략 조정을 진행한 결과라고 밝혔다.
특히 연구진은 AI 연구개발(R&D) 속도가 급격히 빨라질 것이라는 기존의 낙관적인 전망에서 한발 물러섰다. 대신 소프트웨어 지능 폭발(SIE), 즉 AI가 스스로 설계를 개선하며 사람의 지능 수준을 훨씬 뛰어넘는 현상에 대한 새로운 분석 틀을 적용했다. 이를 통해 추가 연산 자원 없이 AI가 스스로 역량을 얼마나 빠르게 향상시킬 수 있는지, 그리고 그 속도가 실제로 어느 정도인지를 살폈다. 아울러 AI가 연구 방향을 설정하고, 실험을 선택하며 그 결과를 해석하는 능력이 얼마나 성숙했는지도 핵심 분석 대상으로 삼았다.
여러 모델링 방식을 검토한 끝에, 레스롱 연구진은 현재의 성능 추세와 표준화된 테스트 결과를 바탕으로 미래 AI 역량을 예측하는 ‘역량 벤치마크 추세 외삽법(capability benchmark trend extrapolation)’을 최종적으로 선택했다. 인공 일반 지능(AGI)에 필요한 연산 자원은 METR의 타임 호라이즌 스위트인 METR-HRS를 활용해 산정했다.
연구진은 “벤치마크 추세는 때로 깨질 수 있고, 벤치마크 자체가 실제 역량을 완전히 대변하지는 않는다”라면서도 “그럼에도 METR-HRS는 고도화된 AI의 향후 발전 수준을 예측하는 데 현재로서는 가장 적절한 기준”이라고 설명했다.
다만 이번 모델은 METR 그래프에만 의존하지 않고 여러 추가 요인을 반영해 결과를 조정했다. 예를 들어 연산 자원, 인력, 데이터 등 AI 개발에 필요한 투입 요소가 지금과 같은 속도로 계속 증가하지는 않을 것으로 봤다. 반도체 생산 능력, 에너지 자원, 재정 투자 한계 등의 제약으로 성장 속도가 둔화될 가능성이 상당히 크다는 판단에서다.
또한 연구진은 소프트웨어 연구에서 수익 체감 현상이 나타나면서 파라미터 업데이트는 약 1년, AI 연구개발 자동화는 약 2년가량 지연될 것으로 추정했다. 이와 관련해 연구진은 해당 분야에서의 전망을 “다소 비관적”이라고 평가했다. 또한 앞선 AI 기업의 연산 자원 및 인력 확대 속도 역시 이전보다 느려질 것으로 내다봤다.
아울러 이번 모델은 급격한 도약이나 지나치게 느린 발전과 같은 극단적인 시나리오 가능성을 낮게 설정했다. 대신 AI 역량이 점진적으로 향상된다는 가정을 바탕으로, 단계적인 성장을 계산하는 방식으로 설계됐다.
연구진은 “이 모델은 우리가 중요하다고 판단한 핵심적인 역학과 요인을 반영하고 있지만, 모든 변수를 포함하고 있지는 않다”라고 설명하면서, 실제로 결과를 분석한 뒤 ‘직관과 기타 요인’을 고려해 추가 조정을 거쳤다고 밝혔다. 연구진은 결국 “이 모델뿐만 아니라 어떤 모델도 완전히 신뢰해서는 안 된다고 본다”라고 덧붙였다.
AGI는 일반적으로 사람과 유사한 수준의 인지 능력을 갖추고, 사람이 수행할 수 있는 거의 모든 작업을 해내는 AI로 이해된다. 그러나 레스롱 연구진은 곧바로 AGI로 도약하는 것이 아니라, 그 사이에 여러 뚜렷한 단계를 거치며 진화한다고 봤다.
예를 들어 ‘초인적 코더’ 단계 이후에는 AI 연구개발을 완전히 자동화할 수 있는 ‘초인적 AI 연구자’ 단계에 접어들어 사람 연구자의 역할이 대체된다. 이어지는 단계는 ‘초지능 AI 연구자’이며, 이는 AI가 사람 전문가를 앞서는 정도가, 사람 전문가가 평균 연구자를 앞서는 수준보다 2배 이상 높아지는 단계다.
그 다음은 거의 모든 인지 작업에서 최고 수준의 전문가와 동등한 역량을 가진 AI가 나타나는 단계다. 이 단계에 이르면 원격 근무 일자리의 약 95%가 AI로 대체될 수 있다고 연구진은 내다봤다.
최종 단계는 인공 초지능(ASI)이다. 이는 거의 모든 인지 작업에서 최상위 전문가보다 훨씬 뛰어난 성능을 보이는 또 하나의 도약 단계다. 연구진은 초인적 코딩 역량이 확보된 이후 약 5년이 지나면 ASI에 도달할 가능성이 있다고 설명했다.
레스롱 연구원 다니엘 코코타일로는 “향후 10년 내 AGI가 등장할 것이라는 가정은 매우 현실적이다”라고 평가했다. 그는 연구진이 AI 발전 과정을 여러 단계로 나눠 분석했으며, 마지막 단계는 현재 인류가 이해하고 있는 인간 지능의 한계에 근접한 수준이라고 설명했다. 연구진은 “이미 많은 AI 연구자가 AI가 자신의 연구 속도를 높인다고 인식하고 있다”라고 전했다.
다만 실제로 어느 정도까지 연구 생산성이 향상되고 있는지는 아직 명확하지 않다는 점이 지적됐다. 연구진은 “AI의 영향이 아예 없진 않지만, 현재로서는 매우 제한적인 수준일 가능성이 크다”라고 진단했다. 그러나 AI 역량이 고도화될수록 그 영향은 점차 커질 수 있으며, 궁극적으로는 AI 시스템이 인간을 ‘초지수적’ 속도로 앞지를 가능성도 배제할 수 없다고 분석했다.
그레이하운드 리서치의 수석 애널리스트 산칫 비르 고기아는 레스롱의 전망 변화가 기업 입장에서 중요한 신호라고 분석했다. 그는 이번 결과가 아무리 정교한 미래 모델이라 하더라도 피드백 루프, 수익 감소, 병목 현상과 같은 요인에 따라 예측이 얼마나 쉽게 달라질 수 있는지를 드러냈다고 설명했다.
고기아는 “이번 업데이트에서 중요한 것은 정확히 어느 해에 도달하느냐가 아니라, 이 분야의 예측이 실제로 얼마나 취약한지를 조용히 보여줬다는 점”이라고 평가했다.
그는 벤치마크 중심의 낙관론은 신중하게 다뤄야 한다고 강조했다. 타임 호라이즌 방식의 벤치마크는 발전 추이를 보여주는 지표로는 유용하지만, 기업의 실제 준비 상태를 판단하는 기준으로는 한계가 있다는 설명이다.
CIO 관점에서 보면 AI가 코딩을 할 수 있는지에 대한 논쟁은 이미 끝났다고 고기아는 지적하면서, 이제 기업이 결과에 대한 책임은 사람이 유지한 채, 개발 주기를 단축하기 위해 AI를 적극적으로 활용해야 한다고 말했다. 그는 최근 기업 사이에서 범위를 제한한 파일럿 프로젝트와 내부 도구 구축이 늘어나고 있으며, 통제된 자율성 아래에서 감사 가능성과 보안을 중시하는 접근 방식이 강화되고 있다고 전했다.
고기아는 앞으로 2~3년을 바라보는 기업의 ‘사고방식’을 바로잡는 것도 중요하다고 언급했다. 향후 핵심 변화는 완전한 자율 코딩으로의 전환이 아니라, 기업 전반의 업무 프로세스를 AI로 가속하는 데 있다는 설명이다. 그는 “가치는 사람을 제거하는 데서 나오는 것이 아니라 업무 흐름을 재설계하는 데서 나온다. 성공하는 조직은 AI를 기존 전달 체계를 대체하는 존재가 아니라, 규율 있는 시스템 안에서 효율을 증폭시키는 도구로 활용할 것”이라고 설명했다.
궁극적으로 그는 AI 시스템이 사람의 개입 없이도 복잡하고 규모가 큰 소프트웨어를 장기간 안정적으로 운영할 수 있는지 여부는, 같은 결과를 계속해서 만들어낼 수 있는지를 통해 판단하게 될 것이라고 분석했다. 그는 “그때까지 기업이 취해야 할 책임 있는 태도는 무조건적인 배제도 맹목적인 신봉도 아닌, 준비하는 자세여야 한다”라고 강조했다.
dl-ciokorea@foundryco.com
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