Artificial intelligence leads to the disappearance of traditional entry-level engineering positions in the IT sector

Despite the undeniable influence of artificial intelligence (AI) on modern society, there remains a sharp debate over whether its overall impact is positive or negative. Just a year or two ago, the internet was flooded with AI-generated images that quickly entered corporate advertising. Soon after, social media platforms like YouTube were filled with AI-generated thumbnails—visuals that initially attracted viewer interest but now increasingly cause audience fatigue and skepticism.

However, outside of consumer social media feeds, artificial intelligence holds significant potential to transform corporate operations, provided it is implemented strategically. In an exclusive interview with IOL, Paul Banting, co-founder of growthguru.ai, analyzed the common narrative that AI has become an overrated tool that does not deliver expected productivity growth.

Banting stated that the problem is not that AI is overrated, but that it is being judged against the wrong promise. He emphasized that productivity gains are real and supported by research. According to a McKinsey survey on AI adoption in corporations, 80% of respondents noted improved individual productivity, yet only 37% claimed that AI directly contributed to increased profit for their organization—a metric that has remained virtually unchanged over the past year.

Therefore, in Banting's view, it is more accurate to say that AI is not overrated, but fundamentally misunderstood. Leaders mistakenly assume that saved time automatically translates into revenue growth. In reality, companies must actively direct the hours freed up by AI toward achieving tangible, high-return commercial opportunities.

While most public attention naturally focuses on optimizing outdated workflows, Banting warns that task efficiency should not become the primary goal of the enterprise. Around 79% of executives expect AI to drive substantial revenue growth by 2030, compared to 40% today. Nevertheless, only 24% have a clear idea of where this future revenue will originate. The true objective, Banting explained, is using AI to identify urgent commercial gaps and capture high-value market opportunities ahead of competitors.

The Growthguru.ai platform was developed precisely to bridge this strategic gap, focusing on business development rather than general workflow automation. Instead of offering a single universal assistant, their platform utilizes specialized AI agents targeting specific commercial levers: Perky helps partners access relevant incentives, discounts, and co-financing from suppliers; Maya develops go-to-market strategies and co-marketing initiatives; Alex guides partners through certifications and supplier tiers; and Lexi tracks important enterprise updates, such as critical Microsoft platform releases.

Nevertheless, alongside the commercial potential lies a darker side—AI-enabled crime, where issues of accountability and legal liability remain unresolved. Banting argues that criminals using AI for financial fraud must be held responsible for their actions, regardless of the tools they employ. However, if proprietary or third-party enterprise AI causes operational or financial damage, the responsibility lies directly with the business that implemented it. Legal liability cannot simply be shifted to the model provider. While software developers must create secure and compliant architectures, the choice of implementation rests entirely with the enterprise. BCG confirmed this finding in its applied AI benchmark.

For companies implementing artificial intelligence, key questions are not only about choosing a model but also about where the AI operates, its cost, and the ability to keep confidential data under the company's control. PeakVision AI offers a solution that involves moving most of the AI tasks inside the enterprise.

The company's system considers the task, rather than an individual application, as the main unit of work: users state a goal, and AI agents perform the necessary steps in a local computing environment. Co-founder and CTO Liangyu Nie told TechNode that PeakVision AI was founded in 2024, overcoming two major obstacles to AI adoption in corporations: the complexity of technology deployment and concerns over data security.

In March 2026, the company opened its headquarters in Hangzhou and received government support for research and development amounting to 15 million yuan. In May, it presented its first enterprise systems and completed an initial funding round, followed by a second round worth tens of millions of yuan in August.

PeakVision AI creates an enterprise AI stack that integrates locally deployed computing, FengKan AIOS, and AI agent services. This system is designed to focus on how agents complete tasks, rather than how people move between different applications. As Nie noted, users no longer need to load different applications; they just need to tell the AI what they want via a chat window, and it will do the work.

The startup's central thesis is that enterprise software will increasingly be structured around intent and execution, rather than menus, files, and separate applications. The company plans to make FengKan AIOS open source by the end of October. PeakVision AI monetizes through hardware, subscriptions, and custom enterprise AI implementations. The company's broader goal is to transform project-specific work into universal solutions for various industries.

FengKan AIOS supports third-party hardware, while PeakVision AI takes a selective approach to its skills ecosystem. Nie clarified that users' end skills, which may contain proprietary workflows, will remain untouched. However, more generalized skills created by partners and leading engineers can be released to the market.

Currently, the company is testing its model through the FDE academy, launched by Shanghai Sixunited Intelligent Technology with support from Advanced Micro Devices (AMD). AMD provides computing hardware, technical toolchains, and resources for projects. PeakVision AI also collaborates with chip manufacturers in early product development stages to optimize costs and performance, including a partnership with ChangXin Memory Technologies to reduce memory costs.

Unlike companies such as Wonderful, which target large, often regulated organizations and provide them with AI platforms and implementation teams, PeakVision AI targets small and medium-sized businesses. Nie emphasized that these enterprises face the same problems—costs, technical complexity, and data security—but have fewer resources to solve them. PeakVision AI uses inexpensive hardware from the Chinese supply chain so that each implementation becomes a repeatable solution for similar clients.

Nie stated:

Job applicants spend time improving their resumes, tailoring cover letters to job requirements, and matching their skills with company needs. After submitting documents, there is often a period of silence, after which they may try again or use artificial intelligence to enhance their application.

On the other hand, recruiters can use AI to analyze hundreds of resumes. Since candidates strive to stand out, and companies want to quickly find a suitable person, the use of the same technology is growing both in the application process and in hiring, turning this procedure into a machine versus machine competition.

AI is capable of simplifying the job search for applicants by helping write resumes, prepare applications, and practice for interviews. However, a rapid increase in the number of prepared applications does not guarantee an increase in their quality.

According to a September 2025 report, one applicant sent 87 applications in a month using AI. Errors were found in these documents, such as incorrect company and job titles. The report warns that AI-generated materials without verification can harm candidates.

When applicants apply for numerous vacancies, the volume of incoming applications for companies increases. Although automated screening seems useful for companies, the question arises as to which candidate will be paid attention to in this process.

Monitoring systems allow companies to organize resumes and find candidates whose experience matches the job requirements. Nevertheless, if the candidate's necessary competencies are described in words that the system does not recognize, they may be ignored.

A October 2025 report details how monitoring system tools match resumes with job requirements. The risk is not limited to missing keywords.

In May 2024, concern was raised that qualified candidates might be rejected by automated hiring systems, with the report emphasizing the need for human oversight.

If the rejection decision is made solely by software, a suitable candidate will never reach the recruiter, and the applicant will not know the reason for rejection.

This pressure is not limited to India. A BBC report mentioned a 22-year-old geography graduate from Medstone, UK. Before getting a restaurant manager position, he submitted over 200 applications. He noted that AI helped in processing applications, but the entire process was exhausting.

Business Insider reported that Google integrated AI tools, including Gemini, into some software development interviews. During these interviews, candidates' ability to understand code and work with AI was tested.

These examples show that applicants are using AI for job searching, while companies expect them to demonstrate AI-related skills. This raises the question: will the technology make the hiring process more efficient or add another level of competition?

Even after submitting an application, applicants may wait without knowing whether their application is being considered. According to a September 2026 report based on the Indeed hiring confidence index, 29% of Indian applicants reported that lack of response after applying is their biggest problem.

The study involved 1073 applicants and 735 employers from India. Simply speeding up screening will not solve this problem; candidates also need clear information and the ability to track the status of their application during the hiring process.

The task for applicants is not to send template applications when using AI and not to claim non-existent skills. The task for companies is not to consider recommendations from fully automated technologies as the final solution.

AI can help candidates better present their experience, and recruiters can manage workload. However, an attractive resume and quick screening do not guarantee that a suitable candidate matches the right job.

For a person waiting for a response after applying, the most useful change will not be a new tool to speed up applications, but a more transparent and impartial hiring process that can identify the person behind the resume.