Is Artificial Intelligence (AI) Coming for Your Job?
This headline has dominated news outlets and board discussions alike over the past two years. It’s well documented, and touted as a strength, that AI makes manual, often time-consuming tasks like document drafting and data entry exceptionally more efficient. This has duly prompted growing uncertainty amongst the human labor force – especially for those that work in tech, legal, managerial, and financial sectors – who are watching AI’s labor replacement capabilities strengthen by the entry. Amidst its rapid evolution, companies are considering how AI might carry out advanced decision-making at a lower cost than human labor, forcing them to confront their workforce growth plans earlier than they might have planned to. However, anticipated cost savings from AI are not certain due to rising computing expense, so rather than blanket human labor replacement with the expectation of enhancing EBITDA, companies should be strategic in how they deploy AI and consider reshaping automation prone roles through upskilling employees instead. Taking this sentiment even further, companies that go forward with short-term workforce reductions, particularly at the entry-level, risk undermining long-term talent development and weakening future leadership pipelines.
AI’s Job Replacement Potential is Not as Grave as the Headlines Portray
It’s commonly stated that today, AI is performing as poorly as it ever will. Its full potential has yet to be unlocked, posing major challenges to grasping AI’s impact on the future of any job’s responsibilities across industries.
Some AI leaders have posited widespread elimination of white-collar jobs in the near future: in May 2025 Dario Amodei, CEO and co-founder of Anthropic, predicted that AI could wipe out half of all entry-level, white-collar jobs, and spike unemployment up levels of 10-20% in the next five years. In July 2025, Sam Altman, CEO of OpenAI uttered similar sentiments, arguing that AI will eliminate entire job categories.
AI capabilities are rapidly expanding, and public sentiment is shifting just as quickly, exacerbating our ability to fully understand its potential impact on the labor market. Even still, it’s difficult to ignore the headlines highlighting mass layoffs linked to AI adoption. In the first half of 2026, the global tech sector experienced over 158,000 AI-related layoffs (as of June 2026). Despite having its best revenue growth in 15 years, Oracle laid off 21,000 employees in May 2026 to free up capital for AI investment and development – approximately 13% of their workforce. Meta simultaneously conducted two rounds of layoffs, the first affecting 8,000 employees and the second impacting 1,395, attributing this reduction in force to its massive AI investment goals. Coinbase and Wix made similarly scaled layoffs, impacting 15% and 20% of their workforces respectively within the same timeframe.
While the surge of layoffs is alarming, the reality is that they are concentrated in the tech sector amongst larger, publicly traded companies. Industry leaders have changed their outlook on AI’s potential to replace human labor, with OpenAI’s Altman rolling back his previous statements asserting that the “job apocalypse” he once predicted has not occurred, highlighting the desire for genuine human interactions as the main contributing factor. Similarly, in March 2026 Anthropic published research that indicated there has been no systematic increase in unemployment for highly exposed workers (e.g., computer programmers, customer service representatives, information security analysts). These recalibrated stances on AI and labor displacement can be corroborated by an April 2026 Boston Consulting Group study which found that 50-55% of jobs will be reshaped – rather than replaced – by AI, with companies placing a greater emphasis on human labor, including their judgment and oversight, and enhancing AI fluency to augment employees responsibilities beyond repetitive tasks.
AI Pricing Models Soar with a Move Toward Token-Based Pricing
The promises of increased productivity and cost savings are driving large scale AI investment and use. To date, we’ve seen accessible, subscription-based pricing models help to power this widespread adoption – however this lower barrier to entry subscription service is no longer profitable. While users used to pay a flat fee for usage caps, AI providers like Anthropic are shifting instead to token-based pricing, charging a fixed seat fee for platform access with additional usage charges based on token consumption to account for AI’s increasingly complex and variable applications.
Tokens are the basic data units, such as words, image pixels, and audio snippets, used for AI model processing and usage calculations. With token-based pricing, AI costs are determined by how much text is processed, both from user prompts and generated outputs (e.g., 75 words cost approximately 100 tokens). The pricing model closely mirrors utilities, like utilities, charging based on consumption, which signals a shift from AI as a consumer tool to an infrastructure layer. The table below highlights the input and output costs of two different GPT models and two different Anthropic models.
Companies are beginning to feel the financial strain of AI token usage. Microsoft engineers were told to stop using Claude because the tokens were costing the Company too much money. Similarly, Uber spent its entire 2026 Claude Code token budget in four months. Perhaps most surprisingly, token-based charges have upended widely held beliefs that AI is cheaper than human labor. Nvidia’s Vice President of Machine Learning found that the cost of AI computing is “beyond the cost of employees.” Even if companies are willing to pay the lofty token-based pricing, such tools only replace a fraction of the employees’ roles.
Strategic Analysis Around AI Deployment and Human Labor
We’ve reached a critical inflection point for private market leaders: value creation opportunities increasingly depend on effective AI deployment, and as token-usage billing models increase costs companies will need to think more strategically to generate the best return on investment (ROI).
Such a strategy includes embedding AI use-case ROI considerations into both the diligence process and operational turnaround period, whether through large-scale applications (coding, large data set analysis), workflow efficiencies (document drafting and summarization), or a combination of both. Private market investors can also consider conducting AI-to-human labor analysis as part of diligence and value-creation planning. Such a process can identify opportunities to reduce costs, enhance productivity, and improve scalability. The resulting insights can help to determine if capital is better deployed towards workforce growth or AI acceleration.
Tracking Productivity and Efficiency Gains from AI Lacks Standardization
Improvements in productivity and efficiency resulting from AI use are often difficult to quantify. Industry experts have noted two major phenomena for accurately tracking AI gains: Shadow AI and the Productivity Paradox. IBM defines “Shadow AI” as the independent adoption of outside AI platforms (i.e., those that are not approved for enterprise use). An IBM-sponsored study revealed that while 80% of American office workers utilize AI, only 22% exclusively use AI tools that are provided and approved by the company, and 58% use unapproved tools making it difficult for employers to track AI usage, limiting visibility into productivity and efficiency gains.
While Shadow AI use is relatively new, the roots of the Productivity Paradox sit much deeper, originally materializing between the 1970s and 1990s. The Productivity Paradox posits that as greater investments are made into newer technologies, employees’ productivity may actually decrease instead of increase. Individual employees can report increased productivity, but if companies are not seeing productivity gains as a whole, can they continue to justify the increased costs of investing into AI?
Lack of robust measurement frameworks paint an incomplete picture, and the inability to track the long-term workforce implications of AI adoption may lead to companies overestimating the benefits of AI and making premature adjustments to their workforce size and plans, including replacement of entry level hires with AI.
Accurately monitoring gains in productivity from AI can be difficult to say the least, but comprehensive frameworks collect key indicators including task-completion time, error rates, and changes in output quality over time to distinguish task efficiency and output quality pre- vs. post- AI adoption.
Without Junior Talent, Companies May Lack a Stable Leadership Pipeline
Although junior roles often center on repetitive tasks suited for automation in pursuit of short-term cost savings, companies may gain greater long-term value by using AI to augment – rather than replace – early-career talent. Hiring entry-level roles plays a critical role in maintaining company culture, preserving institutional knowledge, and developing future company leaders – areas where AI cannot meaningfully compete.
With roughly half of U.S. jobs expected to be reshaped by AI and only 12% expected to be replaced, these projections suggest that companies have an opportunity to redesign entry-level roles rather than eliminate them. By automating routine tasks, AI can shift early-career work toward higher value contributions, accelerating the development of judgement and decision-making skills that define future leaders, provided that firms treat AI as a complement to human labor rather than a substitute.
Conversely, companies that prioritize short-term cost savings by replacing junior talent with AI risk a critical gap in experienced leadership 5-10 years from now. Maintaining entry-level hiring is crucial to preserving institutional knowledge and culture. Employees who begin their careers in junior roles develop a deep understanding of unique company processes, culture, and decision-making practices. As they progress into more senior positions, this knowledge helps shape the next generation of employees. Without a steady pipeline of junior talent moving up the ladder, companies can expect to become increasingly reliant on external hiring to fill senior roles, losing out on institutional knowledge gained over time and potentially weakening long-term stability.
The Solution: Coupling Thoughtful AI Deployment with Investment in Junior Talent
Under stakeholder pressure to drive efficiency gains, reduce costs, and increase returns, companies may view entry-level headcount reductions as a quick win. However, this short-term thinking can carry long-term consequences in the leadership pipeline, which necessitates a more strategic approach to adopting AI. According to FTI Consulting’s 2026 Private Equity AI Radar, as AI automates routine tasks, three distinct needs emerge: 1) shifting workforce capacity toward higher value work, 2) augmenting delivery practices using embedded AI, and 3) building AI governance and integration capabilities. These capabilities especially matter in the context that lagging AI literacy is a major barrier to scaling AI across companies. Without the internal expertise to govern and integrate AI tools effectively, companies risk slowing their own adoption curve. Companies should therefore treat early-career hiring as a value-creation lever rather than a cost line; junior employees provide a critical pipeline for developing the AI literacy and organizational capabilities needed to govern and integrate these tools at scale.
In practice, companies can cultivate AI expertise internally through targeted trainings, mentorship programs, rotational assignments, and opportunities for employees to integrate AI into their day-to-day work. Over time, this approach can help build institutional knowledge and create a scalable foundation for further adoption of AI. To evaluate whether these efforts are strengthening the talent pipeline, companies can track talent development metrics (e.g., promotion rates, early-career retention rates) to assess how AI adoption is affecting workforce development and long-term value. Companies that successfully integrate AI into the core of their businesses while continuing to invest in early-career talent will be better positioned to realize productivity gains today while developing the leaders needed for tomorrow.
Conclusion
As AI adoption accelerates across industries, the question for companies is not whether to integrate these tools, but instead how to deploy them in ways that maximize long-term value. While near-term gains are often framed in terms of cost reduction and task automation, the long-term impact of AI will depend on how firms redesign work, develop talent, and build organizational capabilities around these technologies. For private market investors in particular, this means evaluating AI not only as a productivity tool, but as a structural force that is reshaping workforce composition and skill requirements. The firms that succeed in the long-term will be those that use AI to reallocate human labor toward higher-value tasks, with an emphasis on early-career talent pipelines. This assessment reflects a long-term view of the current state of a rapidly evolving AI landscape, with the expectation that the balance between automation and human labor will continue to shift as AI capabilities advance.
Authors and Contributors
Emily Goldstein-McGowan
Emily is a Vice President and leads Malk’s Growth Equity and Venture practice, partnering with GPs and portfolio companies to develop ESG programs that support long-term value creation and risk mitigation. Emily works directly with 15+ technology focused GPs on bespoke approaches to value creation and portfolio engagement. Emily has managed 300+ ESG due diligence transactions and 100+ monitoring engagements directly with portfolio companies. Emily earned her bachelor’s degree in Sociology from Barnard College.
Abigail Flatau
Abigail Flatau is a Consultant at Malk Partners, where she works on the firm’s Growth Equity and Venture team, conducting ESG due diligence on target investments, performing ESG monitoring on portfolio companies, and providing investors with fund advisory support. Prior to Malk, she spent three and a half years in asset management, managing responsible investing regulatory requirements, as well spearheading the firm’s responsible investing reporting, marketing, and communication efforts. She graduated magna cum laude from Skidmore College, where she earned a bachelor’s in International Affairs and Psychology, along with a minor in French.
Sam Rice
Sam Rice is an Associate at Malk Partners, where he advises private equity investors on ESG risks and value creation opportunities throughout the investment lifecycle. He recently graduated from Santa Clara University, earning a bachelor’s degree in Philosophy with a minor in Economics.
Kerin Debany
Kerin Debany is an Associate at Malk Partners, where she works on the firm’s monitoring team, conducting routine ESG due diligence on portfolio companies. Prior to Malk, she graduated summa cum laude from the University of Richmond where she earned her bachelor’s in Global Studies and Spanish with a minor in Sustainability.
Malk Partners does not make any express or implied representation or warranty on any future realization, outcome or risk associated with the content contained in this material. All recommendations contained herein are made as of the date of circulation and based on current ESG standards. Malk is an ESG advisory firm, and nothing in this material should be construed as, nor a substitute for, legal, technical, scientific, risk management, accounting, financial, or any other type of business advice, as the case may be.