Just like Apple and Duolingo, Google has now chosen to sever ties with its third-party contractor, Appen, instead of taking the layoff route. This also signals a broader trend of companies tightening their belts on AI training data expenditures. The shift away from renewing contracts stems from a preference among AI model developers for special generative AI services over traditional manual methods.
Appen, once a linchpin in training some of the world’s premier AI systems for Adobe, Salesforce, and Nvidia, faced a downturn in its fortunes following a change in leadership. The departure of long-time CEO Mark Brayan, who steered the company for seven years, marked a turning point. The reins were handed over to Armughan Ahmad, a tech veteran with 25 years of experience, yet this transition precipitated a series of executive exits.
Despite boasting a global platform of over 1 million freelance workers in 170 countries, Appen has witnessed a 13% revenue decline in 2022. Citing “challenging external operating and macro conditions”, the company struggled to adapt to the burgeoning era of generative AI.
Former employees, choosing anonymity due to fears of reprisal, shared insights with CNBC, attributing Appen’s current challenges to years of lax quality controls and a disjointed organisational structure. As generative AI’s prominence grows in the tech industry, Appen and similar companies are finding themselves at the crossroads, navigating the consequences of their AI shortcomings.
Automation at Work
For the early days of AI models, manual data collection processes have led contractors like Appen to thrive, but the scene has dynamically changed with the rise of large language models (LLMs). These human-mimicking AI systems – namely Google’s Bard and OpenAI’s ChatGPT – can now scour data from the digital infrastructure to answer queries and even generate images in response.
In the recent turn of events, these language model companies have started spending less money on outsourcing work through firms like Appen. Instead, they are laser-focused on resetting their business through automated services provided by Labelbox and Scale AI, which specialise in generative AI.
Just a mere four months back, Google Cloud decided to lengthen its partnership with Labelbox, a platform that rolls out a suite of tools tailored for AI techniques like reinforcement learning with human feedback (RLHF), reinforcement learning from AI Feedback (RLAIF), evaluation, and red teaming.
Meanwhile, another player in the field, Scale AI, has been solidifying its standing with a clientele that reads like a who’s who of top-tier companies including Toyota, Instacart, PayPal, and Flexport. Scale AI has been making waves, providing bespoke data solutions to navigate the intricate waters of the machine learning lifecycle. The competition in this field is nothing short of a star-studded affair and is only going to get tougher form here.
Excuses Excuses
Google pivoting from Appen to Labelbox is a classic example of why companies should be quick and not hesitate to integrate generative AI and evolve with the landscape. The demand for automated services powered by AI is rising as it’s cheaper, faster and involves fewer problems.
Kim Stagg, Appen’s vice president of product, has publicly declared that the work required for generative AI services was different from what the company had previously needed. The company appears to be in no position to board the generative AI boat and has been left behind.
While the Appen executives have conveniently blamed AI, former employees have argued that the problem lies with the organisation’s structure and shuffling of executives. The pattern is seen in several companies that use generative AI as an excuse to hide their internal mishandled problems.
Cory Stahle, an economist, has clarified that AI tools are not yet sophisticated enough to replace workers entirely. According to Stahle, if AI were really the culprit, layoffs would be far more widespread across diverse industries. “And we haven’t seen that happen yet,” he said.
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