“Silicon Valley AI Engineers” Struggle With 996 Coding Projects Cut, Overnight Work to Please Investors

Explore the demanding world of Silicon Valley AI engineers facing tough work schedules, project cuts along with intense pressure to innovate quickly.

The programming efforts by Amazon engineers during a weekend were ultimately in vain due to the project being deprioritized. The rise of AI has been shadowed by considerable internal discord among Silicon Valley workers.

The increasingly packed event calendars, ever more ambitious deadlines, and futile AI product presentations for the board… AI engineers at major corporations, who are compelled to participate, are increasingly feeling overwhelmed.

As AI gains traction, Silicon Valley engineers are becoming exhausted and discontent due to relentless internal competition.

Sacrificing a weekend to write code that ends up unused because the project’s priority was lowered is disheartening.

The race to outpace competitors in product releases is relentless, focusing solely on speed. Executives issue commands with fervor but pay little heed to the projects’ actual impacts.

In the rush to advance AI projects, inexperienced and untrained staff are recruited hastily; meanwhile, others frantically waste time as deadlines approach. Even when technical experts are available, there’s seldom an opportunity to learn from them…

These are the bizarre realities in Silicon Valley companies amid the boom in generative AI.

The cycle of internal competition continues unabated.

For instance, last year, an Amazon engineer planned a relaxing weekend after weeks of intense work.

amazon funds anthropic

Yet, plans changed when a Slack message arrived: his manager required a project completion by Monday morning at 6 a.m.

His weekend was ruined, necessitating the cancellation of social plans to work tirelessly.

Ultimately, the project’s priority was lowered, rendering his efforts futile… This scenario was not unfamiliar to him.

AI experts often hasten to develop new features, which are frequently put on hold to address the urgent demands of other AI projects.

The Amazon engineer mentioned that he had written thousands of lines of code for new AI features in a setting lacking error detection.

However, the susceptibility to errors without adequate testing sometimes forces team members to coordinate late-night fixes for AI software issues.

In his view, Amazon’s upper management prioritizes an aggressive “I want it all” stance, but as they rush to mimic products from Microsoft and OpenAI, maintaining quality becomes a challenge.

Foreign media have likened these AI engineers, caught in the intense internal competition of large companies, to participants in a “Rat race.”

In this “rat race,” individuals are like rodents chasing a cheese reward, endlessly running in circles, leading a monotonous life devoid of rest.

The confusion is palpable among engineers at Google and Microsoft, who fret about falling behind their rivals and feel compelled to launch tools swiftly.

NVIDIA CEO Jensen Huang insights on ai

Nvidia CEO Huang described AI’s current phase as its “iPhone moment,” indicating immense pressure across Silicon Valley.

Project timelines are incessantly accelerated, with each AI release driven by a desperate attempt to lead the competition. Ironically, leadership often disregards the real impact of many projects.

This is not the practice of a specific company but a prevailing trend across the industry.

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A Google employee noted that after six months of intense work, she hoped for a break.

Yet, under the company’s “building aircraft while flying” strategy, the pressure only intensified.

An Amazon AI engineer reported that to catch up with a delayed project, his team was hastily assembled. However, the team members lacked both experience and relevant training…

To motivate employees, management frequently delivers “inspiring” speeches:

 "Your work will revolutionize the industry!"

Engineers and other employees increasingly feel their work is more about meeting investor expectations and maintaining competitiveness, rather than addressing genuine user needs.

Moreover, in the quest for rapid development, employers often overlook the repercussions of oversight and other potential negative effects of AI.

Employees generally suffer from burnout due to long hours, immense pressure, and ever-changing job requirements.

Many have opted to leave the AI sector or seek new employment, unable to endure the high-pressure, fast-paced work environment.

This is the darker side of the generative AI gold rush.

To stay competitive in a market projected to generate over $1 trillion in revenue over the next decade, tech companies are feverishly developing various chatbots, AI agents, and image generators, pouring billions into training large language models. Meanwhile, Silicon Valley company employees are struggling to breathe.

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Executives at major tech companies are openly discussing with investors and employees the significant impact of AI on their strategic decisions.

Microsoft CFO Amy Hood highlighted in this year’s financial report call that the company is reallocating its workforce to prioritize AI, with continued investment in the area as it is

“crucial for shaping the next decade.”

Similarly, Meta CEO Mark Zuckerberg devoted most of his recent earnings call to discussing products, services, and the latest developments with Llama 3.

“I am convinced that over the next few years, we should invest heavily in developing more advanced models and the world’s largest AI services,”

Zuckerberg stated.

At Amazon, CEO Andy Jassy told investors that the opportunities in generative AI are unparalleled, thus necessitating increased capital investment to seize this moment.

“I believe that we in technology have rarely seen such opportunities, at least since the onset of cloud computing, or even since the birth of the internet,”

Jassy said.

In the AI race today, these major companies are not only laying off employees but also striving to recruit more AI experts.

Eric Gu, a long-standing Apple employee who had been involved in key projects like the Vision Pro headset, felt his growth was severely limited. Despite being surrounded by talented individuals, he had no opportunity to learn from them.

“Apple is extremely product-focused, so we are constantly under immense pressure to work efficiently, launch products swiftly, and introduce new features…”

This relentless pace overwhelmed Eric Gu.

Approximately a year ago, he decided to leave Apple and join AI startup Imbue, where he could engage in ambitious projects at a more reasonable pace.

A Microsoft AI engineer also revealed that the company is ensnared in the intense AI competition.

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Furthermore, in its rush for speed, Microsoft has overlooked ethical and security considerations, launching products prematurely without adequately assessing potential consequences.

He also noted that since all major tech companies have access to nearly identical data, there is essentially no real competitive advantage in the AI field.

Indeed, Morry Kolman, an independent software engineer and digital artist with over 200,000 popular projects, noted that with the rapid advancement of AI technology, it is challenging to discern which areas are worth investing time in.

This often leads to professional burnout, as maintaining sustained enthusiasm becomes increasingly difficult.

At Google, an AI team member mentioned that burnout primarily stems from competitive pressures, tighter schedules, and resource constraints, particularly in terms of budget and staffing.

Despite many leading tech companies proclaiming increased investments in AI, achieving the necessary manpower within tight timelines remains a challenge, even for Google.

The rushed production has led Google to experience several embarrassing setbacks.

The Gemini image generation tool was hastily removed from service shortly after its launch in February this year due to a historical error.

In early 2023, Google employees also criticized the company’s leadership, particularly CEO Pichai. It was clear to observers that Google’s rushed launch of Bard, intended to compete with ChatGPT, was mishandled.

This long-serving Google employee, who has been with the company for over a decade, mentioned that not only that, the industry generally is cutting costs, and many companies have resorted to large-scale layoffs to meet investor expectations and boost net profits.

The dense meeting schedule also places immense pressure on the team.

The AI team’s schedule includes the Google I/O developer conference in May 2023, Cloud Next in August, and another Cloud Next conference in April 2024.

These events are occurring more frequently than before, placing significant pressure on a team that needs to deliver features according to the meeting timeline.

The same pressure is felt in government agencies and startups.

An AI researcher at a government agency noted that although the government is typically slow to act, he still feels the urgency to catch up quickly because the influence of generative AI has now permeated all sectors.

The same holds true for startups. Ayodele Odubela, a data scientist and AI policy consultant, mentioned that some startups, buoyed by large venture capital investments, are working overtime to capitalize on this trend. These investors expect returns of up to ten times their investment.

Using AI for the sake of using AI

Additionally, a significant portion of the work of AI engineers in large companies involves employing AI simply for the sake of using AI, rather than solving business problems or directly serving customers.

A Microsoft AI engineer noted that many of the tasks he encounters are merely contributing to the AI hype and lack practical application.

For instance, even when problems that clearly do not involve generative AI are addressed using large language models, this approach can result in lower efficiency and increased costs.

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Another software engineer at a large Internet company was transferred to a new LLM research team simply because “AI is too popular.”

This engineer, with extensive experience in machine learning, believes that current work in the field of generative AI is rife with empty promises and excessive hype.

From the outside, it may seem like significant progress is made every two weeks, but in reality, the same tasks are repeatedly performed.

For example, he often has to prepare new AI product demonstrations for the company’s board of directors within three weeks, even though these products are effectively “useless.”

Furthermore, to appease investors and secure funding, he also developed a web app. Naturally, this had little to do with the team’s actual work, and the app remained unused after the demonstration was completed.

A product manager at a fintech startup mentioned that senior management was eager to launch AI-enabled solutions, yet they lacked a clear understanding of the problem.

For instance, one of the projects he was involved in entailed repackaging an algorithm the company had long used as “artificial intelligence.” Additionally, he developed a ChatGPT plugin for customer use.

An AI engineer at a retail monitoring startup mentioned he is the sole AI engineer in a company of 40 people.

Here, in addition to managing all AI-related tasks, he also faces “impossible demands” from investors who lack an understanding of AI.

Now, overwhelmed, he is contemplating quitting his job to pursue a master’s degree, then conducting independent research and publishing his findings.

Too fast and prone to errors

As previously mentioned, due to the pressure to launch products quickly, major manufacturers have reduced regular testing and postponed the verification of AI accuracy.

However, AI projects rushed to keep up with competitors can easily falter. For instance, Google’s Gemini image generation tool has been involved in “discrimination” controversies more than once.

For example, when asked to generate “German soldiers in 1943,” it produced images of individuals of various skin colors in German military uniforms from that era.

google gemini ai image generation error

When generating “19th Century US Senators,” it even depicted black, Latino, and indigenous women simultaneously.

gemini ai image generation error

However, the first female U.S. Senator was a white woman who assumed office in 1922. This clearly overlooks the actual history of racial and gender discrimination.

In this context, Odubela stated that as the pace of AI technology development accelerates, careful consideration and thorough evaluation are more crucial than ever, but some large companies not only disregard this need, they are actively doing the opposite.

Source: CNBC

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“Silicon Valley AI Engineers” Struggle With 996 Coding Projects Cut, Overnight Work to Please Investors

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