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There is a particular kind of corporate announcement that tries to play it safe and be two things at once – a show of strength on one end, and an admission of reality on the other. Coinbase seems to have joined the group.The crypto exchange company announced it is cutting approximately 14% of its workforce. According to Forbes, that is roughly 700 employees, just days before its first quarter 2026 earnings report on May 7. The restructuring is being framed as an AI efficiency play. But the timing, in a down crypto market with trading volumes falling significantly despite Bitcoin trading slightly above $81,000, makes it clear this is also a cost-survival move.Coinbase (COIN) gained 4.1% on Tuesday, May 5, 2026,  following the announcement, briefly reaching an intraday high of $208 before pulling back below $200 at close. The market’s initial enthusiasm gave way to skepticism, and the stock ended lower. Earnings on May 7 will be the first real test of whether the restructuring signals a smarter, leaner Coinbase, or a company under pressure.Co-founder and CEO Brian Armstrong didn’t dress up the moment. “We are adjusting early and deliberately to rebuild Coinbase to be lean, fast, and AI-native. We need to return to the speed and focus of our startup founding, with AI at our core,” Armstrong wrote on X.Why Coinbase is restructuring around AI, and what it means for how the company will operateArmstrong cited two forces driving the decision: market cyclicality and the accelerating capabilities of artificial intelligence (AI).The AI rationale is not window dressing. The restructuring will eliminate layers of management and eliminate the concept of “pure managers” entirely. Every leader must be a contributor. Armstrong described the new model as “player coaches, getting their hands dirty alongside their teams.”More Layoffs:E-commerce giant shuts down office as layoffs riseOracle signals massive AI opportunity as layoffs hitAI won’t trigger mass layoffs yet, Fed saysThe new structure will concentrate around what Armstrong called “AI native talent who can manage fleets of agents to drive outsized impact.” Coinbase is also experimenting with significantly reduced team sizes, including single-person pods, where engineers, designers, and product managers collapse into one role, according to Armstrong’s blog post.”The pace of what’s possible with a small, focused team has changed dramatically, and it’s accelerating every day,” Armstrong wrote.The restructuring drew immediate pushback from users who raised concerns about non-technical staff shipping production code. According to Yahoo Finance, a 2025 data breach that exposed 69,461 Coinbase accounts remains fresh in customers’ memories, and the prospect of AI-generated code being deployed more broadly has amplified trust concerns. Armstrong responded directly, stating that all AI-generated code passes human review before deployment.

COIN has received 14 Buy ratings, seven Hold ratings, and three Sell ratings in the current month.LightRocket via Getty Images

What Wall Street expects from Coinbase’s May 7 earnings amid the restructuringThe restructuring announcement lands just before one of the most consequential earnings prints in Coinbase’s recent history.Coinbase analyst consensus for Q1 2026 projects:Revenue of approximately $1.50 billion, with a range of $1.39 billion to $1.77 billionEarnings per share of $0.10, with a wide range of $0.77 loss to $0.96 gain. The previous quarter’s EPS was a loss of $2.49, and revenue for that quarter came in at $705.93 million.
Source: TipRanks
The sequential revenue jump from $705.93 million to a projected $1.50 billion reflects the surge in crypto market activity that defined the first quarter. But trading volumes have since softened meaningfully, raising questions about whether Q1 represents a peak or a plateau.Related: Goldman Sachs cuts Coinbase target as outlook turns cautiousAnalyst sentiment heading into the print is broadly constructive. According to TipRanks, COIN has received 14 Buy ratings, seven Hold ratings, and three Sell ratings in the current month. The average analyst price target over the past three months sits at $260.60,  implying significant upside from the current price of $197.75 May 5 closing bell.What the Coinbase restructuring signals about where crypto companies are heading in 2026The Coinbase move is not happening in isolation. Across the technology sector, companies are using AI capability as justification for workforce reductions, compressing headcount while arguing that smaller, AI-enabled teams can deliver equivalent or greater output.For Coinbase specifically, the bet is that AI-native infrastructure can sustain growth through a crypto down cycle without the overhead costs that made previous downturns so painful. The company emerged from the 2022 crypto winter with a much leaner operation than it entered. Armstrong is trying to repeat that playbook before the pain gets worse.The risk is real. User trust is already strained. A major data breach from 2025 is not forgotten. And telling customers that AI is now more deeply embedded in the code and runs financial accounts requires more than a blog post to reassure. The May 7 earnings will answer the financial questions, but the trust question will take a little longer to resolve.Related: Cathie Wood buys $28.7 million of tumbling megacap stock

The AI trade has been easy to understand so far. Investors piled into chipmakers and model developers, betting that demand for compute and large language models would define the next cycle of growth. That bet worked.But it is also incomplete. A quieter and potentially more consequential shift is beginning to take hold across enterprise budgets. And most investors are not paying enough attention to it yet.Phase one was about speed, not disciplineThe first phase of the AI cycle was defined by urgency. Companies rushed to experiment with generative AI, launching pilots as quickly as possible. Cost discipline took a back seat. Enterprises were willing to spend aggressively just to understand what AI could do.That willingness shows up in the numbers. Global AI spending is forecast to total $2.5 trillion in 2026, according to Gartner. AI infrastructure alone accounts for $401 billion of that, as technology providers race to build the foundations enterprises need to run AI at scale.But experimentation has limits. Eventually, prototypes have to become production systems. And that is where the economics start to change.Running AI is a fundamentally different problemDeploying an AI model is not the hard part. Keeping it running efficiently is.”The bill that surprises most enterprises isn’t the one from launching AI. It’s the one from running it,” Guilhem Tesseyre told TheStreet. Tesseyre is the CTO and co-founder of Zencore, a Google Cloud Premier Partner founded by former Google engineers that has delivered more than 300 AI and cloud projects across over 200 enterprise customers.Related: Elon Musk has a shocking message on AI and robotsThe surprise comes from multiple directions simultaneously. Serving AI models requires round-the-clock availability for inference, even when those resources sit idle. Maintaining multiple model versions across geographies adds management overhead that compounds fast. When an enterprise AI solution gains traction internally, usage patterns shift quickly, and without proactive planning, operating costs inflate before anyone catches them.Data operations add another layer. Enterprises building proprietary models are managing massive datasets across duplicated storage, cross-region data movement, and multiple networks. Each adds cost that compounds quietly. Token usage, without proper monitoring, can spiral in scenarios involving large context windows such as codebases or extended video formats.One in five organizations misses its AI spend forecast by more than 50%, according to CloudZero. AI-native companies, the organizations most immersed in AI, have the worst forecast accuracy of any segment: 36% miss by 50% or more. The most committed organizations are also the most surprised by what it costs to sustain.The architecture problem enterprises did not see comingThe cost surprise is not just a finance problem. It is an engineering problem with financial consequences.”The biggest mistake enterprises make is trying to fit AI into a process that wasn’t designed for it. The smarter move is to redesign the process with AI in mind from the start,” Tesseyre said.More AI:Micron sits at the center of a red-hot chip rallyIBM CEO sends blunt message on AI and quantum computingAnthropic CEO makes shocking admission about AIMost enterprise cloud environments were built for traditional applications where demand is predictable and resource usage is easier to optimize. AI changes that equation. It introduces variability, continuous retraining, and new dependencies on data quality and accessibility.Tesseyre identifies three failure patterns that surface consistently: the absence of a solid MLOps foundation with automated pipelines and model validation; data architecture not designed with AI access patterns in mind, where retrofitting requires significant upfront work; and change management, which is less technical but just as consequential. Most enterprise environments were not built with AI as a core assumption, and inserting AI into processes designed without it generates friction that is substantially harder to resolve than redesigning from scratch.Legacy infrastructure compounds the difficulty. Most enterprise technology decisions were made years before AI workloads existed at meaningful scale, according to Deloitte. That is an entirely different type of demand at the most foundational infrastructure levels.A new spending cycle is emerging for investors to watch”GPU spend is now a board-level conversation. Every technology leader we work with has a dedicated AI budget, and the pressure to justify it is real,” Tesseyre said.That matches what the data shows. Organizations reporting AI as an active FinOps concern jumped from 31% in 2024 to 63% in 2025, according to CloudZero. AI and ML workloads now represent 22% of total cloud costs at SaaS and IT companies. Token leaderboards and token budgets are becoming standard management tools across enterprise teams.The hyperscalers are responding. Amazon, Alphabet, Microsoft, Meta, and Oracle are collectively forecast to exceed $600 billion in capital expenditure in 2026, with roughly $450 billion tied to AI infrastructure, according to CloudZero. And 42% of enterprises say optimizing AI workflows is their top spending priority for 2026, according to NVIDIA. Optimization has overtaken expansion as the primary stated enterprise priority. The first wave was about acquiring capability. The second is about making it sustainable.

The next wave of AI spending looks nothing like the first oneTermmee/Getty Images

Where the real investment is now flowing”Model experimentation gets the headlines. But you can’t build reliable AI without a modernized data estate underneath it. That’s where the real work is happening,” Tesseyre added.That insight is consistent with what Zencore sees across its client base. The critical first steps in almost every enterprise AI engagement involve database modernization, migrating legacy data stores to the cloud, and building the pipelines that feed the AI layer. That foundational work is unglamorous but non-negotiable. Model experimentation tends to come slightly after, or in parallel, once teams realize that models are only as good as the data grounding them.Deloitte’s 2026 enterprise AI report reinforces this view. Legacy data and infrastructure architectures cannot power real-time, autonomous AI. Modernization must create a living AI backbone that adapts dynamically to business and regulatory change, according to Deloitte. Organizations that fail to invest in that foundation will find their models constrained by the quality of the data underneath them.Key figures on AI spending and infrastructure in 2026:Global AI spending forecast for 2026: $2.5 trillion, with AI infrastructure accounting for $401 billion, according to GartnerCombined hyperscaler capex for 2026: more than $600 billion, with $450 billion tied to AI infrastructure, according to CloudZeroInference costs dropped 280-fold over two years, but overall AI spending grew explosively as usage outpaced savings, according to DeloitteSome enterprises are now seeing monthly AI bills in the tens of millions of dollars, with agentic AI’s continuous inference sending token costs spiraling, Deloitte notedData center systems spending forecast to jump 55.8% in 2026, the largest single growth category in IT, according to GartnerRoughly 70% of large enterprises now maintain a dedicated FinOps or cloud economics team, CloudZero confirmedWhat this means for investorsPhase one of the AI trade rewarded those who bought the infrastructure buildout early. Phase two rewards those who understand where the sustained spending goes next.The companies positioned to benefit are not the most visible names in AI. They are the ones solving the operational, architectural, and data problems that emerge once enterprises move past the pilot stage. Cloud optimization tools, data infrastructure platforms, MLOps providers, and specialized engineering firms are all part of this next wave.What is not in doubt is the direction. AI spending is entering a more complex, more scrutinized phase. The days of deploying first and optimizing later are running out. And the investors who recognize that shift early are likely to find it matters more than the first wave ever did.Related: JPMorgan executive says one thing is keeping AI in check