Alphabet is heading into its first-quarter earnings report on April 29 with a potent mix of momentum and unease. The stock trades at roughly €288.10, just shy of its 52-week high, having surged nearly 110% over the past twelve months. But beneath the surface of product launches and a blockbuster customer deal lie growing internal doubts about the company’s competitive edge in AI coding.
A Dual-Chip Strategy and a Billion-Dollar Client
At its Cloud Next ’26 conference in Las Vegas, Google unveiled the eighth generation of its Tensor Processing Units, splitting the line for the first time into two specialized variants. The TPU 8t is designed for training new AI models, delivering nearly three times the compute power of its predecessor. A single superpod packs 9,600 of these processors alongside two petabytes of shared high-speed memory. For inference, the TPU 8i targets ultra-low latency, improving performance per dollar by 80%, according to the company. A new networking fabric called Virgo can link up to one million processors into a single training cluster.
The hardware push has already landed a marquee customer. Thinking Machines Lab, the startup founded by former OpenAI executive Mira Murati, signed a multi-year contract worth a low single-digit billion dollars. The deal, though not exclusive, marks the third major developer Google has locked in for its cloud capacity after Anthropic and Meta. In initial tests, Thinking Machines doubled its training speed using Google’s infrastructure, and the startup will also tap into Nvidia’s new GB300 chips.
The Cloud Momentum Story
Beyond hardware, Google’s software business is showing traction. The Gemini Enterprise platform saw a 40% sequential increase in paid monthly users during the first quarter. Over the direct API, Google’s models now process more than 16 billion tokens per minute, up from 10 billion in the prior quarter. Internally, AI now generates 75% of all new code at Google, compared with 50% last autumn.
Yet the internal picture is less rosy. According to Bloomberg, senior Google managers are worried about the company’s position in AI-powered developer tools. Rival Anthropic is gaining ground with its Claude model among enterprises, and even DeepMind employees have reportedly been using Claude despite a ban on external tools. The problem is structural: Google’s coding capabilities are scattered across more than six products under different brands, diluting focus and impact.
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In response, DeepMind has formed a specialist team under engineer Sebastian Borgeaud to tackle complex, long-term programming tasks, such as writing new software from scratch. Co-founder Sergey Brin and DeepMind CTO Koray Kavukcuoglu are directly involved. Brin made the stakes clear in an internal memo: “The final battleground in the AI race is agents.”
The $185 Billion Question
All eyes are now on Alphabet’s earnings release after the US market close on April 29. Analysts expect earnings of $2.68 per share on revenue of roughly $107 billion. But the market’s focus will be on capital expenditure, not revenue growth.
Alphabet has guided for 2026 capital spending of $175 billion to $185 billion, nearly double the $91.4 billion spent in 2025. Depreciation on new data centers is squeezing margins, and some analysts project negative free cash flow for the year. Morningstar rates the stock as fairly valued with three out of five stars and a price target of $340.
The company’s order backlog stands at $240 billion, offering some justification for the spending spree. But the management team must convince investors on Wednesday that these investments are beginning to pay off in measurable cloud revenue growth. The stock has already priced in the narrative — now the numbers need to deliver.
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