Amazon 2026 Q2 Earnings Call

Hello, and welcome to our Q2 2026 financial results conference call. Joining us today to answer your questions is Andy Jassy, our CEO, and Brian Olsavsky, our CFO. As you listen to today's conference call, we encourage you to have our press release in front of you, which includes our financial results as well as metrics and commentary on the quarter. Please note, unless otherwise stated, all comparisons in this call will be against our results for the comparable period of 2025. Our comments and responses to your questions reflect management's views as of today, July 30th, 2026, only, and will include forward-looking statements. Actual results may differ materially. Additional information about factors that could potentially impact our financial results is included in today's press release and our filings with the SEC, including our most recent annual report on Form 10-K and subsequent filings.

During this call, we may discuss certain non-GAAP financial measures. In our press release, slides accompanying this webcast, and our filings with the SEC, each of which is posted on our IR website, you will find additional disclosures regarding these non-GAAP measures, including reconciliations of these measures with comparable GAAP measures. Our guidance incorporates the order trends that we've seen to date and what we believe today to be appropriate assumptions.

Our results are inherently unpredictable and may be materially affected by many factors, including fluctuations in foreign exchange rates and energy prices, changes in global economic and geopolitical conditions, tariff and trade policies, resource and supply volatility, including for memory chips, and customer demand and spending, including the impact of recessionary fears, inflation, interest rates, regional labor market constraints, world events, the rate of growth of the internet, online commerce, cloud services, and new and emerging technologies, and the various factors detailed in our filings with the SEC. Our guidance assumes, among other things, that we don't conclude any additional business acquisitions, restructurings, or legal settlements. It's not possible to accurately predict demand for our goods and services, and therefore, our actual results could differ materially from our guidance. Now I'll turn the call over to Andy.

Thanks, Dave. We're reporting $200.6 billion in revenue, up 20% year-over-year. Operating income was $27.5 billion, up 43% year-over-year. Q2 was another very strong quarter for Amazon. I'll start with AWS, which is booming right now, and I'll share the numbers, what we think is going on, and why we're enthusiastic about the ROIC equation, even with heavy CapEx the next few years. First, the numbers. Revenue growth of 36.7% year-over-year, accelerating for the fifth straight quarter, our fastest growth in 18 quarters back when AWS was less than half its current revenue size. We added over $4.6 billion in revenue quarter-over-quarter, about 80% more than our largest increase ever. Our backlog stands at $496 billion, growing triple digits year-over-year.

AWS is now a $169 billion annualized revenue run rate business, which, for perspective, would place it 24th on the Fortune 500 list if it was a standalone company. Our chips business now has an annual revenue run rate of over $25 billion, growing triple-digit percentages year-over-year. Our AI revenue run rate climbed significantly quarter-over-quarter, and is now also over $25 billion, growing triple-digit percentages year-over-year. Customers choose AWS because we offer the broadest capabilities. They want their AI inference to reside near their other applications and data, and more of it resides in AWS than anywhere else. Because AWS has the strongest security and operational performance. We're seeing strong growth across both AI and non-AI, what we call core, and growth in one is driving growth in the other.

Growth in AI drives core because post-training reinforcement learning and agent tool use is mostly done on CPUs versus AI accelerators. This is an advantage for AWS, as our Graviton chip is the strongest CPU chip, offering up to 30%-40% better price performance than other options. You need a place to store this AI data and to run vector databases, which are also emblematic of a meaningful edge for AWS because we have the broadest and most capable functionality by a fair bit in these core infrastructure areas. We feel similarly about the AI stack, top to bottom. We have a unique offering that customers are excited about. As we've been saying for 18 months now, technically competent companies are going to build their own foundation models. Not the really big frontier models, but smaller models that leverage their proprietary data.

There is no easier service for this than our SageMaker AI service. Customers also need a high-performance, cost-effective inference service, and that's what Amazon Bedrock provides. Bedrock not only provides the best selection of leading models at superior performance and with the governance and security controls that companies need, it's also continuing to grow incredibly quickly. In addition to leading model building and inference services, customers need easier ways to build, run, and leverage agents. For example, even after you've built an agent, you have a lot of muck to worry about. A production agent needs somewhere secure to run, memory so it holds context, an identity so it can act on a user's behalf, tools and data to connect to, and a way to watch what it's doing once real traffic hits. Stitching all that together reliably is hard, and it's stalled many production deployments.

It's why we've built Amazon Bedrock Agents. It provides building blocks as managed infrastructure, and our teams keep iterating, recently adding features like policies which give companies deterministic controls over what agents can do, payments so agents can execute transactions autonomously, web search to ground agents' knowledge without having to leave AWS, and a new harness that further speeds up how fast customers can put this all together, including creating the agent with Strands Agents. While companies will construct their own purpose-built agents from the ground up, most will also use turnkey agentic services. Coding agents are a good example, and there are several successful ones, including Claude Code, Codex, and our own spec-driven Kiro, which is up to 50% more cost-effective than others and tripled in usage quarter-over-quarter.

Another of these agentic services is Amazon Q, an intelligent AI work companion that helps you manage, search, and automate your digital workload across email, calendar, local or cloud files, and custom workflows. Unlike other offerings in this space, Q also lets you manage across leading SaaS tools like Slack, Salesforce, Jira, Teams, and ServiceNow. Q enforces a company's existing access controls so each person sees only what they're cleared to see. Then it takes action: scheduling meetings, drafting and sending email, updating a CRM record, building a dashboard, and more. In Q2, we made Q even more capable, adding autonomous agents that customers set up in plain language to run continuously in the background and carry out multi-step tasks, a personalized activity feed that pulls email, messages, calendars, and tasks into one prioritized view, and 16 new integrations, including Adobe, Moody's, and Snowflake.

Q has momentum, with 3M, Allianz, AstraZeneca, Autodesk, BMW, Exxon, FINRA, Hyundai, Intuit, Mondelēz International, Moody's, the NBA, the NFL, Sun Life, and Southwest Airlines all using it. We also have services like Amazon Connect, our call center service, and AWS Transform, which automates software migration growing quickly. I will mention one more that I am excited about. As frontier models get increasingly powerful, they're making it easier to find security vulnerabilities in technical applications, many of which humans haven't found. This is obviously concerning for companies that protect important data. We recently released AWS Continuum, which discovers, prioritizes, validates, and remediates code vulnerabilities. It starts by ingesting the backlog of vulnerabilities a team already has and then leverages the new frontier models to run comprehensive scans. Continuum uses agents in each company's own business context to prioritize what matters, reasoning through questions like, "Is the affected component deployed? Is it reachable? Is it in a production path?

What's the impact if it's exploited?" Then it validates vulnerabilities in a sandbox so teams aren't chasing false positives. Finally, it recommends the fix. It is hard to talk with enterprises about AI right now without their mentioning security. We expect Continuum to grow quickly. I mentioned earlier that our chips revenue run rate is now over $25 billion. We are unusually well-positioned for this AI inflection, given our leading price-performance chips in both AI with Trainium and CPU with Graviton. In addition to the two leading AI labs in the world, Anthropic and OpenAI, making multi-year, multi-gigawatt commitments to Trainium, an increasing number of AI startups are also adopting Trainium, including unicorns like Neurorobotics and Odyssey, joining startups like Twelve Labs, Descartes Labs, Poolside AI, Karakuri, Metagenomi, NetoAI, and Splash Music, and larger companies like Uber and Pinterest all adopting Trainium.

Graviton is used by 98% of our top 1,000 EC2 customers. The revenue commitments have increased nearly three times quarter-over-quarter, and Graviton5 is growing nearly 2x faster as Graviton4 did. We also continue to have a deep partnership with Nvidia, and we'll continue making AWS the best place to run Nvidia chips, as we have customers who will run on Nvidia for as long as we can foresee, and we believe strongly that customers want choice. Choice is good for customers, competition, and driving the cost of inference down, which customers care deeply about. Let me talk for a second about how we see this investment playing out. Earlier this year, we said we plan to invest approximately $200 billion in cash CapEx in 2026, the majority of which to support AI and AWS.

At this level of spend and higher, we have clear line of sight to strong financial returns. I'll explain why. There are two major parts of the investment, the data centers and the servers and networking equipment that go into them. These have different capital cycles. Data center capital is spent starting two years before we can put servers into them to start monetizing. Once a data center opens with servers plugged in, we start generating significant revenue right away and then get to monetize these data centers for 30-plus years without having to spend that startup capital again. Servers and networking equipment operate on a shorter cycle. We typically purchase these a few months before putting them into service, so we have strong visibility into customer demand before we trigger the spend. If the demand isn't there, we won't spend the capital.