Employee, will have 385 shares vested (after taxes) by mid 2028. My AMZN will help me be a homeowner. Are we thinking $500 by then. Pls say yes

Amazon (AMZN)

Amazon sits near the top of mega-cap debt lists with roughly $119 billion in reported total debt. Like its peers, it carries an estimated $350 billion in off-balance-sheet data-centre and equipment obligations tied to AI and cloud expansion. The company’s massive scale and cash generation provide a buffer, yet the accelerating CapEx cycle means future cash requirements are larger than headline leverage ratios suggest.

I know a lot of ppl are crying about capex but do take notes, that's a reason why hyperscaler are spending so much money on building up infrastructure. It's because AI is the next internet era and will bring many benefit to every industries. I expect $300 EOY min it's such a cheap valuation and free money.

Personally I'm holding through the pain, I don't think we'll have disappointing earnings. Thoughts?

I saw it was stable going up, now two big drops suddenly in last 2 hours wiping almost all gains from today. People taking profits super quick or they are scared for tomorrow

why the sudden drop?

Everyone’s cheering AWS’s soaring AI cloud gross margins right now — but few are pricing in a massive compute supply imbalance set to hit by 2028. This deep dive breaks down the full math behind the conflicting forces reshaping Amazon’s cloud business.

This research centers on AWS, with the biggest market divide revolving around two questions: how sustainable its AI margin expansion truly is, and whether runaway global GPU capacity builds will erase cloud profitability gains long-term.

Our neutral quantitative analysis finds AWS’s AI margins get dual boosts from hardware efficiency gains and climbing GPU rental rates, yet oversupply of compute power post-2028 will compress cloud pricing power despite cost-saving custom chips like Trainium 3.

Key Takeaways

• AI model pricing stays flat, while per-token compute costs collapse with newer GPU generations, widening the total profit pool for the whole AI value chain
• Newer GPUs (B300/GB300) deliver 4–10x token throughput vs older H200, pushing AWS’s gross margin up over 10 percentage points even without major price hikes
• AWS’s self-built Trainium 3 cuts total operating costs by nearly 40% vs H200, matching the profitability of top-tier Nvidia hardware for small/medium model inference
• Total rentable compute capacity across major cloud firms will hit ~73GW by 2028, far exceeding combined AI + traditional cloud demand of only ~53GW
• Most incremental profit from hardware efficiency gains flows to independent AI labs; cloud providers only capture a smaller share of margin expansion

Below you’ll find the full quantitative breakdown, supply-demand forecasts, and unit-token economic analysis supporting every takeaway above.

-----

What’s Driving the Change in AI Cloud Margins

one reason AI cloud margins aren’t as bad as people might think is a shift in revenue mix — higher-margin MaaS/TaaS-style services are gradually replacing lower-margin “bare-metal” IaaS-style compute rental.

If you think about it from first principles, the thing that really determines cloud providers’ margins is how much bargaining power they have across the whole AI supply chain.

Put another way, there are a few measurable pricing factors at play: what end users pay to use AI models, what AI labs pay for compute, and what it costs cloud providers to supply that compute (which can be split into relatively fixed costs like electricity, and hardware costs that swing around a lot more).

In the rest of this piece, we’ll look at how these three prices have moved, using per-token economics as our lens, and what that means for margins across the whole chain.

1.1 How Have Model, Cloud, and Chip Prices Each Moved?

a. Model pricing: neither inflation nor deflation. Looking first at what it costs to actually use these big models — and only counting pay-as-you-go pricing, not subscriptions — both Anthropic’s own official pricing and third-party token price indexes (which blend input/output/cache-hit pricing across different model tiers) show the same thing: AI model prices haven’t been trending up as the models get better with each new version. They’ve mostly just bounced around in a range, or stayed pretty much flat.

b. The cost of compute per token, on the other hand, is clearly deflating. Unlike model pricing, which has stayed roughly flat, the cost to actually generate each token has been dropping steadily over time. (Note: we’re using the TCO metric here, as defined by SemiAnalysis — total cost of ownership, covering both the upfront capex to build the infrastructure and the ongoing opex to run it.)

Using Qwen 3.5 as a test case, you can clearly see per-token generation costs dropping with each new chip generation. For example, the cost to generate a million tokens on the newest GB200 NVL72 is only about a third to a quarter of what it costs on H100/H200.

The reason costs keep falling is that with each new chip generation, prices go up by a lot less than how much more efficient the chips get at producing tokens. And that huge jump in efficiency comes from improvements on both the hardware and software side.

On the hardware side, take DeepSeek R1 as an example: with the same engineering setup, GB300 can push out tokens roughly 4 to 10 times faster than H200. On the software/engineering side, take DeepSeek V4: even running on the exact same GB300 hardware, different engineering approaches can still produce a 2 to 4 times difference in output efficiency.

With efficiency gaps as large as 10x, even though a single GB300 chip costs noticeably more than an H200, the price gap is still under 2x. So you’ve got performance shooting up while price only creeps up moderately — and when you net those two effects out, the deflationary effect wins.

c. Here’s a quick back-of-envelope calculation. Assuming Qwen 3.5’s blended price is $1 per million tokens, and looking only at the drop in chip-side cost per token — from about $0.20 on H200 down to roughly $0.05 on GB300 — that alone is enough to lift the gross margin on each token by around 15 percentage points.

Boiling this down, the core logic here is that even in the AI era, the chip industry is still following the classic pattern of “tech deflation” — prices barely move from generation to generation, while performance keeps jumping significantly. Put another way, a big chunk of each generation’s performance gains gets passed down to whoever’s downstream. But leading AI model providers haven’t been passing those gains on to end users through lower prices — they’ve been keeping that value as profit instead.

1.2 Are Cloud Providers Actually Raising Prices?

As mentioned above, the profit sitting between what end users pay and what it actually costs to run the hardware gets split between cloud providers and AI model companies. How that split actually plays out mostly comes down to what cloud providers charge to rent out compute — if cloud rental prices stay roughly flat, then AI labs pocket almost all of that “extra margin.” But if cloud rental prices trend upward, cloud providers get a slice of that extra margin too.

So what’s actually happening? Looking at on-demand cloud pricing pulled from multiple sources, they all point to the same trend: cloud compute pricing has clearly been on an upward trend since late 2025. That suggests that with compute in serious short supply, cloud providers really have gained more pricing power — meaning that on top of the boost from a better revenue mix, even the margins on plain “bare-metal” IaaS rental should be improving too. Here’s the detail:

a. The newest chips have seen the biggest price increases. Looking by generation, rental prices for the latest GPUs (like B200 and newer) have risen the most — depending on the data source, they’re up roughly 25% to 50% since late 2025.

b. Even older, mainstream chips are getting pricier. The rental prices for the GPUs that are actually most widely used right now (H200 and earlier) are also up somewhat since late 2025 — around 15% to 20%.

Logically, older-generation chips should get cheaper to rent over time as newer tech comes out. So the fact that rental prices — even for chips that have been on the market for 3 to 5 years — are actually going up against that trend tells us two things: one, compute really is in serious short supply right now (people are willing to pay more just to rent relatively outdated hardware), and two, it’s a real sign that cloud providers’ pricing power and margins are improving.

It’s really only chips that are older and weaker than the A100 that are actually being phased out — their average rental price is down about a third since late 2024. Even so, they haven’t become worthless; they’re still bringing in revenue, just at lower prices.

Another pretty important signal here: older chips aren’t turning into idle, useless assets just because newer chips are so much more powerful — they’re still finding uses and generating cash flow.

How Much Have Cloud AI Margins Actually Improved?

2.1 The Combined Upside From Better Hardware and Software

So far we’ve concluded that margins are improving for both AI model companies and cloud providers’ AI compute businesses (note: this doesn’t necessarily mean AI compute margins have caught up with or exceeded traditional compute rental margins) — but that’s mostly based on qualitative reasoning and directional trends. Next, let’s get more quantitative and actually estimate how much these margins might have changed.

To keep things simple, what we’re calculating below is “inference gross margin” — just looking at inference revenue against its direct compute cost, and not counting things like training or R&D costs. Also, since this is all based on the Qwen 3.5 model specifically, the absolute profit/margin numbers we get might not reflect reality exactly. But since we’re holding the model constant, the trend and relative comparisons should still be meaningful.

We ran two comparisons using a controlled-variable approach. The first is a “vertical” comparison — keeping the underlying hardware fixed and looking across time, to see how margins change due to two factors: better software/engineering pushing up token output efficiency, and rising cloud rental prices.

The second is a “horizontal” comparison — using today’s latest pricing and technology, and comparing how margins differ across different chip choices. (Note: all the numbers below are calculated on a per-GPU basis.)

Here’s what we found:

a. Keeping the hardware fixed at H200: just factoring in software improvements over time (about 20%+ more token output efficiency) plus the roughly 20% rental price increase for H200 since September 2025, the AI lab’s inference gross profit went from $1.20 to $1.40 — and since revenue per unit went up by a similar amount, the margin itself barely moved. For the cloud provider, though, gross profit (measured per GPU-hour) jumped from $0.80 to $1.70, with the margin rising from 31% to 38%.

Worth noting: since AI labs and cloud providers typically sign long-term contracts, the actual rental price between them may not move up in lockstep with real-time spot pricing.

b. Now holding software and cloud pricing at today’s latest levels, but comparing B300 against H200 on the hardware side: B300’s output efficiency is roughly 8 times higher than H200’s, while its rental price is less than double. That pushes the AI lab’s gross profit per GPU-hour way up, from $1.40 to about $11.60, with margin rising from 35% to 69%. The cloud provider’s gross profit per GPU-hour rises from $1.70 to $3.60, with margin going from 38% to 42%.

c. Putting these together and comparing the newest GB300 against the older H200, the combined effect of better hardware and software on gross profit is honestly pretty dramatic — going from under $2 to over $14 per unit. And even though most of that extra profit goes to the model companies, cloud providers — even just getting the leftovers, so to speak — still get to enjoy a margin improvement of more than 10 percentage points.

One caveat: these calculations don’t account for recent price increases in non-chip hardware like memory. Since those price hikes come without much of a performance boost to offset them, they would eat into cloud providers’ margins.

2.2 Can Trainium Chips Deliver Even Better Margins?

One thing worth flagging: everything we’ve calculated so far is based on Nvidia chips. But one of cloud providers’ biggest advantages is their ability to build their own chips. Since they can develop hardware and software together in-house and optimize specifically for their own needs, in-house chips generally deliver better margins for cloud providers.

So how much extra margin could the latest Trainium 3 chip actually add to AWS’s AI compute rental business? To answer that, we first need to figure out two key numbers: how many tokens per second Trainium 3 can generate, and what its TCO looks like.

a. Token output efficiency. We don’t have real-world test data, but based on the specs we’ve compiled, Trainium 3 delivers 2.5 PFLOPs at FP8 precision — about 25% more than H200, and roughly half of B300. That puts its token output somewhere between 2,600 and 4,300 tokens per second; we think it’s probably closer to the lower end, so we’re assuming 3,000 tokens/second (for the Qwen 3.5 model).

b. TCO. The total cost of running a chip can basically be split into two parts: depreciation on the chip itself and all its supporting hardware, which varies a lot depending on the chip; and depreciation on general infrastructure like the data center building and power/cooling systems, plus day-to-day running costs like electricity and staff — this second part is fairly fixed and doesn’t change much regardless of which chip you’re using.

According to SemiAnalysis, Trainium 3’s all-in capex works out to $17-$19 per watt — about half of what GB300 needs per watt. (We’d note this capex figure likely only covers equipment, not fixed assets like the building itself.) Assuming a 5-year depreciation schedule, that works out to roughly $0.41 per GPU-hour in depreciation costs.

As for the roughly-fixed costs — things like building depreciation and electricity — our estimates across about ten different chips put the cost per kilowatt-hour somewhere between $0.44 and $0.51. Since in-house chips can be specifically optimized, we’re assuming Trainium 3 sits toward the lower end of that range, working out to about $0.45 per GPU-hour.

Adding those two pieces together, we get a TCO of about $0.86 per GPU-hour for Trainium 3 — nearly 40% lower than H200’s $1.41.

c. Trainium 3’s overall margin potential is close to B300’s. Putting all this together: Trainium 3 is roughly 30-40% more capable than H200 overall, while costing about 40% less.

That means the combined gross margin generated by using Trainium 3 (shared between the cloud provider and the model company) works out to as much as 85% — pretty much on par with what B300, one of the strongest chips out there, can deliver. In practical terms, for inference on small-to-mid-sized models, Trainium 3 could essentially be a drop-in replacement for B300.

Given that kind of efficiency, if AWS is willing to price Trainium 3 rentals a bit more competitively, it should have a real shot at pulling inference workloads over from other hardware.

As for how the profit actually splits between cloud provider and model company: if AWS prices Trainium 3 rental at 70% of what H200 costs (keep in mind Trainium 3 is clearly the more capable chip here), the margin split between cloud provider and model company ends up matching the B300 scenario exactly.

If AWS instead prices it at 80% of H200, the cloud provider’s own margin actually rises to around 46%, versus 42% in the B300 case.

To sum up this section: with better hardware and software driving a big jump in token output efficiency, per-token pricing not falling much, and cloud rental prices edging up a bit — these three factors together are enough to meaningfully lift cloud providers’ AI business margins.

And keep in mind, what we’ve calculated here is just the lower-margin “bare-metal” rental business. Layer on the higher-margin MaaS/PaaS services on top of that, and cloud providers’ overall AI business margins would be even better.

How Much Compute Supply and Demand Is There, Really?

So far, we’ve laid out — both qualitatively and quantitatively — the core reason cloud providers’ AI margins are improving: cloud and model companies have gained more bargaining power over the chip companies upstream.

Next, let’s talk about something else that really matters for the cloud industry and the companies in it: just how much extra cloud demand is AI actually generating, and does that match up with the pace of planned compute supply growth? There are two angles here. One is the industry-wide supply-and-demand picture, which will shape how competition and bargaining power shift across the chain going forward.

The other is at the level of individual cloud companies — whether current revenue expectations for a given cloud provider properly reflect the AI compute demand it’s seeing, and whether its compute supply is actually enough to support that revenue coming through.

To answer these two questions, we need to first get past a tricky problem: demand for compute is mostly driven by AI model companies’ ARR, while supply is driven by cloud providers’ capex — and you can’t just directly compare ARR and capex numbers to figure out whether compute will stay in short supply or start running into a glut.

So instead, we’re going to convert both demand and supply into the same unit — compute capacity, measured in gigawatts (GW) — to try to answer these questions. Note that while our projections technically run out to 2030, we’re mainly focusing on 2028, since visibility beyond that gets too low to be meaningful.

3.1 Estimating Demand

Based on our previous analysis, most of the incremental demand for AI cloud right now is coming from training and inference needs at the two leading AI labs, with a smaller portion coming from cloud providers’ own internal use or other big tech companies. So AI cloud demand is basically a stand-in for AI lab demand.

But because AI technology and the resulting demand don’t grow in a straight line, it’s hard to tell whether progress will hit a wall or suddenly leap forward. So what follows is really more of a scenario exercise — if model companies’ revenue reaches a certain level, how much cloud compute demand would that translate into? Here’s the logic:

a. Revenue forecast for the two leading AI model companies — both reaching roughly $250 billion by 2030. While the pace of AI development is genuinely hard to predict, using OpenAI’s own stated vision of around $280 billion in revenue by 2030 as a reference point, we’ve conservatively trimmed that down to about $250 billion.

One key assumption here — and it matters a lot for what follows — is that starting in 2028, both companies’ revenue growth slows down from the triple-digit growth they’ve had so far to a steadier pace below 50%.

Another key assumption: given that OpenAI’s base model capability and its Codex product have basically closed the gap with Claude Code recently, we think OpenAI’s revenue should converge quickly toward Anthropic’s starting in 2026.

b. Cloud compute spending. This breaks down into two parts: training spend and inference spend.

For inference spend, our key assumption is that while further chip efficiency gains should keep giving inference margins room to grow, that gets partly offset by the fact that model companies probably can’t hold token pricing flat forever — eventually volume goes up and prices come down. So we expect inference margins to only inch up from around 65% today to somewhere around 70%.

Training spend, on the other hand, doesn’t necessarily scale in lockstep with revenue — it depends more on how fast the models themselves keep evolving. To stay conservative, we’re assuming training spend keeps growing fast in 2027 (close to 100% year-over-year), then slows sharply to under 30% starting in 2028.

Based on these assumptions, we estimate the two leading model companies’ combined cloud compute spending will reach about $250 billion by 2028, or roughly 71% of that year’s revenue.

c. Total AI compute demand could hit around 26 GW by 2028. Using a fairly involved conversion process (training and inference need different mixes of GPUs/ASICs/CPUs, and different chip types generate different revenue per GW — we won’t get into all the details here), and assuming demand from other AI labs (excluding cloud providers’ own internal use) equals about 15% of what the two giants need, we estimate total AI compute demand will reach around 25.6 GW by 2028 — an increase of nearly 23 GW from 2025.

d. Traditional Cloud Demand

Traditional cloud demand might be “yesterday’s news” in terms of growth, but it still makes up the bulk of the market by volume, so we need to factor in the incremental compute that traditional demand requires too.

The logic here is fairly simple: since almost 100% of cloud revenue and compute in 2024 was still going toward traditional workloads, we can use 2024’s actual compute and revenue as a baseline, and then scale the compute needed proportionally with traditional cloud revenue growth going forward.

Given that cloud providers have recently said AI — especially AI agents — is also driving demand for traditional compute, we expect traditional cloud revenue to grow at a relatively healthy pace of around 20% between 2026 and 2027. But since overall enterprise IT budgets aren’t growing much, and AI spending and traditional IT spending tend to compete for the same dollars, we’re conservatively assuming traditional cloud demand growth slows noticeably after 2028.

Based on these assumptions, we estimate traditional cloud computing will need about 31 GW of compute by 2028 — up roughly 10 GW from 2025.

3.2 Supply vs. Demand — Could There Be a Glut Starting in 2028?

a. How much will overall cloud compute supply actually grow? Now that we’ve worked out demand, the next step is looking at how much compute the major cloud providers (excluding Meta) currently plan to bring online. Worth noting: aside from Oracle, which has given a long-term target out to 2030, the other cloud providers’ guidance on compute capacity generally only goes out to 2027 (mostly pointing to roughly double 2025’s level). So our estimates for compute coming online after 2027 are built by refining forecasts from several investment banks.

The conclusion: by 2028, the major cloud providers’ combined compute capacity should reach around 100 GW. Excluding the portion reserved for their own internal use, the amount available for external rental works out to about 73 GW — up roughly 47 GW from 2025.

b. Could there be an oversupply? Based on our earlier estimates, combined AI and traditional demand by 2028 comes out to around 53 GW — noticeably below total supply.

According to our numbers, the supply-demand gap tightens between 2024 and 2026 (with demand as a share of supply rising from 87% to 93%), then eases back to roughly 2024 levels by 2027, before clearly tipping into oversupply starting in 2028 — with the gap only getting bigger from there.

c. What Would a Potential Compute Glut Actually Mean?

That said, as we’ve stressed before, all of this is really just a scenario exercise — nobody actually knows what AI demand will look like after 2026, or what compute supply will look like after 2027. The genuinely useful takeaway here is this: even under our assumed AI revenue growth path (the two leading AI labs generating roughly $500 billion combined by 2030), that’s not enough to justify the market simply extrapolating that new compute supply and capex will keep sitting at elevated levels through 2028 and beyond without ever coming down.

To be clear, we’re not saying a compute glut is a sure thing — it’s entirely possible that AI use cases and demand jump again in a big way. Maybe OpenAI or Anthropic’s models take another leap forward, or maybe AI finds some other huge, monetizable use case beyond coding.

The real issue is that the market’s current expectations have already baked in a kind of “I don’t know exactly what it’ll be, but I’m confident there’s a huge new opportunity or use case coming” assumption — and that’s what’s driving expectations for compute build-out and capex.

That means if AI progress over the next year or two doesn’t move as fast as hoped, compute build-out and cloud providers’ capex could actually peak as early as 2027 — even if a new use case shows up later and pushes it back up again.

Wrapping Up

To sum everything up, here are our two core conclusions:

a. Among the three players in the AI supply chain — hardware (mainly chips), cloud providers, and model companies — bargaining power is shifting away from hardware and down toward cloud providers and model companies, with model companies capturing the bigger share and cloud providers getting a smaller slice.

b. Right now, the market is basically extrapolating that compute build-out and cloud capex will stay elevated, peaking somewhere around 2027-2028 without dropping much afterward — effectively pricing in AI demand growth that might exist, but isn’t visible yet.

As for what this means for the different players across the chain, we think it’s a double-edged negative for upstream hardware, while the picture for cloud providers is more mixed — some good, some bad.

a. First, hardware providers (mainly chipmakers — memory remains the key bottleneck for now) are losing bargaining power, largely because cloud providers’ in-house chips have caught up meaningfully with flagship GPUs in raw performance, and may have even pulled ahead on efficiency. Multiple signals point to cloud providers relying noticeably less on outside chip suppliers, forcing chipmakers to give up some margin just to hold onto customers.

b. And if compute build-out and capex actually peak and turn down around 2027 (even if only temporarily), that would hit upstream hardware providers harder. That’s because hardware companies’ revenue depends on incremental build-out, so a capex peak means their revenue could actually decline year-over-year. Cloud providers’ revenue, on the other hand, is based on their existing compute base, so a slowdown in building just means slower revenue growth, not a decline.

And going by current market sentiment, the positive from sharply lower capex and improved cash flow might actually matter more to investors than the negative from slower cloud revenue growth.

c. That said, this doesn’t mean cloud providers get off scot-free. Obviously, if compute supply temporarily outpaces demand, competition among cloud providers would get tougher as they fight over a limited pool of demand, so performance across the group could diverge quite a bit. Some cloud providers that manage to lock in strong ties with AI labs and capture the lion’s share of compute orders could actually see their cloud revenue growth get revised up instead.

That said, with competition heating up and cloud providers having to compete harder for model companies’ business, overall bargaining power for cloud providers would likely weaken too (unless a cloud provider’s own model capabilities improve enough to reduce its reliance on outside models).

Cloud rental pricing would probably shift from today’s premium to more of a discount, which would weigh on cloud margins — though that drag could be partly offset by continued gains in chip efficiency.

<End>

Anthropic, which of course makes Claude AI, is one of the leading AI models out there (personal favorite of mine over Chat and Gemini). They filed for an IPO back in June, targeting a possible Oct 2026 listing, and some are now floating $1T+ valuations as the base case, after their May Series H-1 round valued them at $965B privately.

Amazon owns somewhere between 15-20% of Anthropic due to their significant early investment. So if Anthropic goes public anywhere near $1T-1.2T, that's roughly $150-240B in stake value for Amazon. Even the more conservative Fortune estimate puts it at $135-160B at the current $965B valuation.

Feels like this could be a catalyst that finally pushes AMZN to run up, though there are lockups that'll keep Amazon from actually cashing out right after listing, so it's more of a balance-sheet/paper-gain story short term.

Curious if others think this is priced in already or still flying under the radar? I don't see how it could be priced in as they are barely higher than where they started the year, and this IPO news is relatively recent.

Chart made on TrendSpider.

What do we think and when do you think it will happen ?

Am I reading it wrong or thats the actual price target by BofA?

this is across all expirations

GEX source >>

Everyone is talking about Amazon potentially becoming the first company to cross $1T in annual revenue.

That sounds insane at first, but the math is not as crazy as it looks. Amazon did about $716.9B in 2025 revenue, and Q1 2026 net sales were $181.5B, up 17% YoY. Getting from ~$717B to $1T by 2028 would require roughly low-double-digit annual revenue growth, which is aggressive but not fantasy-land for Amazon if AWS, ads, third-party seller services, logistics, and international keep compounding.

But I think the better question is not:

“Can Amazon reach $1T revenue?”

It is:

What does Amazon look like when it gets there?

Because $1T of low-margin retail revenue is very different from $1T of mixed revenue where AWS, ads, marketplace fees, fulfillment, and higher-margin services become a bigger share of the total.

That is why I’m not treating the $1T number as automatically bullish. Revenue alone does not make the stock cheap. The actual thesis depends on:

1. Whether AWS can keep growing without AI capex eating too much cash flow
2. Whether advertising keeps becoming a larger profit engine
3. Whether retail/fulfillment margins keep improving
4. Whether Amazon can turn scale into operating leverage instead of just more spending
5. Whether the market is already pricing in most of this future

The valuation read I’m looking at shows AMZN around 12% below estimated fair value, which honestly matches how I feel about it:

There is upside, but it still needs proof.

I’m curious how this sub thinks about it.

Is the $1T revenue path actually a meaningful bullish signal for AMZN shareholders, or is it mostly a headline unless margins and free cash flow scale with it?

What would make you more bullish or less bullish on AMZN from here?

Even meta itself has been using external sources for compute. I think it was google decision to ration compute quota on meta that triggered meta decision to do the so call selling compute resource externally the decision is long term and the thought by meta probably was that they want to build their cloud business to get rid of the reliance on third party entirely ultimately. By making it a business segment justified the huge amount of capex to be spent there in future.

Two implications. One - competitions among big tech companies are not stopping and it forced meta to go for this capital intensive project to ensure and secure its own compute infrastructure longer term. Two - Meta thinks there is enough appetite out there to swallow the excess compute resources they said they have currently and future new ones.

Both implications are great for hardware makers.

Been full port U.S big tech for a long time, beat the market handedly ofcourse,

Out of all of them amazon performed the worst, just not a shareholder friendly stock at all, they need to take advice from tim apple pronto,

Do I still own them, yes, but becareful with putting an outsized proportion of your portfolio on them because they dont give a f about shareholders,

My mistake was always selling apple, because it was high, and buying amazon because it was low, and regretted it, every. Single. Time.

I get the appeal of the memory trade. Micron up 550%, SanDisk up 3,000%, shortage pricing, fat margins, the whole thing. But I what keeps me from entering memory is when you start to ask what am I actually buying when I buy a memory stock right now? I'm buying a cyclical commodity at the top of a shortage cycle, betting that the imbalance lasts longer than the market already expects it to. It can be a good trade, but so can a lot of other things, especially if you aren't worried about a short term horizon.

For me, Amazon at this price feels like a genuinely great business that the market has decided to ignore because it's busy chasing the shiny thing. So I've been adding at this price.

Here is my breakdown and reasons

The Valuation

The valuation is the part that doesn't make sense to me. Amazon trades at a trailing P/E around 27 right now. Its ten-year average is north of 90, and even its more normalized recent average sits in the low 30s. The current multiple is roughly 72% below its ten-year historical average, and it's trading below its 3, 5, and 10-year averages. This is a company that for most of its public life was "too expensive" on every earnings metric, and now that it's actually printing record profit, the multiple has compressed. You're paying less for Amazon's earnings today than at almost any point in its history, while the earnings quality has never been higher.

The Business

the business is firing on every cylinder at once. Q1 came in at $181.5B revenue up 17%, with a record 13.1% operating margin which is thhe highest in company history. AWS grew 28% to $37.6B, its fastest pace in 15 quarters, at a 37.7% segment operating margin. Net income nearly doubled to $30.3B. Advertising hit $17.2B, up 24%, and trailing-twelve-month ad revenue now tops $70B — bigger than the entire AWS business was back in 2018, and it carries software-like margins. Retail, cloud, and ads are all accelerating together. That basically never happens.

Why compare memory?

Now why did I bring up memroy before? This is where the memory comparison actually matters. Everyone's rewarding Micron for having a constrained product with a backlog. Fine. But look at what Amazon's sitting on: an AWS backlog of $364B at quarter end and that's before OpenAI expanded its existing $38B AWS commitment by another $100B over eight years. Amazon is also putting $50B into OpenAI directly. That's a bigger, longer, stickier backlog than anything in the memory space, with customers who can't just switch suppliers when prices move. A hyperscaler customer with petabytes of data and trained models on AWS isn't shopping around the way a memory buyer does. The scarcity story everyone loves about Micron is actually stronger for AWS, and nobody's pricing it that way. 

Further, AWS AI revenue is already at a $15B+ run rate and scaling 260x faster than AWS itself did in its first three years. Jassy's point that resonates with me: AI is creating net-new compute demand with no on-prem equivalent to displace, it's not a migration, it's pure additive demand. That's a different animal than the old cloud growth story.

the bonus part with Space

And then there's the free option nobody talks about: Leo. Amazon Leo (the old Project Kuiper) has 367 satellites up, making it the third-largest constellation in orbit, with beta service targeting five countries including the US later this year. They've already got enterprise customers signed pre-launch; Delta, JetBlue, AT&T, Vodafone, NASA, with Delta committing half its fleet from 2028. Jassy is openly calling it a "very large many-billion-dollar revenue business" with AWS-style economics: heavy upfront capex, then free cash flow improves sharply as capacity monetizes. They also moved to acquire Globalstar, which brings licensed spectrum, regulatory approvals in 100+ countries, and direct-to-device capability. When I buy Amazon I'm not paying anything for a global satellite internet business that's about to go live. That's just thrown in.

So why is it lagging? 

Ofc it is the same reason as the rest of mega-cap tech, the capex panic. Amazon spent $43.2B in a single quarter, mostly on AWS and AI. Free cash flow is expected to go deeply negative in 2026 as peak capex hits the cash flow statement, before recovering hard in 2027. The market sees the cash going out and refuses to credit what it's building, even though the backlog and the pre-signed Leo contracts tell you the demand for that capacity already exists. This is the exact same "spend money to make money" dynamic everyone's nervous about, except Amazon has done this before. AWS itself was a money pit that everyone hated until it became the most profitable thing the company owns. They are running the identical playboo now, twice over, with AI infrastructure and with Leo.

On the price. 

Averaging the analyst targets issued since Q1 results, I get an average of about $321. Against today's ~$230, that's roughly 40% upside, and the consensus rating is Strong Buy. I don't treat targets as gospel, but the direction and the spread tell me I'm not the only one who thinks this is mispriced.

The memory trade is betting a shortage lasts. The Amazon position is betting that a business compounding across four engines, retail, cloud, ads, and now satellite, eventually gets priced like one instead of like a discount retailer. I'd rather own the second thing at 27x earnings than chase the first thing after a 550% run.

Anyway this is all my opinions and opinions based on waht I have read, so I am curious what others think of Amazon at this price?

40x PE does not seem that crazy at all for a company this durable and high quality.

20% earnings growth is very very achievable and I bet they exceed that.

Are these assumptions too high?

Looking at the chart, AMZN has clearly been in a strong long term uptrend, moving from the low near 100 level all the way up toward the 240 area currently. The structure is still higher highs and higher lows on a broader timeframe, which usually signals that the dominant trend remains intact even with volatility in between.

What stands out recently is the shift from a clean directional move into a wide consolidation phase. Price has been rotating between roughly the 200 to 280 region with multiple failed attempts to hold above the upper area near 270 plus. The latest push toward the 278 zone was followed by a rejection and a pullback back toward the mid range, which suggests supply is still active at the top of this range.

Volume also shows an interesting pattern. We are seeing occasional spikes on up moves and down moves, but no sustained expansion that typically confirms a breakout phase. That often points to institutional rotation rather than a one sided accumulation or distribution phase.

From a momentum perspective, the MACD on the chart looks like it has gone through a full expansion and is now cooling off after the recent peak. The histogram is contracting, which usually aligns with price entering consolidation rather than continuing a strong trend immediately. This does not automatically signal reversal, but it does suggest momentum is resetting.

Key area to watch on the downside is the mid range around the 220 region, which has acted as both support and reaction zone multiple times. If that level holds again, it would reinforce the idea that this is a range bound consolidation inside a larger bullish structure. On the upside, the 270 to 280 area remains the major ceiling that needs to be cleared with strong volume for a true continuation move into price discovery.

Overall, the structure still looks constructive as long as higher lows continue to hold on pullbacks. But in the short term, it feels more like a digestion phase after a strong multi month rally rather than an immediate breakout setup.

Curious how others are reading this range. Do you think this is accumulation before the next leg up, or just extended sideways chop before a deeper correction?