Memory Costs Are Rising—AI Can Protect Telco Margins

In 2025, smartphone makers quietly started doing something they hate doing: shipping “new” models with less. Not less marketing. Less hardware—weaker camera modules, simpler displays, trimmed memory configurations. Counterpoint Research points to a clear driver: DRAM price increases pushed smartphone manufacturing costs up about 25% for low-end devices and 10% for high-tier models since the start of the year, with another 10%–15% cost rise expected in the first half of 2026.

That’s the device side of the industry reacting to a cost shock. Here’s why it matters for the AI in Telecommunications crowd: telcos face the same math, just with spectrum, power, backhaul, site leases, and vendor contracts instead of camera modules. When costs rise and revenue per user is flat, you don’t “innovate” your way out with slogans. You either operate more efficiently or you simplify what you offer.

The difference? Smartphone OEMs can downgrade a periscope lens. Telcos can’t downgrade reliability without backlash. That’s why AI-driven network operations—forecasting demand, optimizing RAN energy, and automating service assurance—has become the most practical path to protecting margins.

Why smartphone makers are lowering specs (and why 2026 looks rough)

Answer first: Smartphone OEMs are lowering specs because memory inflation is squeezing bill-of-materials (BOM), especially at the low end where there’s little room to raise prices.

Counterpoint’s read is blunt: DRAM increases have materially raised manufacturing costs—~25% for low-end phones, ~10% for premium models—and the pressure isn’t easing soon. If you’re selling a $150–$250 device, a cost swing of even a few dollars can wipe out your margin.

Manufacturers are responding with familiar tactics:

• Component downgrades (camera modules, periscope solutions, displays, audio parts, and memory)
• Reusing older components (prior-gen parts and tooling)
• Streamlining portfolios (fewer SKUs, fewer weird mid-tier variants)
• Pushing buyers to Pro variants (protecting margin via upsell)

What makes this notable is the macro signal: Counterpoint revised its 2026 smartphone shipments outlook from growth to a 2.1% year-on-year decline, aligning with IDC’s expectation of a near 1% decline. When both demand and costs move against you, you cut complexity fast.

The uncomfortable truth: low-end devices get hit first

Answer first: The cheapest smartphones are most exposed because their pricing power is weakest.

Counterpoint expects China OEMs to feel the largest shipment downturn, with the lowest-priced segment taking the hardest hit. That tracks with basic economics: premium brands can raise prices (or protect pricing via financing, trade-ins, and ecosystem stickiness). Budget brands mostly can’t.

In practice, that means more devices marketed as “new” will be incremental refreshes. And consumers will keep phones longer if upgrades feel less compelling—another headwind for volume.

The telco parallel: you can’t “downgrade the network,” so you optimize it

Answer first: Telcos don’t have a safe equivalent to lowering handset specs; the viable alternative is AI-driven efficiency across planning, operations, and customer experience.

If you run a telecom network, your “BOM” is operational: energy, transport, spectrum utilization, vendor maintenance, truck rolls, and the time your engineers spend triaging issues. When costs rise, the temptation is to do what device makers do—prune offerings, reduce investment, or push users to higher-priced tiers.

Some of that will happen. But unlike a smartphone camera, network performance is a shared resource. A single bottleneck can become a public complaint, a regulator escalation, or churn.

So the question becomes: How do you reduce cost per bit without reducing experience?

Where AI actually helps: cost control with guardrails

Answer first: AI helps by predicting, prioritizing, and automating actions that reduce waste—without letting service quality slip.

The strongest AI use cases in telecom map cleanly to what smartphone OEMs are doing, just at network scale:

1. Predict demand instead of overbuilding

   • Traffic forecasting at cell, sector, and neighborhood level
   • Seasonal modeling (holiday travel, events, weather-related shifts)
   • Capacity decisions that match real usage, not last year’s peak plus 20%

2. Optimize network resources continuously

   • RAN optimization for load balancing and parameter tuning
   • Congestion prediction and proactive mitigation
   • Smarter slice allocation policies (where slicing is deployed) so resources follow revenue and SLA risk

3. Reduce energy spend with AI-driven energy management

   • Sleep/standby strategies tuned to local demand patterns
   • Coordinated carrier shutdown where coverage and QoE allow
   • Site-level anomaly detection for power draw (spot faulty cooling, rectifiers, or misconfigurations)

4. Prevent outages and truck rolls

   • Predictive maintenance for radios, power systems, and backhaul
   • Automated root-cause analysis (RCA) that shortens mean time to repair

In my experience, the real benefit isn’t “automation for its own sake.” It’s fewer expensive surprises—fewer late-night escalations, fewer emergency capacity adds, fewer churn-driven concessions.

A practical lesson from OEMs: prune complexity before it prunes your margin

Answer first: When input costs rise, complexity becomes a hidden tax—on supply chains for OEMs and on operations for telcos.

Counterpoint mentions OEMs streamlining portfolios and reusing older components. That’s not glamorous, but it works. Telcos can take the same stance:

Portfolio simplification (yes, it’s operational too)

Every legacy plan, special bundle, and one-off enterprise SLA adds overhead across:

• provisioning
• billing
• customer care
• network policy and QoS rules
• troubleshooting playbooks

AI can help quantify complexity by tying it to cost:

• Which plans create the highest care contacts per 10,000 subscribers?
• Which enterprise SLAs generate chronic “near-breach” alerts?
• Which device-plan combinations correlate with bad QoE?

Then leadership can make hard calls with data instead of gut feel.

“Reuse old components” becomes “reuse proven network patterns”

For telcos, this translates to:

• standard reference designs for sites and edge nodes
• fewer bespoke integrations
• reusable automation runbooks in your AIOps stack

The goal is simple: fewer unique snowflakes to support.

From smartphone “Pro upsell” to telecom: price for value, not for volume

Answer first: When costs rise, chasing volume often destroys margin; AI helps telcos target value segments without guessing.

Smartphone makers are nudging people toward Pro models because it’s one of the few levers left. Telcos see the same dynamic in premium tiers, fixed wireless access, managed Wi‑Fi, and enterprise connectivity.

AI supports this shift in three concrete ways:

1) Predict willingness-to-pay and churn risk

A good model doesn’t just score churn. It answers: what offer prevents churn at the lowest cost?

• Some customers need a service fix, not a discount.
• Some need a proactive device or Wi‑Fi upgrade.
• Some are price-sensitive and will leave without a targeted retention offer.

That difference is millions in margin.

2) Improve QoE where it moves revenue

Not every cell needs the same attention. AI-driven QoE analytics can identify:

• where performance is hurting high-value segments
• where investment reduces SLA penalties
• where optimization beats new hardware

3) Automate customer experience without lowering service quality

If smartphone OEMs are reducing hardware cost, telcos can reduce cost-to-serve:

• AI agent assist for contact centers
• self-healing workflows for common issues
• intent-based routing and faster resolution

The stance I take: automation is only worth it if it measurably lowers repeat contacts and escalations. If your bot increases customer effort, you’ve just moved cost around.

What telecom leaders should do in Q1 2026 (a simple playbook)

Answer first: Treat the handset memory shock as a warning: cost pressure is contagious, and AI belongs in the operating plan—not a pilot corner.

Here’s a focused checklist that works even if you’re not “AI mature” yet:

1. Set two non-negotiable metrics

   • Cost metric: energy per GB, cost per trouble ticket, or truck rolls per 1,000 sites
   • Experience metric: dropped-call rate, latency percentile, or QoE score for priority segments

2. Prioritize one network optimization loop Pick one closed loop to implement end-to-end (data → model → action → validation). Examples:

   • congestion prediction → parameter change → QoE validation
   • site power anomaly detection → field dispatch prioritization

3. Clean the data that actually blocks automation Don’t try to “fix all data.” Fix the specific gaps that stop decisions:

   • inconsistent site IDs
   • missing topology relationships
   • alarm storms without deduplication

4. Create an “exception budget” Automation needs boundaries. Define what must be human-approved:

   • changes in urban dense areas
   • enterprise SLA zones
   • emergency service coverage regions

5. Align device and network teams around usage reality If consumers buy devices with lower memory or different radio capabilities, behavior changes. Use AI to detect shifts early:

   • app usage patterns
   • uplink/downlink mix
   • video bitrate adaptation behavior

This is where telecoms can outperform OEMs: you see the network truth in near real time.

The bigger takeaway for the AI in Telecommunications series

Smartphone makers are reacting to memory inflation by trimming specs and simplifying product lines. That’s rational—but it also signals a market where growth won’t cover inefficiency.

For telcos, the path is narrower: you can’t quietly ship a “lower-spec network.” What you can do is run a smarter one. AI in telecom isn’t about flashy demos. It’s about operating discipline—forecasting demand, optimizing 5G resources, reducing energy, and preventing outages before they hit customers.

If DRAM can swing smartphone costs by double digits, you should assume your own input costs will keep lurching too—energy prices, vendor renewals, and infrastructure expansions included. The operators that win in 2026 won’t be the ones with the most dashboards. They’ll be the ones with AI systems that make decisions, safely, every day.

Where are you still “overbuilding for safety” in your network—and what would it take to replace that habit with prediction and automation?