Eric Victor Gitter writes about AI infrastructure and technology distribution, including the power, cooling, and data-center capacity behind AI, and how IT channel businesses can respond to changing supply and demand.
They are most useful for translating AI infrastructure trends into IT channel decisions.
Best for
A technology distribution company planning a partner session on AI infrastructure strategy.
Not for
A sponsor wanting an always-on paid content series with audience guarantees.
Key takeaways
About half of the roughly $6.7 trillion data-center buildout is not silicon or compute.
Power infrastructure is 15-25%, cooling 10-15%, facilities 5-10% of spending.
Build a hyperscaler practice on AWS, Microsoft Azure, or Google Cloud.
Check first: Ask for recent LinkedIn analytics, audience breakdowns, engagement, and specific posts before contacting.
Main ideas
AI infrastructure opportunity extends beyond chips · Supply constraints make hyperscaler alignment a strategic choice · AI adoption needs practical outcomes and trustworthy content
Audience
3,955 LinkedIn followers · 4 posts between 6 October 2025 and 24 April 2026 Reviewed post window
Eric Victor Gitter writes about the infrastructure and business decisions behind AI, especially data-center power and cooling, constrained technology supply, and how IT channel companies can respond.
They connect large-scale AI investment to practical choices for distributors, managed service providers, and systems integrators.
Their posts also address synthetic-content trust and industry events, showing interests beyond infrastructure.
Follow Gitter for a channel-focused view of where AI capacity is constrained and what those constraints may mean for technology businesses.
Gitter translates infrastructure trends into decisions for the IT channel.
In one post, they break down projected data-center spending beyond chips, naming power, cooling, and facilities as areas where suppliers may help relieve bottlenecks.
In another, they link concentrated memory supply and hyperscaler capacity to a strategic choice: build or deepen a cloud practice while considering sovereign AI and edge deployments.
These posts connect market pressures to possible business responses rather than treating AI as a technology trend alone.
Gitter also writes about synthetic content and trust, and recaps partner events, including candid advisory discussions and practical AI adoption.
Their posts most clearly establish a perspective on infrastructure and channel strategy.
Eric Victor Gitter’s posts connect AI’s technical growth to the infrastructure and business choices around it.
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They argue that the opportunity extends beyond chips to power, cooling and facilities; that access to constrained supplies can divide IT businesses into different tiers; and that AI’s usefulness depends on keeping trust and practical outcomes in view.
AI infrastructure opportunity extends beyond chips
Gitter argues that data-center growth creates openings across the infrastructure that keeps computing running, not just in processors and servers.
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Power, cooling and facilities account for a substantial share of projected investment, and specialized suppliers may solve bottlenecks that hyperscalers cannot handle alone. Gitter sees an opportunity for IT channel businesses to serve those suppliers.
In a post about a projected $6.7 trillion data-center buildout by 2030, Gitter breaks out power infrastructure, cooling and facilities as separate spending categories. The post points to grid upgrades and transformers, direct-to-chip and immersion cooling, and faster-to-build facilities, then identifies medium-sized industrial and energy businesses as potential winners.
Supply constraints make hyperscaler alignment a strategic choice
Gitter argues that as leading AI companies secure scarce inputs, from GPUs to memory and eventually electricity, IT businesses may face very different access to capacity.
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The post advises them to build or deepen practices with major cloud providers, while also considering sovereign AI and edge deployments where local expertise and relationships matter.
Discussing OpenAI’s Stargate memory agreements, Gitter cites 900,000 wafers per month and concentration among three memory suppliers. The post describes a split between businesses working with hyperscalers and others competing for remaining supply, then advises MSPs and systems integrators to build or expand cloud practices.
AI adoption needs practical outcomes and trustworthy content
Gitter’s posts pair enthusiasm for AI with concern about what happens when its outputs cannot be trusted.
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One warns that synthetic content can undermine confidence in digital experiences and enterprise brands; another praises AI work focused on improving partner outcomes and reducing friction. Together, they suggest that AI progress should be judged by both credibility and practical results.
In a post on synthetic content, Gitter says an enterprise’s brand is only as trustworthy as its content supply chain and raises provenance and deepfake liability as issues under debate.
In a Lenovo Accelerate recap, Gitter praises Reem Gedeon’s team for a clear AI plan and quotes Dilip Bhatia’s focus on better outcomes and less friction for partners.
The views on the AI plan and partner outcomes belong to the named speakers.
The posts pair infrastructure and channel-strategy themes with a broader concern for trust and grounded AI adoption.
This answers a practical question: if AI spending isn’t just on chips, where else does the money go, and how can channel businesses participate?
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Newcomers should start here for a broad, practical map of AI infrastructure beyond compute.
Eric Victor Gitter argues that the AI buildout is not only a semiconductor story.
In their breakdown of a projected $6.7 trillion spend on AI-ready data centers by 2030, roughly half goes to silicon and compute, while the rest goes to power infrastructure, cooling, and facilities.
They estimate power at 15–25% of spending, cooling at 10–15%, and facilities at 5–10%.
They point to specific pressures: AI racks needing 5–10x the power density of traditional racks, major grid and transformer investment, and a shift from air cooling to direct-to-chip and immersion systems.
Their channel argument: medium-sized industrial and energy suppliers can win by solving bottlenecks hyperscalers cannot address alone, while IT channel businesses can serve those suppliers.
For brands planning a campaign
Use this as the starting point for a session on the shift from compute to power and cooling.
Frame partner segmentation around power, cooling, grid connection, and specialized construction—not GPU resale alone.
The percentage ranges can anchor a discussion about where distribution and channel partners can add value in an AI infrastructure strategy.
This explains why hyperscaler alignment matters commercially, not just technically.
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Start here to understand the supply concentration behind AI infrastructure and the strategic split it creates in the channel.
Eric Victor Gitter describes OpenAI's Stargate deals with Samsung Semiconductor and SK hynix as securing 900,000 wafers per month, with the suppliers reportedly unaware of each other's commitments until the announcement.
They note that Samsung, SK hynix, and Micron make up over 90% of the world's memory supply and are focusing on a few AI customers.
The post argues that concentration is moving from GPUs to memory and eventually to electricity, with hyperscalers locking up long-term inputs.
Companies working with hyperscalers have secured capacity, they argue, while everyone else competes for constrained supply and faces shortages and price spikes.
They advise MSPs and systems integrators to build or deepen a hyperscaler practice on AWS, Microsoft Azure, or Google Cloud, while also positioning for sovereign AI and edge deployments.
For brands planning a campaign
This post makes the supply-chain case for a hyperscaler practice.
Use it to examine which partners are exposed to constrained hardware and which are aligned with cloud capacity.
Then discuss timing: when to build a hyperscaler practice, when to double down, and where sovereign or edge work can offer differentiation.
This outlines the social and enterprise risks Eric Victor Gitter sees alongside the infrastructure boom.
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Read it to understand that their interests include AI trust and content authenticity, not only data-center supply chains.
Eric Victor Gitter shifts the focus from infrastructure to trust.
They describe an era in which content is fabricated, personalized, multiplied, and spread not only by bots but by enterprises, governments, platforms, and trusted people.
They point to models that can generate text, images, and people in video calls; to 80% of consumers reportedly unable to tell AI-generated content from reality; and to falling trust in digital experiences.
Their enterprise claim is that a brand is only as trustworthy as its content supply chain, while governments debate watermarking, cryptographic provenance, and liability for deepfakes.
They also share a personal concern: watching people lose the instinct to question what they see worries them more than the technology itself.
For brands planning a campaign
This broadens the AI conversation from infrastructure to adoption and governance.
For a technology distribution company, it can support a secondary session on trust, provenance, and enterprise readiness, especially if partners sell AI tools to regulated or reputation-sensitive customers.
Keep it secondary to the power-and-cooling and hyperscaler-practice threads unless a campaign specifically targets AI trust.
This shows their industry engagement beyond written analysis.
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Start here to see their presence in partner ecosystems, distribution recognition, and event-based conversations about grounded AI adoption.
Eric Victor Gitter recaps Lenovo Accelerate North America and highlights several moments: the Lenovo MSP Advisory Council, which they describe as candid and productive; Ingram Micro being recognized as North America Distributor of the Year; a talk by Cindy Parlow Cone on authentic leadership; a U.S.
Department of Energy session with Carl Coe on business and government; and a panel with Jeff, Reem Gedeon, and Dilip Bhatia.
They praise Reem Gedeon's team for pushing AI forward with a clear plan and Dilip Bhatia for focusing on using AI to improve outcomes and reduce friction for partners.
They close with conversations among people equally obsessed with AI but convinced it must be grounded in reality.
For brands planning a campaign
This shows how Eric Victor Gitter participates in channel events and frames partner-facing AI discussions around practical outcomes and candid advisory work. A campaign could use an event recap, advisory board, or partner panel to connect AI infrastructure strategy to real channel conversations rather than abstract thought leadership.
The half of AI infrastructure spending that isn't chips
Breaks the roughly $6.7 trillion data-center buildout through 2030 into its non-silicon half: power infrastructure at 15-25% of spend, cooling at 10-15%, and facilities and real estate at 5-10%.
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The specifics make the case: AI racks draw 5-10x the power density of traditional racks, utilities face a $1.3 trillion capital call, air cooling is described as finished in favor of direct-to-chip and immersion systems, and builders are projected to spend $800 billion on physical structures. The post calls this the best opening in two decades for medium-sized industrial and energy supply chain businesses that can deliver specialized construction, cooling innovation, and immediate grid connection—and says those businesses are the IT channel's opportunity.
Takes a large public spending estimate, breaks it down by percentage, and names supplier categories for each slice.
For brands planning a campaign
This is a strong spine for a session on the shift from compute to power and cooling, with figures for each layer. A technology distribution company can use the power and cooling percentages to broaden its AI infrastructure strategy beyond GPU resale and identify mid-market industrial and energy suppliers as prospective partners.
Hyperscaler capacity lock-up and the two-tier channel market
Describes OpenAI's Stargate memory agreements with Samsung Semiconductor and SK hynix at 900,000 wafers per month, noting that the two suppliers were reportedly unaware of each other's commitments until the announcement and that Micron ended its consumer brand as the three companies making up over 90% of world memory supply concentrate on a few AI customers.
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The post's thesis: the squeeze moves from GPUs to memory to electricity, and the market is splitting into companies that work with hyperscalers and everyone else competing for what remains.
Its advice is direct: MSPs and systems integrators building on AWS, Microsoft Azure, or Google Cloud inherit capacity that is already secured.
Build a hyperscaler practice if you do not have one, double down if you do, and position for sovereign AI and edge deployments that hyperscalers will not own.
It rejects tactical pivots and asks where the reader is building.
For brands planning a campaign
This is the strongest source for the requested hyperscaler-practice session. Its two-tier framing gives the session structure, the memory and wafer figures provide concrete supply evidence, and its build, double-down, and edge/sovereign guidance gives a distribution company an action list to put in front of partners.
Trust and authenticity as content becomes synthetic by default
Argues that content is no longer merely generated but fabricated, personalized, infinitely multiplied, and spread by bots, enterprises, governments, platforms, and increasingly by trusted people—and that the line between real and synthetic has effectively disappeared.
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Cites that 80% of consumers cannot distinguish AI-generated content from reality and that trust in digital experiences is falling for the first time in a decade.
It says an enterprise's brand is only as trustworthy as its content supply chain.
The post notes government debates over watermarking, cryptographic provenance, and legal liability for deepfakes, and frames the paradox: synthetic content is one of AI's biggest value propositions, but becomes destabilizing when nothing can be verified.
It ends with a personal worry that people are losing the instinct to question what they see.
A risk essay built on statistics and a paradox, ending with a personal observation rather than a recommendation.
The only post focused on a society-level trust problem rather than IT-channel positioning; it does not address MSPs, integrators, or distributors.
For brands planning a campaign
This is off-brief for a distribution client's AI infrastructure planning, but offers a governance and trust lens on the same AI transition if the session needs a broader frame. It does not provide channel strategy.
Event coverage and partner-network visibility as a format
Recaps Lenovo Accelerate North America, thanking Matthew, Pascal, Jeff, and Arsalan and their teams, then listing specific callouts: the Lenovo MSP Advisory Council alongside Zvon Petric, Chris Black, and Jay McBain; Ingram Micro recognized as North America Distributor of the Year; Cindy Parlow Cone on authentic leadership and technology's connection to the future of sport; Carl Coe of the U.S.
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Department of Energy on where business and government intersect; and a panel with Jeff, Reem Gedeon, and Dilip Bhatia on using AI to improve partner outcomes rather than just discussing it. The post says these grounded conversations are why the writer attends events.
A conference recap in short numbered callouts, combining thanks to hosts, named recognition, and a view on which sessions mattered.
For brands planning a campaign
Shows where this audience is reachable and which topics this writer elevates: grounded AI adoption, partner economics, distribution recognition, and the business-government intersection. A distribution company planning event sponsorship or partner-facing sessions can use it to gauge the room and identify sessions likely to be amplified.
Recurring method: market number, channel split, direct question
Two market-analysis posts follow the same shape: open with a large public figure, reinterpret it as a split the channel should care about, turn it into an instruction for MSPs, systems integrators, and distributors, then ask about the reader's own positioning.
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The infrastructure post moves from the $6.7 trillion buildout to power, cooling, and facilities shares, then identifies medium-sized industrial and energy businesses as the channel's opportunity. The memory post moves from 900,000 wafers per month and over 90% market concentration to the two-tier market, the instruction to build or deepen a hyperscaler practice, and the closing question about where the reader is building.
Opens with a checkable public number, reframes it as a split relevant to partners, then closes with a first-person question about the reader's own strategy.
For brands planning a campaign
A brand session modeled on this structure will fit the writer's register: lead with the market number, translate it into a partner decision, and end with a question that invites the audience to describe its own positioning rather than a generic call to action.
The posts show Gitter's direct participation in a Lenovo-hosted industry event, a personal connection to Ingram Micro, and commentary on AI infrastructure suppliers.
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They also discuss NVIDIA, OpenAI, Samsung, SK hynix, Micron Technology, Amazon Web Services, Microsoft Azure, and Google Cloud as subjects of analysis.
No sponsorship, affiliate, client, investment, or disclosed-partnership arrangement is described.
Ask them to clarify whether their Lenovo or Ingram Micro connections involve employment, paid advice, or another commercial arrangement.
Lenovo
Mention
Gitter attended Lenovo Accelerate North America, thanked the Lenovo staff who organized it, and said they were honored to sit on the Lenovo MSP Advisory Council alongside other channel figures.
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They also highlighted a Lenovo panel session and praised Lenovo's AI work. The posts don't clarify whether they work for Lenovo, advise it under contract, or participate as an industry guest; ask them to clarify their relationship to Lenovo.
Gitter wrote that they were "extremely proud to see Ingram Micro recognized as North America Distributor of the Year" at Lenovo Accelerate North America.
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The posts don't clarify whether their connection to Ingram Micro is through employment, a partnership, or another arrangement; ask them to clarify their relationship.
In a post on the $7 trillion AI infrastructure race, Gitter notes that attention centers on NVIDIA's margins while the larger opportunity lies in power, cooling, and facilities.
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NVIDIA is an industry subject in their analysis; the post doesn't state a commercial relationship.
Eric Victor Gitter's posts are easiest to follow as market analysis for people who sell and deliver technology.
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Two posts carry the most weight: they start with a large public number — a projected $6.7 trillion data-center buildout or OpenAI's reported 900,000 memory wafers per month — break it into parts that matter to the channel, and ask readers where their own business is positioned.
Read those first.
A shorter post on synthetic content and trust shows a second register: social and governance risk rather than channel strategy, with no action list for partners.
The Lenovo Accelerate recap shows how they use event posts — crediting hosts, naming partners and speakers, and noting which sessions focused on practical AI adoption.
Together, the four posts suggest that readers seeking infrastructure economics with a go-to-market conclusion should follow the market-analysis posts; those looking for broad AI-society commentary will find less to work with.
Warren also builds LinkedIn posts around hyperscaler infrastructure numbers — average memory per server instance by region and a 14.3% rise in AWS spot prices over three months — and interprets where AI compute demand is heading.
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Both writers use a concrete data point to open a market argument.
Warren's posts read as industry-intelligence marketing for a data provider, with the figures and offer to share more data as the payoff. Gitter takes a comparable infrastructure fact and turns it into a channel decision: build or deepen a hyperscaler practice, or sell into the constraint.
Allen approaches physical constraints from the infrastructure side: UK grid limits, planning reform, supply chain delivery, skills, and a Capacity Europe panel titled 'Scaling Under Scarcity' about power equipment, chips, cooling and supply chains.
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Both treat delivery bottlenecks, not just demand, as the story.
Allen focuses on UK and European market-building and quantum readiness, with much of the material devoted to event and council visibility. Gitter focuses on the North American IT channel and how distributors, MSPs and integrators should reposition around whoever already holds capacity.
Freedman makes the same core argument in more technical language: power was once an optimization variable and is now the constraint, with power, cooling, water, grid interconnects and dense-compute operations shaping where AI workloads can actually run.
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Both writers argue that the physical plant now sets the limits of the compute layer.
Freedman writes as a distributed-systems practitioner about the data layer and real-time operational control loops, naming Tiger Data and Inductive Automation as his working context. Gitter stays at the market and channel level, offering partners positioning advice rather than architecture.
Eric Victor Gitter's posts support a clear decision about subject-matter relevance, but only a limited one about measured audience composition.
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Their LinkedIn following is the only measured audience figure available: 3,955 followers on 8 October 2026.
The posts provide no platform demographics, audience survey or post-level engagement counts, so the following's age, location, job function and seniority remain unverified.
The four posts show who the writing addresses.
Two — a breakdown of the roughly $6.7–7 trillion AI data-center build and an analysis of OpenAI's memory-supply agreements — speak directly to resellers, managed service providers, systems integrators and data-center infrastructure suppliers.
They advise these readers to reposition around power, cooling and hyperscaler capacity rather than compete for constrained hardware.
A third post recaps a Lenovo-hosted industry event, including an MSP advisory council and a distributor-of-the-year recognition, material likely to circulate among the same channel readership.
A fourth post covers AI-generated content and trust, showing the feed is not exclusively focused on AI infrastructure.
The posts run from 6 October 2025 to 24 April 2026: four posts across roughly seven months.
That thin, irregular record is not a reliable basis for forecasting posting volume around a sponsored session.
Treat the follower count as a measure of scale, not proof of audience fit, and the channel-facing tone as intended readership, not a measured audience profile.
For a distribution company briefing on AI infrastructure partner strategy, Gitter is a plausible match on topic and address.
Ask for recent LinkedIn analytics, including follower breakdowns by seniority, function and location, plus impressions and engagement for comparable AI infrastructure posts.
Because LinkedIn's organic follower data often lacks job-function details, also ask whether Gitter has audience survey results or another documented source on employer, occupation or seniority.
Request the specific recent posts a sponsor would appear alongside, rather than follower totals, to judge whether the readership matches a campaign aimed at distribution companies briefing partners on AI infrastructure strategy.
The posts do not describe a partnership or sponsorship program for Eric Victor Gitter.
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They cover AI infrastructure, technology distribution, and industry events, and mention companies such as Lenovo, Ingram Micro, Samsung, SK hynix, Micron, AWS, Microsoft Azure, and Google Cloud as subjects or event participants. For a sponsorship, ask about available formats, terms, and the inquiry process.
Good fit: a technology distribution company planning a partner-facing session on AI infrastructure partner strategy, the shift from compute to power and cooling, and how to build a hyperscaler practice.
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Why it fits.
This is already the core of Eric Victor Gitter's published analysis, so the session does not require new research from scratch.
The post on the roughly $6.7 trillion data-centre buildout gives the session its spine: about half of that spending is not silicon or compute, with power infrastructure at 15-25%, cooling at 10-15%, and facilities and real estate at 5-10%.
It also lays out the pressures the session can use directly: AI racks drawing 5-10x the power density of traditional racks, a $1.3 trillion capital call on utilities, $800 billion projected on physical structures by 2030, and the shift away from air cooling toward direct-to-chip and immersion systems.
The post on OpenAI's memory agreements gives the second act and the commercial argument: 900,000 wafers per month across two suppliers, three companies making up over 90% of world memory supply focusing on a few AI customers, a squeeze that moves from GPUs to memory to electricity, and a market split into companies working with hyperscalers and everyone else competing for constrained supply.
That post also carries the action list: build a hyperscaler practice on AWS, Microsoft Azure, or Google Cloud if one does not exist, double down if it does, and position for sovereign AI and edge work hyperscalers will not own.
The address matches.
Two posts speak directly to resellers, managed service providers, systems integrators, and data-centre infrastructure suppliers—the readership a distribution company wants to reach.
The Lenovo Accelerate recap shows the same audience in person: an MSP advisory council, a distributor-of-the-year recognition for Ingram Micro, and panels on AI improving partner outcomes.
It also shows a working event format and a stated preference for AI grounded in reality rather than hype, useful if the session script is framed as a technology showcase.
Before booking, agree commercial terms independently; no sponsorship program, rate card, or disclosed paid format appears in the posts.
Ask for engagement and impression data before using four posts across roughly seven months to forecast publishing volume.
This is a strong topic-and-format match for one well-researched partner session, not an always-on content commitment.
When to choose a specialist
Poor fit: a sponsor wanting an always-on, disclosed paid content series with audience guarantees—for example, a quarterly run of sponsored product-demo videos targeted at named job titles, with an agreed posting cadence and performance commitments.
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Why it does not fit.
No commercial format is documented.
The posts mention Lenovo, Ingram Micro, Samsung, SK hynix, Micron, AWS, Microsoft Azure, and Google Cloud as subjects of analysis or event participants; none shows paid promotion, sponsorship terms, or an inquiry process.
A buyer would be negotiating from zero rather than slotting into an existing offer.
The requested format is also absent.
The posts consist of strategic market analysis and one conference recap.
There is no product demo, tutorial, on-camera video, or audio material to assess for fit.
The requested guarantees cannot be supported.
The only measured audience figure published is a follower count of 3,955.
There is no breakdown by seniority, function, or location; no impression or engagement counts per post; and no audience survey.
Ask for audience and performance data before committing to job-title or regional reach.
Posting volume cannot support an always-on schedule.
Four posts across roughly seven months, with no engagement data, is not a basis for promising a recurring sponsored series.
Part of the campaign is also off-topic.
One post concerns synthetic content and trust rather than AI infrastructure, and it is the one piece that offers no channel action.
A sponsor expecting consistent infrastructure commentary should not assume every placement will stay on that topic.
A worked campaign example
Worked campaign angle for a partner session: two published arguments, plus a distribution-side worksheet.
Working title: Where the AI buildout actually spends, and what your partners should own.
Opening (the market number).
Lead with the $6.7 trillion projected spend on AI-ready data centres through 2030 and the split that does the work: roughly half to silicon and compute, 15-25% to power infrastructure, 10-15% to cooling, 5-10% to facilities and real estate.
Give the three pressures that make the split credible: AI racks at 5-10x traditional power density, a $1.3 trillion utility capital call, and $800 billion on physical structures.
Label these as the writer's published estimates rather than industry consensus, since they come from the creator's own analysis.
Middle (the supply squeeze and the two-tier split).
Move to memory: 900,000 wafers per month across OpenAI's agreements with Samsung Semiconductor and SK hynix, three suppliers making over 90% of world memory, and those suppliers concentrating on a few AI customers.
State the progression the writer uses—GPUs, then memory, then electricity—and the resulting split into companies working with hyperscalers and everyone else competing for constrained supply.
This pivot makes the session about partner strategy rather than hardware.
Close (the decision and the question).
Give the action list from the published post: build a hyperscaler practice on AWS, Microsoft Azure, or Google Cloud if the firm does not have one; deepen it if it does; and claim what hyperscalers will not own, meaning sovereign AI, edge deployments, local data centres, and the relationships around them.
End with the writer's own closing device, a first-person question to the room, converted into a worksheet.
Distribution-side deliverables.
A 45-minute session for partner principals and practice leads.
A one-page power-and-cooling map partners complete for their own territory, listing which industrial, energy, and cooling suppliers they already know and which they can reach.
A follow-up advisory slot where partners are sorted into two groups—those exposed to constrained hardware supply and those aligned with cloud capacity—and each group leaves with a stated next move.
Why this angle fits the writer.
It reuses arguments already published, so the session does not depend on new reporting.
It addresses all three needs: where the money goes beyond chips, how to build a hyperscaler practice, and how partners can position around it.
It also keeps the grounded register the writer says they prefer in the Lenovo recap rather than a hype-led product pitch.
Before booking, confirm the writer will present the power and cooling percentages directly; the posts are the source of those figures, and no independent study is cited.
Confirm willingness to run an event-style partner session, which the Lenovo recap supports as a format but not as a paid one.
Agree sponsorship terms separately; no offer is documented in the posts.
Choose Trent Warren when the session needs to show that AI infrastructure demand is measurable, not just argued.
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Both posts draw on Liftr Insights data.
One shows average memory per server instance differing sharply by region: a new region such as Colombia exceeds roughly 600 GB while the U.S. stays below 400 GB.
It explains the gap through legacy fleet drag against greenfield builds, AI-shaped server design, memory vendor economics, and capacity-constrained operators packing more memory per rack.
The other tracks AWS spot prices rising about 14.3% over three months, from roughly $0.84 to nearly $0.96, attributing the climb to generative AI training and inference absorbing spare capacity, advanced GPU packaging and data-centre power limits, and enterprises locking capacity in reserved blocks.
For a partner campaign, the posts offer a distribution company partner-facing numbers and a supply-and-demand story, plus a moderator who ends by offering global data across cloud service providers and semiconductors.
The frame is market intelligence for investors and operators, not partner enablement.
Neither post addresses resellers, managed service providers, or distributors, or offers a hyperscaler-practice playbook: there is no advice on building on AWS, Microsoft Azure, or Google Cloud, and no guidance on claiming the sovereign and edge work hyperscalers will not own.
The evidence is strong, but a distribution company would have to translate it into partner actions.
Both posts focus narrowly on memory density and spot pricing; ask for additional material before booking a broader AI infrastructure partner session.
Choose Simon Allen when the room is UK or European and the immediate challenge is delivering projects under scarcity.
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One post recaps Platform UK, with a deliberately domestic focus on planning reform, grid constraints, regional growth, investment appetite, supply chain delivery, and skills.
It treats quantum readiness as a practical infrastructure question involving power, cooling, site design, and long-term strategy, rather than distant hype.
The other previews Capacity Europe 2026, where they moderate a panel titled Scaling Under Scarcity: Delivering Projects Amid Shortages.
It will cover power equipment, chips, cooling, supply chains, and skilled resources, and examine standardisation, alternative sourcing, and smarter procurement.
For a partner campaign, this supports a chaired panel and regional policy frame, with a speaker who can lead a supplier-and-operator conversation about constraints instead of presenting a product.
The posts contain almost no hyperscaler-practice or channel-partner content: they do not address building a practice on AWS, Microsoft Azure, or Google Cloud, or speak to managed service providers, systems integrators, or distributors.
They also offer no market sizing comparable to the figures in Eric Victor Gitter's posts.
The focus on European operators and suppliers may transfer less directly to a distribution company with U.S.-heavy partners, while the quantum-readiness thread may sit outside a near-term partner strategy.
Ask for channel-specific examples before booking a partner campaign; event participation and moderation alone do not provide the strategic argument such a campaign needs.
Choose Michael Freedman when the audience is technical and the session needs to explain why power became the binding constraint and how that changes system design.
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The posts argue that power has shifted from an optimization variable to the constraint itself, turning a facilities issue into a complex systems problem spanning power, cooling, water, grid interconnects, backup generation, and dense-compute reliability.
They say hyperscalers have denied server expansion requests because of regional power capacity limits, not a lack of hardware or demand.
Freedman connects this to their work at Tiger Data, creators of TimescaleDB, describing the operational data layer moving from the periphery to the centre, and a coming talk at the Ignition Community Conference on combining industrial control with Postgres.
For a partner campaign, this suits data platform, systems architecture, or industrial engineering audiences evaluating where compute can actually run.
The posts offer little commercial or channel content: no market sizing, supplier concentration analysis, buying guidance, or advice on choosing cloud partners.
Their conceptual, first-person argument about architecture may read as a product-adjacent technical talk rather than a partner-market briefing.
Both are anchored in the writer's employer and its product, so a distribution company should expect a focus on architecture and data infrastructure rather than the distribution channel; ask for channel-specific examples if those are essential.
Gitter's LinkedIn posts cover four topics: two on AI infrastructure and hyperscaler supply, one on synthetic content and trust, and one conference recap.
The posts do not display publication dates, so no chronology is implied.