Michael Freedman works on data infrastructure for industrial and AI systems at Tiger Data, the company behind TimescaleDB. He writes and speaks about connecting industrial control systems and machine data with PostgreSQL for real-time operations and historical analysis.
8K followersAI data centersBased on 13 posts · latest Sep 2026
They are most useful for technical content about industrial control, PostgreSQL, and time-series data for AI systems.
Best for
A data infrastructure company seeking a technical partnership or content collaboration on industrial AI systems.
Not for
A consumer AI writing assistant wanting paid LinkedIn sponsorship with discount code and general AI productivity message.
Key takeaways
Power is becoming the binding constraint on AI data centers, not just an optimization variable.
Industrial control and Postgres belong together because machine data needs history and relational context.
Titan America uses TimescaleDB for SCADA readings, dashboards, and 19 TB of time-series data.
Check first: Confirm recent results and audience demographics for comparable industrial, manufacturing, or time-series work.
Main ideas
Power is the binding constraint on AI data centers, not an optimization variable · Industrial control and Postgres belong in one system because machine data needs history and relational context together · Customer proof should show specific operational scale, not a generic testimonial
Michael Freedman writes and speaks about data infrastructure for industrial and AI systems, especially how PostgreSQL-based time-series data can connect machine and sensor readings with the context operators need.
The posts make the case that power, cooling, and other physical limits are part of AI system design, not just facilities concerns.
They also point to practical industrial examples, including Titan America’s reported use of TimescaleDB for SCADA data, dashboards, and 19 TB of time-series data.
Freedman connects infrastructure trends to operational data needs.
In the AI data center post, they trace a shift from treating power as something to optimize to treating it as a constraint, then explain why power, cooling, and equipment state belong in system planning and real-time operations.
The industrial posts make the same argument from another angle: the Ignition Enterprise Historian partnership illustrates a productized collaboration, while the Titan America example gives concrete scale and use cases.
Conference and revenue updates show positioning and business context, rather than independently tested results.
Readers interested in how industrial systems and AI infrastructure depend on data can follow their work for that connection between physical operations and database architecture.
Michael Freedman argues that physical infrastructure—power, cooling, water, grid capacity, and equipment state—has become part of the compute architecture rather than a background utility.
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From that premise, they argue that industrial machine data belongs in the same database system as business context, with specific operational deployments such as Titan America’s SCADA data as proof. Timing is another priority: Freedman ties demand to an AI-driven re-industrialization wave across manufacturing, energy, robotics, and data centers.
Power is the binding constraint on AI data centers, not an optimization variable
Freedman’s argument is that, for years, compute treated power as something to optimize; now power itself is the constraint.
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AI data center challenges are moving out of the facilities department and into systems architecture: power, cooling, water, grid interconnects, backup generation, and the ability to run dense compute reliably set the digital performance envelope.
Operational details such as power, cooling, capacity, utilization, and equipment state are no longer just dashboard metrics; they’re part of the architecture.
They also describe this as a real-time control problem as power and heat density rise and more facilities move toward liquid cooling.
Freedman points to hyperscalers denying server expansion requests because of regional power capacity limits, rather than hardware or demand, and to the proposed $67 billion NextEra–Dominion deal as a direct response to AI data center electricity requirements.
The post ends with “More to write,” promising a follow-up argument that isn’t included.
Industrial control and Postgres belong in one system because machine data needs history and relational context together
Freedman’s work centers on pairing industrial control software with Postgres that can scale to the volume and history of the physical world.
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Their reasoning: real-time data alone isn’t enough; systems also need historical data combined with the relational context that gives it meaning. They frame this as an architecture question—how the stack got here and where it goes next—aimed at controls engineers and system integrators as much as software buyers.
Freedman spoke at the Ignition Community Conference about pairing Inductive Automation with Tiger Data, the company behind TimescaleDB.
After the event, they described being invited onstage by Inductive Automation CEO Colby Clegg during the keynote, in front of more than 2,000 attendees, to announce a first-party Ignition Enterprise Historian powered by TimescaleDB Enterprise.
They also reported meeting existing open-source TimescaleDB users across data centers, energy, oil and gas, and manufacturing.
Customer proof should show specific operational scale, not a generic testimonial
Freedman uses named industrial customers and hard operational figures as evidence.
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The point is to show machine data running at real volume, with the database layer supporting both real-time dashboards and business reporting while managing storage tiers. The concrete opening—much of the modern world is built on concrete, and some of that concrete runs on Postgres—signals that heavy industry is the intended proof, not a software-only reference.
Freedman describes Titan America using Tiger Data and TimescaleDB as the data foundation for its manufacturing operations: millions of SCADA sensor readings per day, continuous aggregates for real-time dashboards and business reporting, and 19 TB of time-series data managed across hot and tiered storage. The post links to a case study but doesn’t include it; the figures are reported as Freedman stated them.
AI is driving a re-industrialization wave that needs machine, sensor, and operations data infrastructure
Freedman’s commercial argument is that AI isn’t only changing software; it’s driving a re-industrialization wave that needs modern data infrastructure for machines, sensors, operations, and real-time intelligence.
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Demand is coming from physical-industry customers, not only software, making industrial data infrastructure a growth thesis rather than a niche.
In a quarterly update, Freedman reports roughly twice expected revenue growth, the largest net revenue growth month so far in April, and acceleration at scale, driven by industrial, manufacturing, energy, robotics, and data centers.
They close with the thesis in one line: the physical world is coming online, and it runs on data.
The figures are company-reported in Freedman’s own post; ask for independently audited figures if that matters to your decision.
This is the shortest statement of what Freedman works on: not general data infrastructure, but the specific problem of holding machine and sensor history alongside business context.
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A newcomer should start here: it names the software vendor and database vendor, and explains what they are trying to do together.
Freedman announces a talk at the Ignition Community Conference about the volume of real-time data coming out of AI infrastructure, energy, manufacturing, and robotics, and about pairing industrial control software with Postgres so a system can keep both its history and the relational context that makes that history meaningful.
The post names the pairing explicitly: Inductive Automation together with Tiger Data (creators of TimescaleDB).
It says the talk will cover the architecture, how the industry arrived at it, and where it goes next.
This is a preview of a live event, not a finished argument.
For brands planning a campaign
For a data infrastructure company weighing a technical partnership or content collaboration, this post shows the kind of collaboration Michael Freedman already promotes: a joint architecture story at a customer-facing industry event, positioning the database layer as the home for operational history. A joint technical narrative looks like the natural first deliverable, rather than a standalone product pitch.
This is where the company positioning gets its rationale.
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It answers a technical buyer’s question — why would a time-series database matter to an AI data center operator? — by showing that the physical plant now defines what compute can do, moving operational data from the periphery to the center of the system.
A long argument that power has shifted from an optimization variable to the binding constraint on AI infrastructure.
Drawing on a background in distributed systems, networking, and storage — server utilization, network bisection, storage placement, scheduling, cluster orchestration — Freedman notes that earlier green-computing work in the late 2000s still treated energy as something to tune.
The change now shows up in physical infrastructure: power, cooling, water, grid interconnects, backup generation, and the ability to run dense compute reliably.
Two supporting examples: a proposed $67 billion NextEra–Dominion deal, read as a response to data center electricity demand, and hyperscalers declining server expansion requests because of regional power capacity rather than hardware or demand.
The conclusion: power, cooling, capacity, utilization, and equipment state are no longer just dashboard metrics; they are part of the architecture, requiring tighter control loops as densities rise and liquid cooling spreads.
For brands planning a campaign
The concrete buying angle is operational telemetry: power, thermal, cooling, and equipment state data that operators need for planning, failure investigation, and efficiency work. A partner whose offering touches energy, power management, or industrial monitoring can pitch against this framing rather than competing on generic database performance.
This is the clearest, most checkable evidence behind claims made elsewhere: a named customer, system, data volumes, and storage behavior.
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Newcomers who want to verify the story before reading the arguments should start here.
A short customer example: Titan America uses Tiger Data (creators of TimescaleDB) as the data foundation for manufacturing operations, with millions of SCADA sensor readings per day, continuous aggregates feeding real-time dashboards and business reporting, and 19 TB of time-series data managed across hot and tiered storage. The post links to the example with a one-line framing about concrete running on Postgres.
For brands planning a campaign
This is a ready reference case in industrial manufacturing, and the numbers give a partner concrete benchmarks for a joint case study or benchmark comparison. The tiered-storage detail offers a specific technical hook a partner could support or contrast with.
The earlier conference post has become a shipped, productized partnership rather than a one-off talk, with the executive and audience named.
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That marks the difference between stated interest and a standing commercial relationship.
A conference recap in which Freedman says they joined Inductive Automation CEO Colby Clegg on stage during the keynote, before more than 2,000 attendees, to announce a new product offering and strategic partnership: the first-party Ignition Enterprise Historian, powered by TimescaleDB Enterprise. The post also reports meeting existing open-source TimescaleDB users across data centers, energy, oil and gas, and manufacturing, and presents the moment as a shift for the modern industrial data stack.
For brands planning a campaign
Freedman is willing to announce and co-present a joint product on a keynote stage, making a co-marketing or launch partnership a realistic ask. The named verticals — data centers, energy, oil and gas, manufacturing — point to where an existing user base and event presence can carry a joint message.
This provides the commercial context behind the technical posts: demand is coming from physical-industry customers, not only software.
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Anyone assessing whether the industrial data story is a niche or a direction should read this alongside the customer example.
A results post reports roughly twice expected revenue growth, describing April as the largest net revenue growth month so far and naming industrial, manufacturing, energy, robotics, and data centers as drivers.
Freedman frames this as an AI-driven re-industrialization wave that needs modern data infrastructure for machines, sensors, operations, and real-time intelligence, and says the same pattern is visible in users.
The post is an announcement, not an analysis; it gives no figures beyond the growth description.
For brands planning a campaign
The case for partnering now is momentum in the segments a technical partner would target.
A joint offer could focus on industrial, energy, and robotics accounts.
The post gives no audited numbers, so ask for substantiation before citing it as financial evidence.
Communication style: first-person essays, partnership announcements and milestone updates5
Industrial control plus Postgres: the Inductive Automation partnership2
Customer evidence from heavy industry: Titan America's SCADA data at scale2
Power is becoming the constraint on AI data centers, not just an optimization variable1
The sector thesis: AI is driving a re-industrialization wave that needs machine data infrastructure1
Power is becoming the constraint on AI data centers, not just an optimization variable
Freedman's longest post is an argument, not an announcement: for years, compute treated power as something to optimize; now power itself is the constraint.
Read more
They describe the shift as moving AI data center problems out of the facilities department and into systems architecture, where power, cooling, water, grid interconnects, backup generation and the ability to run dense compute reliably set the digital performance envelope.
Their examples include hyperscalers turning down server expansion requests because of regional power capacity limits rather than hardware or demand, and the proposed $67B NextEra–Dominion deal, which they read as a direct response to AI electricity demand.
They also argue that rising power and heat density, alongside more facilities moving toward liquid cooling, make this a real-time control problem and turn operational data into part of the architecture rather than a dashboard metric.
Freedman draws on their academic background in distributed systems, networking, storage and scheduling, then contrasts today's framing with the late-2000s green-computing wave, when power was treated as an optimization variable.
The post closes with "More to write," making clear that the argument is unfinished and a follow-up is promised but not yet present.
For brands planning a campaign
A vendor selling power monitoring, cooling management, facility telemetry or grid-facing software has a ready-made frame here: physical-plant data belongs inside the compute architecture. A collaboration pitch should engage that coupling claim directly, with a concrete industrial example, rather than lead with general AI growth statistics.
Industrial control plus Postgres: the Inductive Automation partnership
Two posts cover the same relationship from different angles.
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Before the event, Freedman described speaking at the Ignition Community Conference about pairing industrial control with Postgres that can scale to the volume and history of the physical world, combining real-time data with the relational context that gives it meaning, and discussing "the architecture, how we got here, and where it's going next."
After the event, they described being invited onstage by Inductive Automation CEO Colby Clegg during the keynote to announce a new product offering and strategic partnership in front of 2,000+ attendees: the first-party Ignition Enterprise Historian, powered by TimescaleDB Enterprise.
They noted excitement from existing open-source TimescaleDB users across data centers, energy, oil and gas, and manufacturing.
Announcement posts paired with an architecture talk, aimed at controls engineers and system integrators as much as software buyers.
For brands planning a campaign
This is direct evidence that Freedman's employer already co-markets a named integration, announced jointly with a partner executive. A time-series or data infrastructure company pitching a technical partnership should expect the conversation to center on historical-plus-relational data as the product story and could propose a joint launch or conference moment in the same format.
Customer evidence from heavy industry: Titan America's SCADA data at scale
Freedman uses named industrial customers as proof rather than making generic claims.
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In one post, they describe Titan America using Tiger Data and TimescaleDB as the data foundation for its manufacturing operations, citing millions of SCADA sensor readings per day, continuous aggregates for real-time dashboards and business reporting, and 19 TB of time-series data managed across hot and tiered storage. They open with a concrete image rather than a product pitch, noting that much of the modern world is built on concrete and that some of that concrete apparently runs on Postgres.
The post points to a shortened link, and the underlying case study is not included. Ask for the case study to verify the figures Freedman cites.
For brands planning a campaign
The reusable pattern is a quantified customer story about an industrial operator, not a testimonial quote. A partner with a comparable reference customer and hard operational numbers could fit this format; without one, a partner would need a different angle, such as an architecture comparison or technical demonstration.
The sector thesis: AI is driving a re-industrialization wave that needs machine data infrastructure
Freedman reports a quarter with roughly twice expected revenue growth, the largest net revenue growth month the company had seen in April, and growth accelerating at scale, attributing it to industrial, manufacturing, energy, robotics and datacenter customers.
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They argue that AI is not only changing software but driving a re-industrialization wave that needs modern data infrastructure for machines, sensors, operations and real-time intelligence. They close with the thesis in one line: the physical world is coming online and it runs on data.
Freedman says the growth figures are company-reported. Ask for supporting data if independent verification matters to the collaboration.
For brands planning a campaign
A brand partnering here would be buying into this market thesis. Pitch material should address machine, sensor and operational data infrastructure rather than a software-only AI narrative, and can reference the same customer sectors Freedman names.
Communication style: first-person essays, partnership announcements and milestone updates
The posts show a consistent, focused set of formats.
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The longest and most distinctive is the argument essay, written in the first person and built from Freedman's technical background into a market-level claim, supported by named deals, customers and constraints.
Other formats include short conference and product announcements naming partner executives and audience size, customer stories with specific metrics, and brief business updates tying company performance to a sector thesis.
Across them runs the same claim: operational and historical data from industrial systems is becoming core infrastructure, and Postgres or time-series tooling is where it should live.
First-person LinkedIn posts mixing personal background, market evidence and product positioning.
Before commissioning a collaboration, ask for publication dates and talk transcripts if you need to assess how the arguments have changed over time or evaluate the conference talks beyond their announcements.
For brands planning a campaign
A brand evaluating a content collaboration should expect to join an existing argument rather than get a standalone promotional slot. The best fit is a specific technical or customer example that supports the physical-world-data thesis; broad awareness messaging is a weaker match for how Freedman writes.
Michael Freedman's LinkedIn posts document their affiliation with Tiger Data, a strategic partnership between Tiger Data and Inductive Automation, and a customer case study with Titan America.
Tiger Data
Employment
Freedman writes from inside Tiger Data: 'At Tiger Data (creators of TimescaleDB), we are experiencing this firsthand.' An earlier post promotes an Inductive Automation + Tiger Data integration, and another says Titan America uses Tiger Data as its manufacturing data foundation.
Tiger Data and Inductive Automation announced a strategic partnership and a first-party Ignition Enterprise Historian powered by TimescaleDB Enterprise.
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Freedman joined Inductive Automation CEO Colby Clegg onstage at the Ignition Community Conference. They had earlier said they would speak about controls engineers and system integrators building with Inductive Automation and Tiger Data.
Titan America uses Tiger Data (creators of TimescaleDB) as the data foundation for its manufacturing operations, managing millions of SCADA sensor readings a day, continuous aggregates for dashboards and reporting, and 19 TB of time-series data across hot and tiered storage.
The longest and most distinctive is a first-person argument that power, cooling, water, and grid capacity have stopped being background utilities and become part of the compute architecture, with hyperscalers turning down server expansion because of regional power limits rather than hardware shortages.
The shorter posts include announcements around the Ignition Community Conference and the Inductive Automation partnership, a customer story about Titan America running SCADA data on TimescaleDB, and a quarterly update tying company growth to industrial, manufacturing, energy, robotics, and data center demand.
Start with the power-as-constraint argument, then the two conference posts for the partnership shape, the Titan America post for operational scale, and the quarterly update for the sector thesis.
The through-line is that machine, sensor, and operations data needs the same database as business context, and that this demand comes from physical industry rather than software alone.
AI data centersData center power constraintsIndustrial data infrastructureTime-series dataSCADA and operational technology dataRe-industrializationPostgreSQLTimescaleDBIndustrial automationFirst-person argument postsConference talks and keynotesControls engineers and system integratorsData infrastructure buyers
Simon Allen
Both write about the physical constraints shaping digital and AI infrastructure.
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Allen's posts cover grid constraints, power equipment, cooling, supply chains, and skills shortages that decide whether projects get delivered—the same coupling between compute and the physical plant that Freedman describes.
Allen writes from an industry-event and advisory vantage point, co-founding the Quantum Infrastructure Council and moderating a Capacity Europe panel on delivering projects amid shortages. Freedman writes from the data-software layer, connecting power limits to database architecture and named industrial customer deployments.
Both analyze AI infrastructure through industry data, covering hyperscaler capacity, memory density, GPU packaging, and data center power limits.
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Warren explains that AWS spot prices are rising because AI workloads absorb spare compute and power bottlenecks slow new hardware—the same capacity argument Freedman makes from the operator side.
Warren presents proprietary market-intelligence findings from Liftr Insights and closes by inviting readers to talk about competitive intelligence. Freedman's posts are first-person architecture arguments anchored to their employer's customers and partnerships, rather than a data-subscription pitch.
Both follow the physical buildout of AI beyond models and chips.
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Yan traces how AI demand pulls in power generation, transformers, and thermal management, naming Generac and Modine as industrial beneficiaries. Freedman makes a parallel case that the constraint has moved to power, cooling, and grid interconnects.
Yan frames this as an investment thesis, comparing China, the United States, and Japan and listing venture opportunities. Freedman writes about the operational data layer, using SCADA and time-series examples from manufacturing rather than capital allocation.
The only measured audience figure available is Michael Freedman's LinkedIn follower count: 8,028 as of 23 October 2026.
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It offers a realistic reach estimate for a creator with a narrow, technical audience, but not a picture of who those followers are.
The five posts target people who run industrial data systems: controls engineers and system integrators working with Inductive Automation and Tiger Data; manufacturers pulling millions of SCADA readings a day into dashboards and reporting; and engineers weighing power, cooling, water, and grid interconnect limits as constraints on AI compute.
The clearest fit signal for a campaign about time-series and industrial AI is the partnership announcement delivered onstage to more than 2,000 attendees at the Ignition Community Conference, alongside a follow-up noting that open-source TimescaleDB users met across data centres, energy, oil and gas, and manufacturing.
These posts indicate the content’s target, but provide no engagement, reach, or follower-demographic data.
Before committing to a technical partnership or content collaboration, ask for recent results on comparable industrial, manufacturing, or time-series material: impressions, engagement rate, and follower change.
The posts report no performance, and older or higher-viewed items do not show that a particular topic or format drove results.
Also ask for any audience survey or platform demographics they can share.
LinkedIn follower data establishes reach, not industry, job function, or seniority.
Demographics from another platform can inform geography, age, and gender, but not employer, occupation, or seniority; ask whether survey results cover those professional attributes.
LinkedIn followers
8,028
LinkedIn profile for Michael Freedman, as recorded in the account data · 2026-10-23
Michael Freedman has no published sponsorship policy, rate card, or open partnership application.
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The collaborations mentioned are product and platform collaborations, not paid placements: Tiger Data's work with Inductive Automation was announced jointly, and Freedman appeared onstage with Inductive Automation's CEO to launch a first-party product built on TimescaleDB Enterprise.
A brand approaching them should frame the ask as a technical partnership or content collaboration tied to industrial data infrastructure, since every collaboration mentioned centers on an integration or shared technical story rather than a sponsorship slot.
No official inquiry route is named, so ask for the appropriate contact directly.
Platform and product integration partnership
Buyer requirement
The partner needs a product that Tiger Data's time-series database can genuinely integrate with or power.
The disclosed example is Inductive Automation's first-party Ignition Enterprise Historian, which they describe as powered by TimescaleDB Enterprise.
A proposal should show a working integration and a joint technical story for industrial operations; a straight sponsorship placement does not match the documented pattern.
Joint conference keynote or technical session
Buyer requirement
The disclosed example is a co-announcement onstage during the Ignition Community Conference keynote, before more than 2,000 attendees, alongside Inductive Automation's CEO.
The practical requirements are an existing event with an audience of controls engineers and system integrators, a launch-worthy announcement, and a partner willing to share the bill.
Freedman also gives standalone talks on the architecture behind industrial control plus Postgres, making a technical session without a product launch a plausible variant.
Deployment story and technical content
Buyer requirement
Freedman's published material highlights real deployments with operating numbers, such as Titan America's SCADA sensor volumes, continuous aggregates for dashboards and reporting, and 19 TB of time-series data across hot and tiered storage. A collaborator should bring measurable results from a live industrial, energy, manufacturing, or data center deployment and be willing to discuss the architecture openly, rather than supplying marketing copy alone.
A data infrastructure company wants to assess Michael Freedman for a technical partnership or content collaboration on time-series data for industrial AI systems.
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Decision: strong fit.
They work at Tiger Data (creators of TimescaleDB) and write and speak about exactly this problem.
In their posts, they describe speaking at the Ignition Community Conference about pairing industrial control with Postgres to scale to the volume and history of the physical world, combining real-time data with the relational context that gives it meaning.
They argue that power, cooling, water, grid interconnects and the ability to run dense compute reliably are becoming the constraints on AI data centers, citing hyperscalers turning down server expansion requests due to regional power capacity and the proposed $67B NextEra–Dominion deal.
They point to a concrete industrial deployment: Titan America uses Tiger Data and TimescaleDB as the data foundation for manufacturing operations, handling millions of SCADA sensor readings per day, continuous aggregates for real-time dashboards and reporting, and 19 TB of time-series data across hot and tiered storage.
They have already co-announced a product with a partner: onstage with Inductive Automation CEO Colby Clegg at the Ignition Community Conference, announcing the first-party Ignition Enterprise Historian powered by TimescaleDB Enterprise in front of 2,000+ attendees.
They also report quarterly revenue roughly double expected, driven by industrial, manufacturing, energy, robotics, and data centers, framing it as an AI-driven re-industrialization wave that needs modern data infrastructure.
Their disclosed partnership pattern is integration and joint technical story, not a one-off sponsorship post.
A data infrastructure company proposing a technical collaboration should expect to discuss a real integration or a shared customer story, and can reasonably propose a joint launch or conference moment.
The main gap is audience data: only the LinkedIn follower count (8,028) is available, with no engagement or demographic breakdown.
Ask for performance data on comparable industrial or time-series posts before committing.
When to choose a specialist
A consumer-facing AI writing assistant wants a paid LinkedIn sponsorship post with a personal testimonial, a discount code, and a general 'AI productivity' message aimed at a broad business audience.
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Decision: poor fit.
Michael Freedman's posts do not describe paid creator formats, a sponsorship policy, rate card, or open partnership application.
Every disclosed collaboration is a product and platform partnership anchored in an integration or a shared technical story.
Their content is narrow and technical: power as a data center constraint, industrial control plus Postgres, SCADA data at scale, and a re-industrialization thesis driven by machines, sensors and operational data.
A general productivity message with a discount code does not connect to that work.
Their posts offer no basis to assume the audience would respond to a consumer software offer or to confirm a broad business audience; the only audience figure is the LinkedIn follower count (8,028), with no demographics.
If the company wants to work with them, a better route would be a technical collaboration tied to industrial, energy, manufacturing or data center data rather than a paid endorsement.
If it proceeds with the sponsorship, ask directly whether they accept placements and request audience data; partner hashtags alone do not establish a paid offer.
A worked campaign example
Worked campaign angle for a technical collaboration on operational data for AI-era industrial sites.
Campaign idea: a joint technical series on operational data for AI-era industrial sites, anchored by a conference session and a customer proof point.
Deliverables: (1) a co-presented session at an industrial automation or data infrastructure event, following the pattern Michael Freedman used at the Ignition Community Conference, where industrial control was paired with Postgres and the architecture explained; (2) a long-form technical article or post arguing that power, cooling, water, grid interconnects and equipment state are now part of the system architecture, not just dashboard metrics, using the power-as-constraint argument and concrete examples such as regional power capacity limits blocking server expansion; (3) a case study on a named industrial customer, in the shape of the Titan America example, with specific volumes, storage tiers, and dashboard/reporting use cases.
Frame the collaboration as a product integration or a shared technical story, the partnership pattern they have publicly demonstrated, including the onstage announcement with Inductive Automation's CEO for a first-party historian powered by TimescaleDB Enterprise.
The partner should bring a working integration or a reference customer with measurable results.
Before launch, request recent engagement and reach data on industrial or time-series posts; the only audience metric available is the LinkedIn follower count, 8,028, with no demographic or engagement breakdown.
Do not expect a paid sponsorship slot; expect a technical partnership negotiation.
Success would look like a co-branded session, a published customer story, and a product integration mention, with the partner's data infrastructure shown as the layer where operational history and relational context meet.
Choose Simon Allen when the collaboration is about market-level positioning and convening rather than product integration.
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Allen's posts show them working the UK digital infrastructure agenda directly: representing the Quantum Infrastructure Council at Platform UK, noting that every discussion stayed grounded in UK realities such as planning reform, grid constraints, regional growth, investment appetite, supply chain delivery and skills, and moderating a Capacity Europe panel titled 'Scaling Under Scarcity: Delivering Projects Amid Shortages' covering power equipment, chips, cooling, supply chains and skilled resources.
A data infrastructure company seeking a co-hosted panel, a market-strategy article or an introduction to UK data centre and energy audiences would get a more direct route here than through a technical partnership post.
Allen also frames quantum readiness as an infrastructure question tied to power, cooling, site design and long-term strategy, which suits a buyer focused on planning and delivery constraints rather than the data layer itself.
The tradeoff is depth for a joint technical launch or reference-customer campaign.
Allen's posts focus on events and market commentary: conference attendance, panel moderation and industry-wide constraints.
They show strategic reach and credible industry framing, but not named customer deployments, time-series or historian architecture, or a co-announced product.
For a joint technical launch or a reference customer with measurable operational volumes, ask for evidence of an integration or shared-customer story; without it, this is a weaker fit than the one Freedman's posts demonstrate.
Choose Trent Warren when a campaign needs supplier-side market intelligence rather than deployment experience.
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The posts use proprietary data to answer quantitative questions about AI infrastructure supply and demand: why average memory per hyperscaler server exceeds roughly 600 GB in regions like Colombia while the U.S. stays below 400 GB, and why average AWS spot prices rose about 14.3% over three months as generative AI workloads absorbed spare compute and reserved blocks shrank the spot pool.
A company whose campaign angle is 'what hyperscaler capacity and pricing data tells buyers about AI demand' would get a quantitative, data-led voice here.
Both posts close by inviting people to talk about seeing more global data across cloud service providers, AI and semiconductors, pointing to a data-subscription or market-intelligence relationship.
The tradeoff is the layer of the stack.
Warren's reviewed work analyses hyperscaler hardware economics and cloud pricing from an intelligence provider's vantage point, not industrial control, SCADA data, time-series storage or operational data architecture.
The posts imply an intelligence-access relationship, not a co-built product.
For a joint integration, industrial customer case study or content about running machines and sensors on a data platform, ask for evidence of overlap with time-series data for industrial AI.
Choose Joe Yan when the collaboration is an investment or market thesis about physical AI and the industrial supply chain, not a data-platform integration.
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The posts map how AI demand propagates into physical suppliers: Generac, founded in 1959, now building large-scale data centre power systems with an approximately $1.6 billion data-centre backlog, and Modine HVAC, founded in 1916, supplying mission-critical cooling through its Airedale business under a long-term agreement worth more than $4 billion between 2027 and 2029, with $165 million paid upfront to fund capacity.
Yan also lays out a China-U.S.-Japan thesis on embodied AI, arguing the next phase of AI investing is less about separating software from hardware and more about understanding how software demand moves through the physical supply chain.
A buyer whose message is 'the beneficiaries of AI may not look like AI companies' would get a clear, portfolio-level framing here, plus a comparative read on where physical AI is funded and deployed.
The tradeoff is altitude and specificity.
Yan's posts focus on investment theses, market structure and company positioning, naming large industrials and their financial commitments rather than describing how operational data is collected, stored or queried.
A partner would get strategic framing and possible investor or deep-tech audience reach, but not the hands-on technical story or pattern of co-announcing a product with an industrial automation vendor.
For a data-platform integration campaign, ask for a named customer deployment or time-series architecture at the data layer.
Five complete LinkedIn posts from Michael Freedman present an argument that power is becoming the constraint on AI data centers, two posts on the Ignition Community Conference and the Inductive Automation partnership, a Titan America customer example, and a quarterly business update.
The only audience figure stated is the LinkedIn account's follower count, 8,028, observed 23 October 2026.
The posts do not provide publication dates for the individual posts, conference talk transcripts or recordings, engagement and audience demographics, third-party coverage, or a documented sponsorship or inquiry policy.
Similar-creator comparisons use post text from three accounts dated February 2026 through September 2026; the posts may not be complete, so they support similarity and difference only.
The posts do not say whether linked case studies or product pages were reviewed.
