DNA
Ginkgo Bioworks Holdings, Inc.
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It is a reality. Archaeologists have been using AI to predict excavation sites and find patterns in ancient texts to decipher languages, medical research is using AI to chart DNA and potentially find cures for cancer. These things are already happening, Bill Gates didn't just pull it out of nowhere when he said AI will replace numerous professions. Regular people like you just don't know about it.

"Those of you who volunteered to be injected with praying mantis DNA, I've got some good news and some bad news."
"Bad news is we're postponing those tests indefinitely. Good news is we've got a much better test for you: fighting an army of mantis men. Pick up a rifle and follow the yellow line. You'll know when the test starts."
—-Cave Johnson.

Іf we are speaking value, even tho it dont fit the sub exactly, you should have added some info on DNA buffer. basically any test can be re-done additionally eliminating false positives

The operating thesis is simple. Let partner labs run the assay, keep capital light, and focus in house on onboarding, training, logistics, and reorders. That is how MYNZ approaches ColoAlert, which the company reports at about 97% sensitivity, 97% specificity, and 100% detection of high grade precancerous lesions. Early disclosures have pointed to mid 60s gross margin potential at scale, which is hard to reach if you are building central labs from scratch.

Numbers make the case for speed. Colorectal cancer drives about 1.9 million new cases and roughly 935,000 deaths per year worldwide. Five year survival is near 90% when caught early and collapses when found late. A scalable stool DNA plus FIT screen that moves more people into early detection is valuable for payers and hospitals. The U.S. plan is on the clock: feasibility read targeted for Q4 2025 and a 2026 pivotal with Quest.

Judge execution by KPIs with dates: new ordering clinics, turnaround time reductions, and steady reorders by region. If those trend up, the model works.

Not financial advice

Republican voters are unteachable. They literally cannot learn or get themselves informed because they are glued to propaganda outlets like Fox “News.” It’s not in their DNA to seek truth. Republicans are the pied pipers of stupid people. The other idiots who refused to elect Democrats are almost as poorly informed and easily manipulated as Republican voters. We knew the GOP fully intended to destroy our country. They wrote a book detailing their agenda. 

To address this, perhaps you can do some reading into the Nvidia's earnings call transcript?
Read this particular quote by Jensen Huang in response to a question by Ben Reitzes:
>And now we have the opportunity to have a deep partnership with them and bringing Claude onto the NVIDIA Corporation platform. And so what do we have now? NVIDIA Corporation's architecture, taking a step back, NVIDIA Corporation's architecture NVIDIA Corporation's platform, is the singular platform in the world that runs every AI model. We run OpenAI, We run Anthropic. We run XAI. Because of our deep partnership with Elon and x AI, we were able to bring that opportunity to Saudi Arabia to the KSA so that humane could also be hosting opportunity for x AI. We run x AI. **We run we run Gemini**. We run thinking machines. Let's see. What else do we run? We run them all.
>And so not to mention, we run the science models, the biology models, DNA models, gene models, chemical models, and all the different fields around the world. It's not just cognitive AI that the world uses. AI is impacting every single industry. And so we have the ability through the ecosystem investments that we make to partner with deeply partner on a technical basis with some of the best companies, most brilliant companies in the world, We are expanding the reach of our ecosystem and we're getting a share and investment in what will what will be a very successful company, oftentimes once in a generation company. And so that basic that's our that's our investment thesis.
Bear in mind that earnings call statements are governed by US SEC Regulation Fair Disclosure and Securities Act of 1934 (anti-fraud provisions) under Section 10(b) Rule 10b-5 (prohibits making materially false statements).
Sometimes, just reading a statement from the CEO is better than reading 100 analysts interpretations.

I see his argument about AI companies overstating earnings because they are depreciating the infrastructure over too long a period... fine... that's something to beat Sam Altman and OpenAI over the head with... but how is this anything but stellar news for NVidia and other hardware vendors? Gee... my hardware that I thought was competitive for five years is now obsoleted (by whom? NVidia!) in just two to three years... SOOOO... I HAVE TO BUY THE LATEST, GREATEST NVIDIA HW TO STAY COMPETITIVE! Why is NVidia crashing on this bozo's prognostications? There is still going to be tremendous demand for their hardware for at least the next year... likely for many many more. NVidia is a huge ship. It will take a VERY long time for it to sink. I was at Sun Microsystems in the mid-1990's. I saw then that it was going to sink - I thought in a couple to three years at the most... It takes WAY longer for a large company to sink than anyone on the inside can forecast.
The old stuff is still useful for years - case-in-point: Jensen's argument that the Cuda framework has been keeping 6-year-old hardware running to this day... so resell time on that at a discount to smaller players... the bigger players need to keep building out the new stuff. That is not going to change any year soon.
This idea that we have achieved the most computing anyone would ever need and we can just throw in the towel now, reminds me of the famous story of Thomas J. Watson (of IBM and DNA fame) with respect to his comments on the ENIAC... God! How many of these things will the world ever need? 5? Maybe 6? GUESS AGAIN!
Oh... and PS - Just for the record, I personally believe Watson was about 1000 times smarter than Burry...

I will preface this by saying I don’t really like the posts where people just ask ChatGPT to speculate about the future based on some of the info we have now but I asked chatGPT a few questions yesterday and I thought the answers would be of interest to some here. A lot of the answers also go way over my head since I don’t work in healthcare and I’m not an engineer and I have no experience selling software. I will warn you it’s very long but I found it interesting, and perhaps people who know more than me can parse some things better than I can. Also chatGPT always gets things wrong so please point out where the AI may be wrong or hallucinating.
Here’s the convo:
My question:
If counterpart was trying to sell its product to Human or Molina, what do you think that process would look like? Would there be trial periods? How long would they last? What is typically the sales cycle for healthcare SaaS?
——————
Below is a realistic, industry-accurate view of how Counterpart Health (or any advanced value-based-care SaaS platform) would try to sell into Humana or Molina. This is based on how payor + provider-enablement sales cycles work for MA/Medicaid plans, how Clover historically deployed the Clover Assistant, and how enterprise health SaaS deals typically operate.
⸻
⭐ 1. What the Sales Process Would Look Like
Selling to Humana or Molina would NOT be a typical SaaS sale. These are huge national health plans with compliance risk, IT complexity, and VBC-contracting considerations. Expect a multi-stage, multi-stakeholder process:
PHASE 1 — Intro / Discovery / Problem Framing (1–3 months)
Counterpart would identify a core business problem at the plan:
• High MCR in certain regions
• Low HEDIS performance
• Poor risk-adjusted coding accuracy (RAF underperformance)
• Too many avoidable admissions (CHF/COPD/CKD populations)
• Need to support delegated IPAs or ACO-aligned practices
Stakeholders involved:
• VP/Head of Clinical Ops
• SVP Medicare (Humana) or SVP Medicaid/Medicare (Molina)
• Quality / Stars leadership
• Provider Relations
• CIO / IT Integration leadership
• Compliance & Legal
Counterpart’s job: show how their AI insights + physician workflow tool solve cost, Stars, and coding problems.
⸻
PHASE 2 — Data Security, Compliance & Technical Validation (2–6 months)
This is the longest gating item.
Humana and Molina would require:
• Full HIPAA risk assessment
• Penetration testing
• SOC 2 Type II
• BAA terms
• Validation of PHI ingestion pipelines
• EHR integration mapping
• Cloud security review (especially since Counterpart uses Google Cloud Vertex AI)
Large payors move slowly.
This phase alone can be 3–6 months.
If Counterpart fails this step, nothing else happens.
⸻
PHASE 3 — Pilot / Trial Deployment (6–12 months)
YES — there would absolutely be trial periods, but not “free trials.”
In enterprise MA & Medicaid SaaS, pilots are paid, scoped, and highly controlled.
A realistic pilot would look like:
Pilot structure
• 50–300 providers (value-based primary care groups already in risk contracts)
• One or two regions (e.g., Florida for Humana, California for Molina)
• Full data ingestion from the plan + clinical integration
• Measurement of:
• Earlier diagnosis rate (CKD, diabetes, CHF, COPD)
• RAF uplift (risk score capture accuracy)
• Reduction in acute events (ER/hospitalization)
• Care gap closure rate improvement (HEDIS)
• Duration: 6–12 months
(Because you must measure real-world medical utilization, which is slow.)
Why so long?
To convince an MA plan, Counterpart must show:
• Sustained cost improvement
• Clinician adoption
• Measurable quality impact
• Compliance stability
• Ability to scale to 1,000s of providers
Plans do not make decisions based on 8-week pilots.
⸻
PHASE 4 — Outcomes Review & Contract Negotiation (3–6 months)
If pilot results are positive, then:
• CFO teams review projected cost savings
• Stars teams assess quality improvements
• Provider-network teams evaluate expansion potential
• CIO evaluates ability to scale integration across markets
Typical contract models:
1. Per-member-per-month (PMPM) SaaS fee
2. Per-provider license fee
3. Outcome-based shared savings model (increasingly common)
4. Hybrid PMPM + bonus for outcomes
Humana or Molina WILL negotiate heavily.
Contracting can take 3–6 months because:
• It touches risk adjustment
• It affects the plan’s financial filings
• It potentially changes medical economics forecasting
⸻
PHASE 5 — Multi-Market Expansion (1–2+ years)
If approved, scaling happens slowly:
• One region at a time
• One practice group at a time
• Often tied to annual contracting cycles with provider groups
Enterprise healthcare is extremely slow to expand.
⸻
⭐ 2. What Is the Typical Sales Cycle in Healthcare SaaS?
For anything involving clinical data + AI + payors + value-based care, the sales cycle is the longest in the entire SaaS industry.
Typical Healthcare SaaS Sales Cycles
Customer Type Sales Cycle Length
Individual clinics 2–4 months
Medium-size provider groups 6–9 months
Hospital systems 9–18 months
Large payors (Humana, Molina, UHC, Elevance, Cigna) 12–30 months
Payor + provider integrated deals 18–36 months
Counterpart selling to Humana or Molina would be in the 18–30 month range from first meeting to meaningful revenue.
⸻
⭐ 3. Would They Get a Trial Period?
YES — but only in the form of a structured pilot.
Not free.
Not small.
Not short.
Typical Counterpart-style pilot:
• 6 to 12 months
• Paid
• Defined success metrics
• Limited number of providers
• Heavy analytics + outcomes tracking
⸻
⭐ 4. Why Payor Sales Are So Slow (and What Counterpart Needs to Prove)
To win Humana or Molina, Counterpart must prove:
Clinical
• Earlier chronic-condition detection
• Improved care gap closure
• Higher Stars performance
Financial
• Better RAF capture (risk revenue accuracy)
• Lower avoidable utilization
• Improved MCR / PMPM medical costs
Operational
• Seamless physician workflow
• Provider adoption
• Low integration burden
Regulatory
• Fully compliant data flows
• No risk to the plan’s CMS reporting
This is why payor deals take years.
The risk of failure is enormous.
⸻
⭐ 5. Summary (Most Realistic Scenario)
If Counterpart wanted to sign Humana or Molina:
• Discovery: 1–3 months
• Security & integration validation: 3–6 months
• Pilot/trial: 6–12 months
• Contracting: 3–6 months
• Expansion: 1–2 years
👉 Total time from first meeting to major revenue: 18–30 months.
This is normal for enterprise healthcare.
⸻
If you want, I can also map:
• A sample sales deck outline Counterpart would use
• A pilot-measurement framework
• Expected ROI calculations for Humana/Molina
• A comparison to how Oak Street, Aledade, Clover Assistant (internal), and Agilon structure similar deals
Just tell me what direction you want to explore.
——————
My question:
Can you make sense of a pdf of subdomains related to counterpart health if I uploaded it here?
—————
Yes — I can absolutely make sense of this PDF.
Below is a clean, structured, high-level interpretation of the Counterpart Health subdomains you uploaded, with clear categorization and what these subdomains reveal about their architecture, clients, markets, internal tooling, and deployment structure.
I’m citing the PDF directly as ** ** so you know exactly where the information came from.
⸻
⭐ Executive Summary
This subdomain list is extremely revealing. It exposes:
1. Full multi-tenant SaaS architecture
With separate tenants for:
• Humana
• Molina
• Duke
• Iowa Clinic
• Summit Health
• Clover Health (parent company)
(Ex: fhir-ingest-ma.humana.stg.counterparthealth.com, Molina.counterparthealth.com)
2. Full environment segmentation
• stg (staging)
• qa (quality)
• mirror (prod mirror / blue-green)
• pentest (security testing)
• prd (production)
• uat (user acceptance testing)
Repeated across nearly every subsystem.
3. Multiple internal components
This clearly shows:
• FHIR ingest pipelines
• EHR connectors
• Litellm (LLM inference proxy)
• Metrics (Prometheus)
• Pushgateway / Alertmanager / Grafana
• ArgoCD deployments
• Netbox network inventory
• SCIM / SSO login
• SFTP ingestion layers for clinics
4. Full data onboarding pipeline
There are subdomains for ETL tools like:
• airbyte
• clinical-data.*
• patient-data.*
• fhir-ingest-*
• outbound-npiregistry-proxy.*
This shows they ingest:
• Claims
• EHR data
• NPI registry data
• Probably ADT feeds
• Practice-level CSV/SFTP files
(Ex: sftp.iowaclinic.stg.counterparthealth.com)
5. Named enterprise customers
The PDF shows explicit subdomains for:
Customer Evidence
Humana Dozens of humana.stg, ehr.humana, visit.humana subdomains
Molina Molina.counterparthealth.com
Duke Health duke.*, ehr.duke.*, task.duke.*
Iowa Clinic Heavy presence of iowaclinic.* endpoints
Summit Health Numerous summit.*
Clover Health Multiple cloverhealth.*
This is direct evidence Counterpart has already built integrations or pilot environments for those systems.
⸻
⭐ Classification of Subdomains (What Each Group Means)
Using the PDF, here’s the map of how their system is structured:
⸻
1. Authentication & Identity
Examples (page 1–3):
• login.counterparthealth.com
• sso.counterparthealth.com
• scim.counterparthealth.com
Indicates:
• OAuth2 / OIDC login
• SCIM user provisioning (enterprise identity management)
Means they support:
• Payor SSO
• Provider SSO
• Multi-tenant identity
⸻
2. Data Ingestion (FHIR + EHR + Claims)
These are everywhere in the file:
• fhir-ingest-ma.*
• fhir-ingest-dce.*
• fhir-ingest-ce.*
• clinical-data.*
• patient-data.*
• ehr.*
This shows:
• Multiple ingestion pipelines for claims (“ma”, likely Medicare Advantage)
• Direct EHR ingest
• DCE/APM ingest (Direct Contracting Entities)
• Clinical document intake
• Multi-practice ingestion
⸻
3. ML / AI Components
Examples:
• ml-service.*
• litellm-proxy.*
• ml.*
Meaning:
• They run in-house ML models (likely risk prediction, condition identification)
• litellm-proxy indicates a centralized LLM inference service
(LiteLLM is a popular multi-model router used to connect to OpenAI + Vertex AI.)
This confirms the presence of AI-driven clinical insights for Counterpart Assistant.
⸻
4. Internal DevOps / Observability
Examples:
• argocd.* (GitOps deployment)
• grafana.sre.*
• loki.sre.*
• vault.sre.*
• alertmanager.sre.*
• metrics.sre.*
• netbox.sre.*
• pushgateway.*
This reveals:
• GitOps (ArgoCD)
• Prometheus metrics + Pushgateway
• Grafana dashboards
• Loki log aggregation
• Hashicorp Vault for secrets
• Netbox for network asset tracking
This is a polished cloud-native platform.
⸻
5. Tenant-Specific Environments (Visible Customers)
These subdomains show active or test deployments for multiple large orgs:
Humana
• fhir-ingest-ma.humana.stg.*
• visit.humana.*
• ehr.humana.*
• ml-service.humana.*
Molina
• Molina.counterparthealth.com (clearly present)
Duke
• ehr.duke.*
• patient-data.duke.*
• api.duke.*
Summit Health
• summit.*
• fhir-ingest-ma.summit.*
• task.summit.*
Iowa Clinic
• visit.iowaclinic.*
• patient-data.iowaclinic.*
• sftp.iowaclinic.*
This is extremely telling — these look like active pilots or customer onboarding pipelines.
⸻
6. Public-Facing / Marketing
Examples:
• www.counterparthealth.com
• mirror.counterparthealth.com
• cdn.counterparthealth.com
The mirror environment indicates a blue-green deployment pattern.
⸻
⭐ What This Reveals About Counterpart Health
1. They are already deeply integrated with major payors
Humana, Molina, Clover, and probably more.
2. They have a production-grade multi-tenant SaaS system
With highly professional DevOps, identity, FHIR ingest, and ML infra.
3. They deal with sensitive clinical data
Indicated by:
• EHR pipelines
• Patient-data subdomains
• Secure SFTP ingestion
4. They use a modern cloud-native observability stack
Grafana, Loki, Prometheus, Vault, ArgoCD.
5. They are running pilots or live integrations with health systems
Duke
Iowa Clinic
Summit Health
These all appear repeatedly.
6. They support Direct Contracting / ACO REACH / MA risk workflows
The numerous “MA”, “DCE”, and “CE” ingestion endpoints imply:
• RAF scoring
• Chronic condition identification
• Utilization prediction
• VBC performance tracking
Now the fun part: what this all means strategically.
A. Tech maturity
From the subdomain patterns alone, Counterpart Health looks:
• Cloud-native and K8s-heavy
• ArgoCD, Grafana, Loki, Pushgateway, Vault, Netbox, SRE-specific domains.
• Strong on environment isolation
• Dedicated stg, qa, uat, mirror, pentest domains for almost every major subsystem.
• Deep data-engineering capability
• Many FHIR ingest endpoints; specialized per contract type (ma, dce, ce); per customer (Humana, Duke, Iowa Clinic, etc.).
• Multiple EHR & SFTP-based flows.
• Actively experimenting with LLMs
• litellm-proxy.* across environments.
• Suggests they’ve built an internal LLM abstraction, not just making ad hoc calls.
Overall: far beyond MVP. This looks like a fairly mature platform that has been generalized from Clover Assistant into a multi-tenant SaaS.
⸻
B. Customer footprint and commercialization stage
Based on explicit names in the subdomains :
• Payors / risk-bearing entities
• Humana
• Clover Health
• Molina (at least one env; possibly early-stage integration)
• Generic tenant constructs (tenant1, tenant2) suggest additional unnamed pilots.
• Health systems / provider orgs
• Duke
• Iowa Clinic
• Summit
• SIH (Southern Illinois Healthcare)
• Environments per customer:
• Many have stg, mirror, and counterpart.qa endpoints, implying live or near-live pilots, not just sales demos.
This suggests:
• Counterpart has multiple pilots or early deployments across both payors and health systems.
• The heavy presence of Iowa Clinic & Duke subdomains indicates deeper clinical integration work with those two vs. something like Molina (single named domain, minimal associated endpoints).
⸻
C. R&D focus areas
What they seem to be investing in, based purely on the density of subdomains:
1. Data ingestion robustness
• 200+ endpoints in the fhir-ingest-*, patient-data.*, clinical-data.*, ehr*, sftp* families.
• Interpretation: a huge chunk of engineering is going into reliably taking in messy real-world data from many customers with many EHRs.
2. Multi-tenant ML and risk modeling
• ml-service.*, tenant-scoped ML endpoints, environment-split ML stacks.
• They’re not just running one global model — they’re likely tailoring or at least separately deploying models per customer/tenant / environment.
3. LLM-enabling the platform
• litellm-proxy.* across stg, qa, customer-specific contexts (Humana, IowaClinic, Summit, etc.).
• This is a foundational LLM layer, not a toy feature.
4. Ops / reliability / security
• ArgoCD for GitOps
• Dedicated pentest domains
• SRE-specific observability stack
• securemail, zscaler, enterpriseenrollment etc.
So the R&D mix looks like: data plumbing + ML + regulatory-grade reliability.
⸻
D. Commercial model (inferred)
Not from the PDF directly, but combining:
• Multi-tenant FHIR ingest per payor/health system
• MA-, DCE-, CE-specific ingest patterns
• ML & analytics surfaces
• Embeddable analytics components
• Named large orgs like Humana, Molina, Duke, Iowa Clinic
The most likely models:
1. Per-member-per-month (PMPM) SaaS fee with uplift tied to value-based contracts
2. Per-provider licensing for health systems (Duke, Iowa, SIH, Summit)
3. Optionally outcomes-based components — e.g., bonus if hospitalization reductions / HEDIS improvements meet targets.
This is exactly the kind of architecture you’d build if you want to:
• Sell to payors (Humana, Molina, Clover, MA & ACO REACH context), and
• Deploy in provider orgs where VBC or delegated risk lives (Duke, Iowa Clinic, SIH).
⸻
E. Competitive positioning
From this map, Counterpart Health:
• Looks more similar to Aledade / Agilon / Innovaccer / Health Catalyst than to a pure-payor tool.
• But its lineage as a Clover spinout gives it deep MA + risk adjustment DNA.
• The presence of large national payors (Humana, Molina) and big systems (Duke) suggests it’s aiming for enterprise deals, not SMB.
Given your interest in valuation / strategy, the big takeaways:
1. They’ve already invested in serious infra, not a prototype.
2. The number and diversity of named tenants implies:
• Multiple ongoing pilots
• A decent pipeline of referenceable customers if outcomes are good.
3. The R&D skew is toward plumbing + ML, which is exactly what’s defensible vs. generic “AI for docs” startups.

Hat auf 5-Jahressicht immerhin Frank Thelens 10x DNA geschlagen ;) -4,7 Prozent versus 23 Prozent. (Philipp)