diff --git a/_demos/programmatic_model_gen/DEMO.md b/_demos/programmatic_model_gen/DEMO.md
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--- a/_demos/programmatic_model_gen/DEMO.md
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@@ -1,37 +0,0 @@
-# Programmatic Model Generation Demo
-This code demonstrates how you can use command line/terminal
-and python to generate many models at one time.
-
-This demo was created using dbt Cloud CLI and Python 3.11.
-
-Python modules used:
-+ subprocess: to run a macro using dbt
-+ ast: to convert the results of the macro to a Python dictionary
-+ pathlib: to create the directory to store the generated files in if it doesn't exist.\
-
-Additionally, this was created for a specific use case to generate 
-models using the inc_stream package, but should be flexible enough
-to customize to different use cases.
-
-### How to use
-For visual learners, [here's a loom video](https://www.loom.com/share/daf635da1f164109bf41989b4c0e13b8?sid=bbc10104-d0c0-4189-aaa8-f4a247707cef)!
-
-1. **Add a macro to the project**
-   This macro works similar to code-gen and returns the code content
-   of all tables in a particular database/schema, with the option to 
-   match table patterns or exclude tables.
-
-   Use the file located at `macros/generate_inc_stream_model_sql.sql` within this folder.
-
-2. **Add a python script to the project**
-   The location here is absolutely optional as long as you change the script to 
-   work with the file paths you want. This demo puts it at the same level as dbt_project.yml.
-
-   This script will run the macro and get the results, then use
-   the results to create and write the files. 
-   
-   Use the file located at `scripts/create_dbt_inc_stream_model.py` within this folder.
- 
-3. **Sit back and watch the magic happen**
-   After ensuring you've covered customizing the "To Use" points in the script, 
-   run the Python script and watch it do all the work!
diff --git a/_demos/programmatic_model_gen/macros/generate_inc_stream_model_sql.sql b/_demos/programmatic_model_gen/macros/generate_inc_stream_model_sql.sql
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--- a/_demos/programmatic_model_gen/macros/generate_inc_stream_model_sql.sql
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-{% macro generate_inc_stream_model_sql(database_name, schema_name, table_pattern='%', exclude='') %}
-
-{% set table_list = dbt_utils.get_relations_by_pattern(
-    schema_pattern=schema_name,
-    database=database_name,
-    table_pattern=table_pattern,
-    exclude=exclude
-) %}
-
-{%- set objects = [] %}
-{%- for table in table_list %}
-    {%- set column_response = adapter.get_columns_in_relation(adapter.get_relation(
-        database=table.database,
-        schema=table.schema,
-        identifier=table.identifier
-    )) -%}
-    {%- set column_names = column_response | map(attribute='name') %}
-
-{%- set model_sql %}
-{% raw %}{{- config({%- endraw %}
-        materialized="incremental_stream",
-        unique_key=["id"]
-    )
-    select 
-        {%- for column in column_names %}
-        {{ column | lower }},
-        {%- endfor %}
-        {% raw %}{{ incr_stream.get_stream_metadata_columns() }}{%- endraw %}
-    from incr_stream.stream_source({{ table.schema | lower }}, {{ table.identifier | lower }})
-{% raw %}-}}{% endraw %}
-{%- endset %}
-    
-    {%- do objects.append({
-        "name": "stg_" ~ table.schema | lower ~ "__" ~ table.identifier | lower ~ ".sql",
-        "sql": model_sql
-    }) %}
-{%- endfor %}
-
-{% if execute %}{{ print(objects) }}{% endif %}
-
-{% endmacro %}
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diff --git a/_demos/programmatic_model_gen/scripts/create_dbt_inc_stream_models.py b/_demos/programmatic_model_gen/scripts/create_dbt_inc_stream_models.py
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--- a/_demos/programmatic_model_gen/scripts/create_dbt_inc_stream_models.py
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-# You could also use dbtRunner that comes with dbt Core, if not using dbt Cloud
-# CLI. This is written to operate using dbt Cloud CLI.
-
-# To use:
-# 1. This is for creating multiple dbt models using the inc_stream package 
-#    (https://hub.getdbt.com/arnoN7/incr_stream/latest/). Make sure this is
-#    included in your packages.yml and you have run dbt deps before you test
-#    the models generated.
-# 2. Make sure you have the generate_inc_stream_model_sql.sql macro in your project
-# 3. Make sure you are at the parent directory of your dbt project and this .py
-#    file is at the same level as your dbt_project.yml folder
-# 4. Change these parameters if desired:
-#    + file_path: The folder that you want the generated files to go in. This 
-#                 does not have to exist before you run the .py 
-#    + source_config: Make sure this is pointed to the databases and schema
-#                     that your sources are located in.
-import subprocess
-import ast
-import pathlib
-
-file_path = 'models/staging/_generated_models/'
-pathlib.Path(file_path).mkdir(parents=True, exist_ok=True)
-
-source_config = '''{
-    "database_name": "raw_tpch",
-    "schema_name": "tpch_sf1"
-}'''
-
-response = subprocess.run(
-    ['dbt', 'run-operation', 'generate_inc_stream_model_sql', '--args', source_config],
-    capture_output = True, # Python >= 3.7 only
-    text = True # Python >= 3.7 only
-)
-
-start = '[{'
-end = '}]'
-result_string = start + response.stdout.split(start)[1].split(end)[0] + end
-results = ast.literal_eval(result_string)
-
-for result in results:
-    model_path = file_path + result["name"]
-    model_content = result["sql"]
-
-    with open(model_path, "w") as model:
-        model.write(model_content)
-    
-    print('Generated model ' + model_path + '.')
-
-print('Done.')
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